The ABC of Network Sharing – The Fundamentals (Part I).

  • Up-to 50% of Sites in Mobile Networks captures no more than 10% of Mobile Service Revenues.
  • The “Ugly” (cost) Tail of Cellular Networks can only be remedied by either removing sites (and thus low- or –no-profitable service) or by aggressive site sharing.
  • With Network Sharing expect up-to 35% saving on Technology Opex as well as future Opex avoidance.
  • The resulting Technology Opex savings easily translates into a Corporate Opex saving of up-to 5% as well as future Opex avoidance.
  • Active as well as Passive Network Sharing brings substantial Capex avoidance and improved sourcing economics by improved scale.
  • National Roaming can be an alternative to Network Sharing in low traffic and less attractive areas. Capex attractive but a likely Ebitda-pressure point over time.
  • “Sharing by Towerco” can be an alternative to Real Network Sharing. It is an attractive mean to Capex avoidance but is not Ebitda-friendly. Long-term commitments combined with Ebitda-risks makes it a strategy that should to be considered very carefully.
  • Network Sharing frees up cash to be spend in other areas (e.g., customer acquisition).
  • Network Sharing structured correctly can result in faster network deployment –> substantial time to market gains.
  • Network Sharing provides substantially better network quality and capacity for a lot less cash (compared to standalone).
  • Instant cell split option easy to realize by Network Sharing –> cost-efficient provision of network capacity.
  • Network Sharing offers enhanced customer experience by improved coverage at less economics.
  • Network Sharing can bring spectral efficiency gains of 10% or higher.

The purpose of this story is to provide decision makers, analysts and general public with some simple rules that will allow them to understand Network Sharing and assess whether it is likely to be worthwhile to implement and of course successful in delivering the promise of higher financial and operational efficiency.

Today’s Technology supports almost any network sharing scenario that can be thought of (or not). Financially & not to forget Strategically this is far from so obvious.

Network Sharing is not only about Gains, its evil twin Loss is always present.

Network Sharing is a great pre-cursor to consolidation.

Network sharing has been the new and old black for many years. It is a fashion that that seems to stay and grow with and within the telecommunications industry. Not surprising as we shall see that one of the biggest financial efficiency levers are in the Technology Cost Structure. Technology wise there is no real stumbling blocks for even very aggressive network sharing maximizing the amount of system resources being shared, passive as well as active. The huge quantum-leap in availability of very high quality and affordable fiber optic connectivity in most mature markets, as well between many countries, have pushed the sharing boundaries into Core Network, Service Platforms and easily reaching into Billing & Policy Platforms with regulatory and law being the biggest blocking factor of Network-as-a-Service offerings. Below figure provides the anatomy of network sharing. It should of course be noted that also within each category several flavors of sharing is possible pending operator taste and regulatory possibilities.

anatomy of network sharing

Network Sharing comes in many different flavors. To only consider  one sharing model is foolish and likely will result in wrong benefit assessment. Setting a sharing deal up for failure down the road (if it ever gets started). It is particular important to understand that while active sharing provides the most comprehensive synergy potential, it tends to be a poor strategy in areas of high traffic potential. Passive sharing is a much more straightforward strategy in such areas. In rural areas, where traffic is less of an issue and profitability is a huge challenge, aggressive active sharing is much more interesting. One should even consider frequency sharing if permitted by regulatory authority. The way I tend to look at the Network Sharing Flavors are (as also depicted in the Figure below);

  1. Capacity Limited Areas (dense urban and urban) – Site Sharing or Passive Sharing most attractive and sustainable.
  2. Coverage Limited Areas (i.e., some urban environments, mainly sub-urban and rural) – Minimum Passive Sharing should be pursued with RAN (Active) Sharing providing an additional economical advantage.
  3. Rural Areas – National Roaming or Full RAN sharing including frequency sharing (if regulatory permissible).

networtksharingflavors

One of the first network sharing deals I got involved in was back in mid-2001 in The Netherlands. This was at the time of the Mobile Industry’s first real cash crises. Just as we were about to launch this new exiting mobile standard (i.e., UMTS) that would bring Internet to the pockets of the masses. After having spend billions & billions of dollars (i.e., way too much of course) on high-frequency 2100MHz UMTS spectrum, all justified by an incredible optimistic (i.e., said in hindsight!) belief in the mobile internet business case, the industry could not afford to deploy the networks required to make our wishful thinking come true.

T-Mobile (i.e., aka Ben BV) engaged with Orange (i.e., aka Dutchtone) in The Netherlands on what should have been a textbook example of the perfect network sharing arrangement. We made a great business case for a comprehensive network sharing. It made good financial and operational sense at the setup. At the time the sharing game was about Capex avoidance and trying to get the UMTS network rolled out as quickly as possible within very tight budgets imposed by our mother companies (i.e., Deutsche Telekom and France Telecom respectively). Two years down the road we revised our strategic thoughts on network sharing. We made another business case for why deploying on standalone made more sense than sharing. At that time the only thing T-we (Mobile NL) really could agree with Orange NL about was ancillary cabinet sharing and of course the underlying site sharing. Except for agreeing not to like the Joint Venture we created (i.e., RANN BV), all else were at odds, e.g., supplier strategy, degree of sharing, network vision, deployment pace, etc… Our respective deployment strategies had diverged so substantially from each other that sharing no longer was an option. Further, T-Mobile decided to rely on the ancillary cabinet we had in place for GSM –> so also no ancillary sharing. This was also at a time where cabinets and equipment took up a lot of space (i.e., do you still remember the first & 2nd generation 3G cabinets?). Many site locations simply could not sustain 2 GSM and 2 UMTS solutions. Our site demand went through the roof and pretty much killed the sharing case.

  • Starting point: Site Sharing, Shared Built, Active RAN and transport sharing.
  • Just before breakup I: Site Sharing, cabinet sharing if required, shared built where deployment plans overlapped.
  • Just before breakup II:Crisis over and almost out. Cash and Capex was no longer as critical as it was at startup.

It did not help that the Joint Venture RANN BV created to realize T-Mobile & Orange NL shared UMTS network plans frequently were at odds with both founding companies. Both entities still had their full engineering & planning departments including rollout departments (i.e., in effect we tried to coordinate across 3 rollout departments & 3 planning departments, 1 from T-Mobile, 1 from Orange and 1 from RANN BV … pretty silly! Right!). Eventually RANN BV was dissolved. The rest is history. Later T-Mobile NL acquired Orange NL and engaged in a very successful network consolidation (within time and money).

The economical benefits of Sharing and Network Consolidation are pretty similar and follows pretty much the same recipe.

Luckily (if Luck has anything to do with it?) since then there have been more successful sharing projects although the verdict is still out whether these constructs are long-lived or not and maybe also by what definition success is measured.

Judging from the more than 34 Thousand views on my various public network sharing presentations, I have delivered around the world since 2008, there certainly seem to be a strong and persistent interest in the topic.

  1. Fundamentals of Mobile Network Sharing.(2012).
  2. Ultra-Efficient Network Factory: Network Sharing & other means to leapfrog operator efficiencies. (2012).
  3. Economics of Network Sharing. (2008).
  4. Technology Cost Optimization Strategies. (2009).
  5. Analyzing Business Models for Network Sharing Success. (2009).

I have worked on Network Sharing and Cost Structure Engineering since the early days of 2001. Very initially focus was on UMTS deployments, the need and requirements to deploy much more cash efficient. Cash was a very scarce resource after the dot-com crash between 2000 & 2003. After 2004 the game changed to be an Opex Saving & Avoidance game to mitigate stagnating customer growth and revenue growth slow down.

I have in detail studied many Network Sharing strategies, concepts and deals. A few have turned out successful (at least still alive & kicking) and many more un-successful (never made it beyond talk and analysis). One of the most substantial Network Sharing deals (arguable closer to network consolidation), I work on several years ago is still very much alive and kicking. That particular setup has been heralded as successful and a poster-boy example of the best of Network Sharing (or consolidation). However, by 2014 there has hardly been any sites taken out of operation (certainly no where close to the numbers we assumed and based our synergy savings on).

More than 50% of all network related TCO comes from site-related operational and capital expenses.

Despite the great economical promises and operational efficiencies that can be gained by two mobilenetworksharingtco operations (fixed for that matter as well) agreeing to share their networks, it is important to note that

It is NOT enough to have a great network sharing plan. A very high degree of discipline and razor-sharp focus in project execution is crucial for delivering network sharing within money and time.

With introduction of UMTS & Mobile Broadband the mobile operator’s margin & cash have come under increasing pressure (not helped by voice revenue decline & saturated markets).

Technology addresses up-to 25% of a Mobile Operators Total Opex & more than 90% of the Capital Expenses.

Radio Access Networks accounts easily for more than 50% of all Network Opex and Capex.

For a reasonable efficient Telco Operation, Technology Cost is the most important lever to slow the business decline, improve financial results and return on investments.

P&L Optimization

Above Profit & Loss Figure serves as an illustration that Technology Cost (Opex & Capex) optimization and is pivotal to achieve a more efficient operation and a lot more certain that relying on new business (and revenue) additions

It is not by chance that RAN Sharing is such a hot topic. The Radio Access Network takes up more than half of Network Cost including Capex.

Of course there are many other general cost levers to consider that might be less complex than Network Sharing to implement. Another Black (or Dark Grey) is outsourcing of (key) operational functions to a 3rd party. Think here about some of the main ticks

  1. Site acquisition (SA) & landlord relations (LR) – Standard practice for SA, not recommended for landlord relations. Usually better done by operator self (at least while important during deployment)..
  2. Site Build – Standard practice with sub-contractors..
  3. Network operations & Maintenance – Cyclic between in-source and outsource pending business cycle.
  4. Field services – standard practice particular in network sharing scenarios.
  5. Power management – particular interesting for network sharing scenarios with heavy reliance of diesel generators and fuel logistics (also synergetic with field services).
  6. Operational Planning – particular for comprehensive managed network services. Network Sharing could outsource RAN & TX Planning.
  7. Site leases – Have a site management company deal with site leases with a target to get them down with x% (they usually take a share of the reduced amount). Care should be taken not to jeopardize network sharing possibilities. Will impact landlord relations.
  8. IT operations – Cyclic between in-source and outsource pending business cycle.
  9. IT Development – Cyclic between in-source and outsource pending business cycle.
  10. Tower Infrastructure – Typical Cash for infrastructure swap with log-term Opex commitments. Care must be taken to allow for Network Sharing and infrastructure termination.

In general many of the above (with exception of IT or at least in a different context than RAN Sharing) potential outsourcing options can be highly synergetic with Network Sharing and should always be considered when negotiating a deal.

Looking at the economics of managed services versus network sharing we find in general the following picture;

managedservicesvsnetwokrsharing

and remember that any managed services that is assumed to be applicable in the Network Sharing strategy  column will enable the upper end of the possible synergy potential estimated. Having a deeper look at the original T-Mobile UK and Hutchinson UK 3G RAN Sharing deal is very instructive as it provides a view on what can be achieved when combining both best practices of network sharing and shared managed services (i.e., this is the story for The ABC of Network Sharing – Part II).

Seriously consider Managed Services when it can be proven to provide at least 20% Opex synergies will be gained for apples to apples SLAs and KPIs (as compared to your insourced model).

Do your Homework! It is bad Karma to implement Managed Services on an in-efficient organizational function or area that has not been optimized prior to outsourcing.

Do your Homework (Part II)! Measure, Analyze and Understand your own relevant cost structure 100% before outsourcing!

It is not by chance that Deutsche Telekom AG (DTAG) has been leading the Telco Operational Efficiency movement and have some of the most successful network sharing operations around. Since 2004 DTAG have had several (very) deep dives programs into their cost structure and defining detailed initiatives across every single operation as well as on its Group level. This has led to one of the most efficient Telco operations around in Western Europe & the US and with lots to learn from when it comes to managing your cost structure when faced with stagnating revenue growth and increasing cost pressure.

In 2006, prior to another very big efficiency program was kicked off within DTAG, I was asked to take a very fundamental and extreme (but nevertheless realistic) look at all the European mobile operations technology cost structures and come back with how much Technology Opex could be pulled out of them (without hurting the business) within 3-4 years (or 2010).

Below (historical) Figure illustrates my findings from 2006 (disguised but nevertheless the real deal);

fullnetworkpotential

This analysis (7-8 years old by now) directly resulted in a lot of Network Sharing discussions across DTAGs operations in Europe. Ultimately this work led to a couple of successful Network Sharing engagements within the DTAG (i.e., T-Mobile) Western European footprint. It enabled some of the more in-efficient mobile operations to do a lot more than they could have done standalone and at least one today went from a number last to number 1. So YES … Network Sharing & Cost Structure Engineering can be used to leapfrog an in-efficient business and by that transforming an ugly duckling into what might be regarded as an approximation of a swan. (in this particular example I have in mind, I will refrain from calling it a beautiful swan … because it really isn’t … although the potential is certainly remain even more today).

The observant reader till see that the order of things (or cost structure engineering) matters. As already said above, the golden rule of outsourcing and managed services is to first ensure you have optimized what can be done internally and then consider outsourcing. We found that first outsourcing network operations or establish a managed service relationship prior to a network sharing relationship was sub-optimal and actually might be hindering reaching the most optimal network sharing outcome (i.e., full RAN sharing or active sharing with joint planning & operations).

REALITY CHECK!

Revenue Growth will eventually slow down and might even decline due to competitive climate, poor pricing management and regulatory pressures, A Truism for all markets … its just a matter of time. The Opex Growth is rarely in synch with the revenue slow down. This will result in margin or Ebitda pressure and eventually profitability decline.

Revenue will eventually stagnate and likely even enter decline. Cost is entropy-like and will keep increasing.

The technology refreshment cycles are not only getting shorter. These cycles imposes additional pressure on cash. Longer return on investment cycles results compared to the past. Paradoxical as the life-time of the Mobile Telecom Infrastructure is shorter than in the past. This vicious cycle requires the industry to leapfrog technology efficiency, driving demand for infrastructure sharing and business consolidation as well as new innovative business models (i.e., a topic for another Blog).

The time Telco’s have to return on new technology investments is getting increasingly shorter.

Cost saving measures are certain by nature. New Business & New (even Old) Revenue is by nature uncertain.

Back to NETWORK SHARING WITH A VENGENCE!

I have probably learned more from the network sharing deals that failed than the few ones that succeeded (in the sense of actually sharing something). I have work on sharing deals & concepts across across the world; in Western Europe, Central Eastern Europe, Asia and The USA under very different socio-economical conditions, financial expectations, strategic incentives, and very diverse business cycles.

It is fair to say that over the time I have been engaged in Network Sharing Strategies and Operational Realities, I have come to the conclusion that the best or most efficient sharing strategy depends very much on where an operator’s business cycle is and the network’s infrastructure age.

The benefits that potentially can be gained from sharing will depend very much on whether you

  • Greenfield: Initial phase of deployment with more than 80% of sites to be deployed.
  • Young: Steady state with more than 80% of your sites already deployed.
  • Mature: Just in front of major modernization of your infrastructure.

The below Figure describes the three main cycles of network sharing.

stages_of_network_sharing

It should be noted that I have omitted the timing benefit aspects from the Rollout Phase (i.e., Greenfield) in the Figure above. The omission is on purpose. I believe (based on experience) that there are more likelihood of delay in deployment than obvious faster time-to-market. This is inherent in getting everything agreed as need to be agreed in a Greenfield Network Sharing Scenario. If time-to-market matters more than initial cost efficiency, then network sharing might not a very effective remedy. Once launch have been achieved and market entry secured, network sharing is an extremely good remedy in securing better economics in less attractive areas (i.e., typical rural and outer sub-urban areas). There are some obvious and very interesting games that can be played out with your competitor particular in the Rollout Phase … not all of them of the Altruistic Nature (to be kind).

There can be a very good strategic arguments of not sharing economical attractive site locations depending on the particular business cycle and competitive climate of a given market. The value certain sites market potential could  justify to not give them up for sharing. Particular if competitor time-to-market in those highly attractive areas gets delayed. This said there is hardly any reason for not sharing rural sites where the Ugly (Cost) Tail of low or no profitable sites are situated. Being able to share such low-no-profitability sites simply allow operators to re-focus cash on areas where it really matters. Sharing allows services can be offered in rural and under-develop areas at the lowest cost possible. Particular in emerging markets rural areas, where a fairly large part of the population will be living, the cost of deploying and operating sites will be a lot more expensive than in urban areas. Combined with rural areas substantially lower population density it follows that sites will be a lot harder to make positively return on investment within their useful lifetime.

Total Cost of Ownership of rural sites are in many countries substantially higher than their urban equivalents. Low or No site profitability follows.

In general it can be shown that between 40% to 50% of mature operators sites generates less than 10% of the revenue and are substantially more expensive to deploy and operate than urban sites.

The ugly (cost) tail is a bit more “ugly” in mature western markets (i.e., 50+% of sites) than in emerging markets, as the customers in mature markets have higher coverage expectations in general.

ugly_tail

(Source: Western European market. Similar Ugly-tail curves observed in many emerging markets as well although the 10% breakpoint tend to be close to 40%).

It is always recommend to analyze the most obvious strategic games that can be played out. Not only from your own perspective. More importantly, you need to have a comprehensive understanding of your competitors (and sharing partners) games and their most efficient path (which is not always synergetic or matching your own). Cost Structure Engineering should not only consider our own cost structure but also those of your competitors and partners.

Sharing is something that is very fundamental to the human nature. Sharing is on the fundamental level the common use of a given resource, tangible as well as intangible.

Sounds pretty nice! However, Sharing is rarely altruistic in nature i.e., lets be honest … why would you help a competitor to get stronger financially and have him spend his savings for customer acquisition … unless of course you achieve similar or preferably better benefits. It is a given that all sharing stakeholders should stand to benefit from the act of sharing. The more asymmetric perceived or tangible sharing benefits are the less stable will a sharing relationship be (or become over time if the benefit distribution should change significantly).

Recipe for a successful sharing partnership is that the sharing partners both have a perception of a deal that offers reasonable symmetric benefits.

It should be noted that perception of symmetric benefits does not mean per see that every saving or avoidance dollar of benefit is exactly the same for both partners. One stakeholder might get access to more coverage or capacity faster than in standalone. The other stakeholder might be able to more driven by budgetary concerns and sharing allows more extensive deployment than otherwise would have been possible within allocated budgets.

Historical most network sharing deals have focused on RAN Sharing, comprising radio access network (RAN) site locations, related passive infrastructure (e.g., such as tower, cabinets, etc..) and various degrees of active sharing. Recent technology development such as software definable network (SDN), virtualization concepts (e.g., Network Function Virtualization, NFV) have made sharing of core network and value-add service platforms interesting as well (or at least more feasible). Another financially interesting industry trend is to spin-off an operators tower assets to 3rd party Tower Management Companies (TMC). The TMC pays upfront a cash equivalent of the value of the passive tower infrastructure to the Mobile Network Operator (MNO). The MNO then lease (i.e., Opex) back the tower assets from the TMC. Such tower asset deals provide the MNO with upfront cash and the TMC a long-term lease income from the MNO. In my opinion such Tower deals tend to be driven by MNOs short-term cash needs without much regard for longer  term profitability and Ebitda (i.e., Revenue minus Opex) developments.

With ever increasing demand for more and more bandwidth feeding our customers mobile internet consumption, fiber optical infrastructures have become a must have. Legacy copper-based fixed transport networks can no longer support such bandwidth demands. Over the next 10 years all Telco’s will face massive investments into fiber-optic networks to sustain the ever growing demand for bandwidth. Sharing such investments should be obvious and straightforward. In this area we also are faced with the choice of passive (Dark Fiber itself) as well as active (i.e., DWDM) infrastructure sharing.

NETWORK SHARING SUCCESS FACTORS

There are many consultants out there who evangelize network sharing as the only real cost reduction / saving measure left to the telecom industry. In Theory they are not wrong. The stories that will be told are almost too good to be true. Are you “desperate” for economical efficiency? You might then get very exited by the network sharing promise and forget that network sharing also has a cost side to it (i.e., usually forget and denial are fairly interchangeable here).

In my experience Network Sharing boils down to  the following 4 points:

  • Who to share with? (your equal, your better or your worse).
  • What to share? (sites, passives, active, frequencies, new sites, old sites, towers, rooftops, organization, ,…).
  • Where to share? (rural, sub-urban, urban, regional, all, etc..).
  • How to share? (“the legal stuff”).

In my more than 14 years of thinking about and working on Network Sharing I have come to the following heuristics of the pre-requisites a successful network sharing:

  • CEOs agree with & endorse Network Sharing.
  • Sharing Partners have similar perceived benefits (win-win feel).
  • Focus on creating a better network for less and with better time-to-market..
  • Both parties share a similar end-goal and have a similar strategic outlook.

While it seems obvious it is often forgotten that Network Sharing is a very-long term engagement (“for Life!”) and like in any other relationship (particular the JV kind) Do consider that a break-up can happen … so be prepared (i.e., “legal stuff”).

Compared to 14 – 15 years ago, Technology pretty much support Network Sharing in all its flavors and is no longer a real show-stopper for engaging with another operator to share network and ripe of (eventually) the financial benefits of such a relationship. References on the technical options for network sharing can be found in the 3GPP TR 3GPP TS 22.951 (“Service Aspects and Requirements for network sharing”) and 123.251 (“Network Sharing; Architecture and Functional Description”). Obviously, today 3GPP support for network sharing runs through most of the 3GPP technical requirements and specification documents.

Technology is not a show-stopper for Network Sharing. The Economics might be!

COST STRUCTURE CONSIDERATIONS.

Before committing man power to a network sharing deal, there are a couple of pretty basic “litmus tests” to be done to see whether the economic savings being promised make sense.

First understand your own cost structure (i.e., Capex, Opex, Cash and Revenues) and in particular where Network Sharing will make an impact – positive as well as negative. I am more often that not, surprised how few Executives and Senior Managers really understand their own company’s cost structure. Thus they are not able to quickly spot un-realistic financial & operational promises made.

Seek answers to the following questions:

  1. What is the Total Technology Opex (Network & IT) share out of the Total Corporate Opex?
  2. What is the Total Network Opex out of Total Technology Opex?
  3. What is the Total Radio Access Network (RAN) Opex out of the Total Network Opex?
  4. Out of the Total RAN Opex how much relates to sites including Operations & Maintenance?

expectation management

In general, I would expect the following answers to the above questions based on many of mobile operator cost structure analysis across many different markets (from mature to very emerging, from Western Europe, Central Eastern & Southern Europe, to US and Asia-Pacific).

  1. Technology Opex is 20% to 25% of Total Corporate Opex defined as “Revenue-minus-Ebitda”(depends a little on degree of leased lines & diesel generator dependence).
  2. Network Opex should be between  70% to 80% of the Technology Opex.,
  3. RAN related Opex should be between 50% to 80% of the Network Opex. Of course here it is important to understand that not all of this Opex might be impacted by Network Sharing or at least the impact would depend on the Network Sharing model chosen (e.g., active versus passive).

Lets assume that a given RAN network sharing scenario provides a 35% saving on Total RAN Opex, that would be 35% (RAN Saving) x 60% (RAN Opex) x 75% (Network Opex) x 25% (Technology Opex) which yields a total network sharing saving of 4% on the Corporate Opex.

A saving on Opex obviously should translate into a proportional saving on Ebitda (i.e., Earnings before interest tax depreciation & amortization). The margin saving is given as follows

\frac{{{E_2} - {E_1}}}{{{E_1}}} = \frac{{1 - {m_1}}}{{{m_1}}}x(with E1 and E2 represents Ebitda before and after the relative Opex saving x, m1 is the margin before the Opex saving, assuming that Revenue remains unchanged after Opex saving has been realized).

From the above we see that when the margin is exactly 50% (i.e., fairly un-usual phenomenon for most mature markets), a saving in Opex corresponds directly to an identical relative saving in Ebitda. When the margin is below 50% the relative impact on Ebitda is higher than the relative saving on Opex. If your margin was 40% prior to a realized Opex saving of 5%, one would expect the margin (or Ebitda) saving to be 1.5x that saving or 7.5%.

In general I would expect up-to 35% Opex saving on relevant technology cost structure from network sharing on established networks. If much more saving is claimed, we should get skeptical of the analysis and certainly not take it on face value. It is not un-usual to see Network Sharing contributing as much as 20% saving (and avoidance on run-rate) on the overall Network Opex (ignoring IT Opex here!).

Why not 50% saving (or avoidance)? You may ask! But only once please!

After all we are taking 2 RAN networks and migrating them into 1 network … surely that should result in at 50% saving (i.e., always on relevant cost structure).

First of all, not all relevant (to cellular sites) cost structure is in general relevant to network sharing. Think here about energy consumption and transport solutions as the most obvious examples. Further, landlords are not likely to allow you to directly share existing site locations, and thus site lease cost with another operator without asking for an increased lease (i.e., 20% to 40% is not un-heard of). Existing lease contracts might need to be opened up to allow sharing, terms & conditions will likely need to be re-negotiated, etc.. in the end site lease savings are achievable but these will not translate into a 50% saving.

WARNING! 50% saving claims as a result of Network Sharing are not to be taken at face value!

Another interesting effect is that more shared sites will eventually result compared to the standalone number of sites. In other words, the shared network will have sites than either of the two networks standalone (and hopefully less than the combined amount of sites prior to sharing & consolidation). The reason for this is that the two sharing parties networks rarely are completely symmetric when it comes to coverage. Thus the shared network that will be somewhat bigger than compared to the standalone networks and thus safeguard the customer experience and hopefully the revenue in a post-merged network scenario. If the ultimate shared network has been planned & optimized properly, both parties customers will experience an increased network quality in terms of coverage and capacity (i.e., speed).

#SitesA , #SitesB < #SitesA+B < #SitesA + #SitesB

The Shared Network should always provide a better network customer experience than each standalone networks.

I have experienced Executives argue (usually post-deal obviously!) that it is not possible to remove sites, as any site removed will destroy customer experience. Let me be clear, If the shared network is planned & optimized according with best practices the shared network will deliver a substantial better network experience to the combined customer base than the respective standalone networks.

Lets dive deeper into the Technology Cost Structure. As the Figure below shows (i.e., typical for mature western markets) we have the following high level cost distribution for the Technology Opex

  1. 10% to 15% for Core Network
  2. 20% to 40% for IT & Platforms and finally
  3. 45% to 70% for RAN.

The RAN Opex for markets without energy distribution challenges, i.e., mature & reliable energy delivery grid) is split in (a) ca. 40% (i.e., of the RAN Opex) for Rental & Leasing which is clearly addressable by Network Sharing, (b) ca. 25% in Services including Maintenance & Repair of which at least the non-Telco part is easily addressable by Network Sharing, (c) ca. 15% Personnel Cost also addressable by Network Sharing, (d) 10% Leased Lines (typical backhaul connectivity) is less dependent on Network Sharing although bandwidth volume discounts might be achievable by sharing connectivity to a shared site and finally (e) Energy & other Opex costs would in general not be impacted substantially by Network Sharing. Note that for markets with a high share of diesel generators and fuel logistics, the share of Energy cost within the RAN Opex cost category will be substantially larger than depicted here.

It is important to note here that sharing of Managed Energy Provision, similar to Tower Company lease arrangement, might provide financial synergies. However, typically one would expect Capex Avoidance (i.e., by not buying power systems) on the account of an increased Energy Opex Cost (compared to standalone energy management) for the managed services. Obviously, if such a power managed service arrangement can be shared, there might be some synergies to be gained from such an arrangement. In my opinion this is particular interesting for markets with a high reliance of diesel generators and fuelling logistics.This said

Power sharing in mature markets with high electrification rates can offer synergies on energy via applicable volume discounts though would require shared metering (which might not always be particular well appreciated by power companies).technology cost distribution

Maybe as much as

80% of the total RAN Opex can be positively impacted (i.e., reduced) by network sharing.

Above cost structure illustration also explain why I rarely get very exited about sharing measures in Core Network Domain (i.e., spend too much time in the past to explain that while NG Core Network might save 50% of relevant cost it really was not very impressive in absolute terms and efforts was better spend on more substantial cost structure elements). Assume you can save 50% (which is a bit on the wild side today) on Core Network Opex (even Capex is in proportion to RAN fairly smallish). That 50% saving on Core translates into maybe maximum 5% of the Network Opex as opposed to RAN’s 15% – 20%. Sharing Core Network resources with another party does require substantially more overhead management and supervision than even fairly aggressive RAN sharing scenarios (with substantial active sharing).

This said, I believe that there are some internal efficiency measures to Telco Groups (with superior interconnection) and very interesting new business models out there that do provide core network & computing infrastructure as a service to Telco’s (and in principle allow multiple Telco’s to share the core network platforms and resources. My 2012 presentation on Ultra-Efficient Network Factory: Network Sharing & other means to leapfrog operator efficiencies. illustrates how such business models might work out. The first describes in largely generic terms how virtualization (e.g., NFV) and cloud-based technologies could be exploited. The LTE-as-a-Service (could be UMTS-as-a-Service as well of course) is more operator specific. The verdict is still out there whether truly new business models can provide meaningful economics for customer networks and business. In the longer run, I am fairly convinced, that scale and expected massive improvements in connectivity in-countries and between-countries will make these business models economical interesting for many tier-2, tier-3 and Generation-Z businesses.

businessmodels2

businessmodels1

BUT BUT … WHAT ABOUT CAPEX?

From a Network Sharing perspective Capex synergies or Capex avoidance are particular interesting at the beginning of a network rollout (i.e., Rollout Phase) as well as at the end of the Steady State where technology refreshment is required (i.e., the Modernization Phase).

Obviously, in a site deployment heavy scenario (e.g., start-ups) sharing the materials and construction cost of greenfield tower or rooftop (in as much as it can be shared) will dramatically lower the capital cost of deployment. In particular as you and your competitor(s) would likely want to cover pretty much the same places and thus sharing does become very compelling and a rational choice. Unless its more attractive to block your competitor from gaining access to interesting locations.

Irrespective, between 40% to 50% of an operators sites will only generate up-to 10% of the turnover. Those ugly-cost-tail sites will typically be in rural areas (including forests) and also on average be more costly to deploy and operate than sites in urban areas and along major roads.

Sharing 40% – 50% of sites, also known as the ugly-cost-tail sites, should really be a no brainer!

Depending on the market, the country particulars, and whether we look at emerging or mature markets there might be more or less Tower sites versus rooftops. Rooftops are less obvious passive sharing candidates, while Towers obviously are almost perfect passive sharing candidates provided the linked budget for the coverage can be maintained post-sharing. Active sharing does make rooftop sharing more interesting and might reduce the tower design specifications and thus optimize Capex further in a deployment scenario.

As operators faces RAN modernization pressures it can Capex-wise become very interesting to discuss active as well as passive sharing with a competitor in the same situation. There are joint-procurement benefits to be gained as well as site consolidation scenarios that will offer better long-term Opex trends. Particular T-Mobile and Hutchinson in the UK (and T-Mobile and Orange as well in UK and beyond) have championed this approach reporting very substantial sourcing Capex synergies by sharing procurements. Note network sharing and sharing sourcing in a modernization scenario does not force operators to engage in full active network sharing. However, it is a pre-requisite that there is an agreement on the infrastructure supplier(s).

Network Sharing triggered by modernization requirements is primarily interesting (again Capex wise) if part of electronics and ancillary can be shared (i.e., active sharing). Suppliers match is an obviously must for optimum benefits. Otherwise the economical benefits will be weighted towards Opex if a sizable amount of sites can be phased out as a result of site consolidation.

total_overview

The above Figure provides an overview of the most interesting components of Network Sharing. It should be noted that Capex prevention is in particular relevant to (1) The Rollout Phase and (2) The Modernization Phase. Opex prevention is always applicable throughout the main 3 stages Network Sharing Attractiveness Cycles. In general the Regulatory Complexity tend to be higher for Active Sharing Scenarios and less problematic for Passive Sharing Scenarios. In general Regulatory Authorities would (or should) encourage & incentivize passive site sharing ensuring that an optimum site infrastructure (i.e., number of towers & rooftops) is being built out (in greenfield markets) or consolidated (in established / mature markets). Even today it is not un-usual to find several towers, each occupied with a single operator, next to each other or within hundred of meters distance.

NETWORK SHARING DOES NOT COME FOR FREE!

One of the first things a responsible executive should ask when faced with the wonderful promises of network sharing synergies in form of Ebitda and cash improvements is

What does it cost me to network share?

The amount of re-structuring or termination cost that will be incurred before Network Sharing benefits can be realized will depend a lot on which part of the Network Sharing Cycle.

(1) The Rollout Phase in which case re-structuring cost is likely to be minimum as there is little or nothing to restructure. Further, also in this case write-off of existing investments and assets would likewise be very small or non-existent pending on how far into the rollout the business would be. What might complicate matters are whether sourcing contracts needs to be changed or cancelled and thus result in possible penalty costs. In any event being able to deploy together the network from the beginning does (in theory) result in the least deployment complexity and best deployment economics. However, getting to the point of agreeing to shared deployment (i.e., which also requires a reasonable common site grid) might be a long and bumpy road. Ultimately, launch timing will be critical to whether two operators can agree on all the bits and pieces in time not to endanger targeted launch.

Network Sharing in the Rollout Phase is characterized by

  • Little restructuring & termination cost expected.
  • High Capex avoidance potential.
  • High  Opex avoidance potential.
  • Little to no infrastructure write-offs.
  • Little to no risk of contract termination penalties.
  • “Normal” network deployment project (though can be messed up by too many cooks syndrome).
  • Best network potential.

    (2) The Steady State Phase, where a substantial part of the networks have been rollout out, tend to be the most complex and costly phase to engage in Network Sharing passive and of course active sharing. A substantial amount of site leases would need to be broken, terminated or re-structured to allow for network sharing. In all cases either penalties or lease increases are likely to result. Infrastructure supplier contracts, typically maintenance & operations agreements, might likewise be terminated or changed substantially. Same holds for leased transmission. Write-off can be very substantial in this phase as relative new sites might be terminated, new radio equipment might become redundant or phased-out, etc If one or both sharing partners are in this phase of the business & network cycle the chance of a network sharing agreement is low. However, if a substantial amount of both parties site locations will be used to enhance the resulting network and a substantial part of the active equipment will be re-used and contracts expanded then sharing tends to be going ahead. A good example of this is in the UK with Vodafone and O2 site sharing agreement with the aim to leapfrog number of sites to match that of EE (Orange + T-Mobile UK JV) for improved customer experience and remain competitive with the EE network.

    Network Sharing in the Steady State Phase is characterized by

  • Very high restructuring & termination cost expected.
  • None or little Capex synergies.
  • Substantial Opex savings potential.
  • Very high infrastructure write-offs.
  • Very high termination penalties incl. site lease termination.
  • Highly complex consolidation project.
  • Medium to long-term network quality & optimization issues.

    (3) Once operators approaches the Modernization Phase more aggressive network sharing scenarios can be considered as the including joint sourcing and infrastructure procurement (e.g., a la T-Mobile UK and Hutchinson in UK). At this stage typically the remainder of the site leases term will be lower and penalties due to lease termination as a result lower as well. Furthermore, at this point in time little (or at least substantially lower than in the steady state phase) residual value should remain in the active and also passive infrastructure. The Modernization Phase is a very opportune moment to consider network sharing, passive as well as active, resulting in both substantial Capex avoidance and of course very attractive Opex savings mitigating a stagnating or declining topline as well as de-risking future loss of profitability.

    Network Sharing in the Modernization Phase is characterized by

    • Relative moderate restructuring & termination cost expected.
    • High Capex avoidance potential.
    • Substantial Opex saving potential.
    • Little infrastructure write-offs.
    • Lower risk of contract termination penalties.
    • Manageable consolidation project.
    • Instant cell splits and cost-efficient provision of network capacity.
    • More aggressive network optimization –> better network.

    As a rule of thumb I usually recommend to estimate restructuring / termination cost as follows (i.e., if you don’t have the real terms & conditions of contracts by the hand);

    1. 1.5 to 3+ times the estimated Opex savings – use the higher multiple in the Steady State Phase and the Lower for Modernization Phase.
    2. Consolidation Capex will often be partly synergetic with Business-as-Usual (BaU) Capex and should not be fully considered (typically between 25% to 50% of consolidation Capex can be mapped to BaU Capex).
    3. Write-offs should be considered and will be the most pain-full to cope with in the Steady State Phase.

    NATIONAL ROAMING AS AN ALTERNATIVE TO NETWORK SHARING.

    A National Roaming agreement will save network investments and the resulting technology Opex. So in terms of avoiding technology cost that’s an easy one. Of course from a Profit & Loss (P&L) perspective I am replacing my technology Opex and Capex with wholesale cost somewhere else in my P&L. Whether National Roaming is attractive or not will depend a lot of anticipated traffic and of course the wholesale rate the hosting network will charge for the national roaming service. Hutchinson in UK (as well in other markets) had for many years a GSM national roaming agreement with Orange UK, that allowed its customers basic services outside its UMTS coverage footprint. In Austria for example, Hutchinson (i.e., 3 Austria) provide their customers with GSM national roaming services on T-Mobile Austria’s 2G network (i.e., where 3 Austria don’t cover with their own 3G) and T-Mobile Austria has 3G national roaming arrangement with Hutchinson in areas that they do not cover with 3G.

    In my opinion whether national roaming make sense or not really boils down to 3 major considerations for both parties:

    national_roaming

    There are plenty of examples on National Roaming which in principle can provide similar benefits to infrastructure sharing by avoidance of Capex & Opex that is being replaced by the cost associated with the traffic on the hosting network.The Hosting MNO gets wholesale revenue from the national roaming traffic which the Host supports in low-traffic areas or on a under-utilized network. National roaming agreements or relationships tends to be of temporary nature.

    It should be noted that National Roaming is defined in an area were 1-Party The Host has network coverage (with excess capacity) and another operator (i.e., The Roamer or The Guest) has no network coverage but has a desire to offer its customers service in that particular area. In general only the host’s HPLMN is been broadcasted on the national roaming network. However, with Multi-Operator Core Network (MOCN) feature it is possible to present the national roamer with the experience of his own network provided the roamers terminal equipment supports MOCN (i.e., Release 8 & later terminal equipment will support this feature).

    In many Network Sharing scenarios both parties have existing and overlapping networks and would like to consolidate their networks to one shared network without loosing service quality. The reduction in site locations provide the economical benefits of network sharing. Throughout the shared network both operators will radiate  their respective HPLMNs and the shared network will be completely transparent to their respective customer bases.

    While having been part of several discussions to shut down one networks in geographical areas of a market and move customers to a host overlapping (or better) network via a national roaming agreement, I am not aware of mobile operators which have actually gone down this path.

    Regulatory and from a spectrum safeguard perspective it might be a better approach to commission both parties frequencies on the same network infrastructure and make use of for example the MOCN feature that allows full customer transparency (at least for Release 8 and later terminals).

    national_roaming _examples

    National Roaming is fully standardized and a well proven arrangement in many markets around the world. One does need to be a bit careful with how the national roaming areas are defined/implemented and also how customers move back and forth from a national roaming area (and technology) to home area (and technology). I have seen national roaming arrangements not being implemented because the dynamics was too complex to manage. The “cleaner” the national roaming area is the simpler does the on-off national roaming dynamics become. With “Clean” is mean keep the number of boundaries between own and national roaming network low, go for contiguous areas rather than many islands, avoid different technology coverage overlap (i.e., area with GSM coverage, it should avoided to do UMTS national roaming), etc.. Note you can engineer a “dirty” national roaming scenario of course. However, those tend to be fairly complex and customer experience management tends to be sub-optimal.

    Network Sharing and National Roaming are from a P&L perspective pretty similar in the efficiency and savings potentials. The biggest difference really is in the Usage Based cost item where a National Roaming would incur higher cost than compared to a Network Sharing arrangement.

    p&l_comparison

    An Example: Operator contemplate 2 scenarios;

    1. Network Sharing in rural area addressing 500 sites.
    2. Terminate 500 sites in rural area and make use of National Roaming Agreement.

    What we are really interested in, is to understand when Network Sharing provides better economics than National Roaming and of course vice versa.

    National Roaming can be attractive for relative low traffic scenarios or in case were product of traffic units and national roaming unit cost remains manageable and lower than the Shared Network Cost.

    national roaming vs network sharing

    The above illustration ignores the write-off and termination charges that might result from terminating a given number of sites in a region and then migrate traffic to a national roaming network (note I have not seen any examples of such scenarios in my studies).

    The termination cost or restructuring cost, including write-off of existing telecom assets (i.e., radio nodes, passive site solutions, transmission, aggregation nodes, etc….) is likely to be a substantially financial burden to National Roaming Business Case in an area with existing telecom infrastructure. Certainly above and beyond that of a Network Sharing scenario where assets are being re-used and restructuring cost might be partially shared between the sharing partners.

    Obviously, if National Roaming is established in an area that has no network coverage, restructuring and termination cost is not an issue and Network TCO will clearly be avoided, Albeit the above economical logic and P&L trade-offs on cost still applies.

    National Roaming can be an interesting economical alternative, at least temporarily, to Network Sharing or establishing new coverage in an area with established network operators.

    However, National Roaming agreements are usually of temporary nature as establishing own coverage either standalone or via Network Sharing eventually will be a better economical and strategic choice than continuing with the national roaming agreement.

    SHARING BY TOWER COMPANY (TOWERCO).

    There is a school of thought, within the Telecommunications Industry, that very much promotes the idea of relying on Tower Companies (Towerco) to provide and manage passive telecom site infrastructure.

    The mobile operator leases space from the Towerco on the tower (or in some instances a rooftop) for antennas, radio units and possible microwave dishes. Also the lease would include some real estate space around the tower site location for the telecom racks and ancillary equipment.

    In the last 10 years many operators have sold off their tower assets to Tower companies that then lease those back to the mobile operator.

    In most Towerco deals, Mobile Operators are trading off up-front cash for long-term lease commitments.

    With the danger of generalizing, Towerco deals made by operators in my opinion have a bit the nature and philosophy of “The little boy peeing in his trousers on a cold winter day, it will warm him for a short while, in the long run he will freeze much more after the act”. Let us also be clear that the business down the road will not care about a brilliant tower deal (done in the past) if it pressures their Ebitda and Site Lease cost.

    In general the Tower company will try (should be incented) to increase the tower tenancy (i.e., having more tenants per tower). Pending on the lease contract the Towerco might (should!) provide the mobile operator lease discount as more tenants are added to a given tower infrastructure.

    Towerco versus Network Sharing is obviously a Opex versus Capex trade-off. Anyway, lets look at a simple total-cost-of-ownership example that allows us to understand better when one strategy could be better than the other.towerco vs network sharing

    From the above very simple and high level per tower total-cost-of-ownership model its clear that a Towerco would have some challenges in matching the economics of the Shared Network. A Mobile Operator would most likely (in above example) be better of commencing on a simple tower sharing model (assuming a sharing partner is available and not engaging with another Towerco) rather than leasing towers from a Towerco. The above economics is ca. 600 US$ TCO per month (2-sharing scenario) compared to ca. 1,100 (2-tenant scenario). Actually, unless the Towerco is able to (a) increase occupancy beyond 2, (b) reduce its productions cost well below what the mobile operators would be (without sacrificing quality too much), and (c) at a sufficient low margin, it is difficult to see how a Towerco can provide a Tower solution at better economics than conventional network shared tower.

    This said it should also be clear that the devil will be in the details and there are various P&L and financial engineering options available to mobile operators and Towercos that will improve on the Towerco model. In terms of discounted cash flow and NPV analysis of the cash flows over the full useful life period the Network Sharing model (2-parties) and Towerco lease model with 2-tenants can be made fairly similar in terms of value. However, for 2-tenant versus 2-party sharing, the Ebitda tends to be in favor of network sharing.

    For the Mobile Network Operator (MNO) it is a question of committing Capital upfront versus an increased lease payment over a longer period of time. Obviously the cost of capital factors in here and the inherent business model risk. The inherent risk factors for the Towerco needs to be considered in its WACC (weighted average cost of capital) and of course the overall business model exposure to

    1. Operator business failure or consolidation.
    2. Future Network Sharing and subsequent lease termination.
    3. Tenant occupancy remains low.
    4. Contract penalties for Towerco non-performance, etc..

    Given the fairly large inherent risk (to Towerco business models) of operator consolidation in mature markets, with more than 3 mobile operators, there would be a “wicked” logic in trying to mitigate consolidation scenarios with costly breakaway clauses and higher margins.

    From all the above it should be evident that for mobile operators with considerable tower portfolios and also sharing ambitions, it is far better to (First) Consolidate & optimize their tower portfolios, ensuring minimum 2 tenants on each tower and then (Second) spin-off (when the cash is really needed) the optimized tower portfolio to a Towerco ensuring that the long-term lease is tenant & Ebitda optimized (as that really is going to be any mobile operations biggest longer term headache as markets starts to saturate).

    SUMMARY OF PART I – THE FUNDAMENTALS.

    There should be little doubt that

    Network Sharing provides one of the biggest financial efficiency levers available to mobile network operator.

    Maybe apart from reducing market invest… but that is obviously not really a sustainable medium-long-term strategy.

    In aggressive network sharing scenarios Opex savings in the order of 35% is achievable as well as future Opex avoidance in the run-rate. Depending on the Network Sharing Scenario substantial Capex can be avoided by sharing the infrastructure built-out (i.e., The Rollout Phase) and likewise in the Modernization Phase. Both allows for very comprehensive sharing of both passive and active infrastructure and the associated capital expenses.

    Both National Roaming and Sharing via Towerco can be interesting concepts and if engineered well (particular financially) can provide similar benefits as sharing (active as well as passive, respectively). Particular in cash constrained scenarios (or where operators see an extraordinary business risk and want to minimize cash exposure) both options can be attractive. Long-term National Roaming is particular attractive in areas where an operator have no coverage and has little strategic importance. In case an area is strategically important, national roaming can act as a time-bridge until presence has been secure possibly via Network Sharing (if competitor is willing).

    Sharing via Towerco can also be an option when two parties are having trust issues. Having a 3rd party facilitating the sharing is then an option.

    In my opinion National Roaming & Sharing via Towerco rarely as Ebitda efficient as conventional Network Sharing.

    Finally! Why should you stay away from Network Sharing?

    This question is important to answer as well as why you should (which always seems initially the easiest). Either to indeed NOT to go down the path of network sharing or at the very least ensure that point of concerns and possible blocking points have been though roughly considered and checked of.

    So here comes some of my favorites … too many of those below you are not terrible likely to be successful in this endeavor:

    whynotsharing

    ACKNOWLEDGEMENT

    I would like to thank many colleagues for support and Network Sharing discussions over the past 13 years. However, in particular I owe a lot to David Haszeldine (Deutsche Telekom) for his insights and thoughts. David has been my true brother-in-arms throughout my Deutsche Telekom years and on our many Network Sharing experiences we have had around the world. I have had many & great discussions with David on the ins-and-outs of Network Sharing … Not sure we cracked it all? … but pretty sure we are at the forefront of understanding what Network Sharing can be and also what it most definitely cannot do for a Mobile Operator. Of course similar to all the people who have left comments on my public presentations and gotten in contact with me on this very exiting and by no way near exhausted topic of how to share networks.

    The term the “Ugly Tail” as referring to rural and low-profitability sites present in all networks should really be attributed to Fergal Kelly (now CTO of Vodafone Ireland) from a meeting quiet a few years ago. The term is too good not to borrow … Thanks Fergal!

    This story is PART I and as such it obviously would indicate that another Part is on the way Winking smilePART II“Network Sharing – That was then, this is now” will be on the many projects I have worked on in my professional career and lessons learned (all available in the public domain of course). Here obviously providing a comparison with the original ambition level and plans with the reality is going to be cool (and in some instances painful as well). PART III“The Tools” will describe the arsenal of tools and models that I have developed over the last 13 years and used extensively on many projects.

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    Time Value of Money, Real Options, Uncertainty & Risk in Technology Investment Decisions

    “We have met the Enemy … and he is us”

    is how the Kauffman Foundations starts their extensive report on investments in Venture Capital Funds and their abysmal poor performance over the last 20 years. Only 20 out of 200 Venture Funds generated returns that beat the public-market equivalent with more than 3%. 10 of those were Funds created prior to 1995. Clearly there is something rotten in the state of valuation, value creation and management. Is this state of affairs limited only to portfolio management (i..e, one might have hoped a better diversified VC portfolio) is this poor track record on investment decisions (even diversified portfolios) generic to any investment decision made in any business? I let smarter people answer this question. Though there is little doubt in my mind that the quote “We have met the Enemy … and he is us” could apply to most corporations and the VC results might not be that far away from any corporation’s internal investment portfolio. Most business models and business cases will be subject to wishful thinking and a whole artillery of other biases that will tend to overemphasize the positives and under-estimate (or ignore) the negatives.The avoidance of scenario thinking and reference class forecasting will tend to bias investments towards the upper boundaries and beyond of the achievable and ignore more attractive propositions that could be more valuable than the idea that is being pursued.

    As I was going through my archive I stumbled over an old paper I wrote back in 2006 when I worked for T-Mobile International and Deutsche Telekom (a companion presentation due on Slideshare). At the time I was heavily engaged with Finance and Strategy in transforming Technology Investment Decision Making into a more economical responsible framework than had been the case previously. My paper was a call for more sophisticated approaches to technology investments decisions in the telecom sector as opposed to what was “standard practice” at the time and in my opinion pretty much still i.

    Many who are involved in techno-economical & financial analysis as well as the decision makers acting upon recommendations from their analysts are in danger of basing their decisions on flawed economical analysis or simply have no appreciation of uncertainty and risk involved. A frequent mistake made in decision making of investment options is ignoring one of the most central themes of finance & economics, the Time-Value-of-Money. An investment decision taken was insensitive to the timing of the money flow. Furthermore, investment decisions based on Naïve TCO are good examples of such insensitivity bias and can lead to highly in-efficient decision making. Naïve here implies that time and timing does not matter in the analysis and subsequent decision.

    Time-Value-of-Money:

    I like to get my money today rather than tomorrow, but I don’t mind paying tomorrow rather than today”.

    Time and timing matters when it comes to cash. Any investment decision that does not consider timing of expenses and/or income has a substantially higher likelihood of being an economical in-efficient decision. Costing the shareholders and investors (a lot of) money. As a side note Time-Value-of-Money assumes that you can actually do something with the cash today that is more valuable than waiting for it at a point in the future. Now that might work well for Homo Economicus but maybe not so for the majority of the human race (incl. Homo Financius).

    Thus, if I am insensitive to timing of payments it does not matter for example whether I have to pay €110 Million more for a system the first year compared to deferring that increment to the 5th year

    Clearly wrong!

    naive tco

    In the above illustration outgoing cash flow (CF) example the naïve TCO (i..e, total cost of ownership) is similar for both CFs. I use the word naïve here to represent a non-discounted valuation framework. Both Blue and Orange CFs represent a naïve TCO value of €200 Million. So a decision maker (or an analyst) not considering time-value-of-money would be indifferent to one or the other cash flow scenario. Would the decision maker consider time-value-of-money (or in the above very obvious case see the timing of cash out) clear it would be in favor of Blue. Further front-loaded investment decisions are scary endeavors, particular for unproven technologies or business decisions with a high degree of future unknowns, as the exposure to risks and losses are so much higher than a more carefully designed cash-out/investment trajectory following the reduction of risk or increased growth. When only presented with the (naïve) TCO rather than the cash flows, it might even be that some scenarios might be unfavorable from a naïve TCO framework but favorable when time-value-of-money is considered. The following illustrates this;

    naive tco vs dcf

    The Orange CF above amounts to a naïve TCO of €180 Million versus to the Blue’s TCO of €200 Million. Clearly if all the decision maker is presented with is the two (naïve) TCOs, he can only choose the Orange scenario and “save” €20 Million. However, when time-value-of-money is considered the decision should clearly be for the Blue scenario that in terms of discounted cash flows yields €18 Million in its favor despite the TCO of €20 Million in favor of Orange. Obviously, the Blue scenario has many other advantages as opposed to Orange.

     

    When does it make sense to invest in the future?

     

    Frequently we are faced with  technology investment decisions that require spending incremental cash now for a feature or functionality that we might only need at some point in the future. We believe that the cash-out today is more efficient (i.e., better value) than introducing the feature/functionality at the time when we believe it might really be needed..

     

    Example of the value of optionality: Assuming that you have two investment options and you need to provide management with which of those two are more favorable.

     

    Product X with investment I1: provides support for 2 functionalities you need today and 1 that might be needed in the future (i.e., 3 Functionalities in total).

    Product Y with investment I2: provides support for the 2 functionalities you need today and 3 functionalities that you might need in the future (i.e., 5 Functionalities in total).

     

    I1 < I2 and \Delta = I2I1 > 0

     

    If, in the future, we need more than 1 additional functionality it clearly make sense to ask whether it is better upfront to invest in Product Y, rather than X and then later Y (when needed). Particular when Product X would have to be de-commissioned when introducing Product Y, it is quite possible that investing in Product Y upfront is more favorable. 

     

    From a naïve TCO perspective it clearly better to invest in Y than X + Y. The “naïve” analyst would claim that this saves us at least I1 (if he is really clever de-installation cost and write-offs might be included as well as saving or avoidance cost) by investing in Y upfront.

     

    Of course if it should turn out that we do not need all the extra functionality that Product Y provides (within the useful life of Product X) then we have clearly made a mistake and over-invested by\Delta and would have been better off sticking to Product X (i.e., the reference is now between investing in Product Y versus Product X upfront).

     

    Once we call upon an option, make an investment decision, other possibilities and alternatives are banished to the “land of lost opportunities”.

     

    Considering time-value-of-money (i.e., discounted cash-flows) the math would still come out more favorable for Y than X+Y, though the incremental penalty would be lower as the future investment in Product Y would be later and the investment would be discounted back to Present Value.

     

    So we should always upfront invest in the future?

     

    Categorically no we should not!

     

    Above we have identified 2 outcomes (though there are others as well);

    Outcome 1: Product Y is not needed within lifetime T of Product X.

    Outcome 2: Product Y is needed within lifetime T of Product X.

     

    In our example, for Outcome 1 the NPV difference between Product X and Product Y is -10 Million US$. If we invest into Product Y and do not need all its functionality within the lifetime of Product X we would have “wasted” 10 Million US$ (i.e., opportunity cost) that could have been avoided by sticking to Product X.

     

    The value of Outcome 2 is a bit more complicated as it depends on when Product Y is required within the lifetime of Product X. Let’s assume that Product X useful lifetime is 7 years, i.e., after which period we would need to replace Product X anyway requiring a modernization investment. We assume that for the first 2 years (i.e., yr 2 and yr 3) there is no need for the additional functionality that Product Y offers (or it would be obvious to deploy up-front at least within this examples economics). From Year 4 to Year 7 there is an increased likelihood of the functionalities of Product X to be required.

     

    product Y npv

    In Outcome 2 the blended NPV is 3.0 Million US$ positive to deploy Product X instead of Product Y and then later Product X (i.e., the X+Y scenario) when it is required. After the 7th year we would have to re-invest in a new product and the obviously looking beyond this timeline makes little sense in our simplified investment example.

     

    Finally if we assess that there is a 40% chance that the Product Y will not be required within the life-time of Product X, we have the overall effective NPV of our options would be negative (i.e., 40%(-10) + 3 = –1 Million). Thus we conclude it is better to defer the investment in Product Y than to invest in it upfront. In other words it is economical more valuable to deploy Product X within this examples assumptions.

     

    I could make an even stronger case for deferring investing in Product Y: (1) if I can re-use Product X when I introduce Product Y, (2) if I believe that the price of Product Y will be much lower in the future (i..e, due to maturity and competition), or (3) that there is a relative high likelihood that the Product Y might become obsolete before the additional functionalities are required (e.g., new superior products at lower cost compared to Product Y). The last point is often found when investing into the very first product releases (i.e., substantial immaturity) or highly innovative products just being introduced. Moreover, there might be lower-cost lower-tech options that could provide the same functionality when required that would make investing upfront in higher-tech higher-cost options un-economical. For example, a product that provide a single targeted functionality at the point in time it is needed, might be more economical than investing in a product supporting 5 functionalities (of which 3 is not required) long before it is really required.

     

    Many business cases are narrowly focusing on proving a particular point of view. Typically maximum 2 scenarios are compared directly, the old way and the proposed way. No surprise! The new proposed way of doing things will be more favorable than the old (why else do the analysis;-). While such analysis cannot be claimed to be wrong, it poses the danger of ignoring more valuable options available (but ignored by the analyst). The value of optionality and timing is ignored in most business cases.

     

    For many technology investment decisions time is more a friend than an enemy. Deferring investing into a promise of future functionality is frequently the better value-optimizing strategy.

     

    Rules of my thumb:

    • If a functionality is likely to be required beyond 36 months, the better decision is to defer the investment to later.
    • Innovative products with no immediate use are better introduced later rather than sooner as improvement cycles and competition are going to make such more economical to introduce later (and we avoid obsolescence risk).
    • Right timing is better than being the first (e.g., as Apple has proven a couple of times).

    Decision makers are frequently betting on a future event (i..e, knowingly or unknowingly) will happen and that making an incremental investment decision today is more valuable than deferring the decision to later. Basically we deal with an Option or a Choice. When we deal with a non-financial Option we will call such a Real Option. Analyzing Real Options can be complex. Many factors needs to be considered in order to form a reasonable judgment of whether investing today in a functionality that only later might be required makes sense or not;

    1. When will the functionality be required (i.e., the earliest, most-likely and the latest).
    2. Given the timing of when it is required, what is the likelihood that something cheaper and better will be available (i.e., price-erosion, product competition, product development, etc..).
    3. Solutions obsolescence risks.

    As there are various uncertain elements involved in whether or not to invest in a Real Option the analysis cannot be treated as a normal deterministic discounted cash flow. The probabilistic nature of the decision analysis needs to be correctly reflected in the analysis.

     

    Most business models & cases are deterministic despite the probabilistic (i.e., uncertain and risky) nature they aim to address.

     

    Most business models & cases are 1-dimensional in the sense of only considering what the analyst tries to prove and not per se alternative options.

     

    My 2006 paper deals with such decisions and how to analyze them systematically and provide a richer and hopefully better framework for decision making subject to uncertainty (i.e., a fairly high proportion of investment decisions within technology).

    Enjoy Winking smile!

    ABSTRACT

    The typical business case analysis, based on discounted cash flows (DCF) and net-present valuation (NPV), inherently assumes that the future is known and that regardless of future events the business will follow the strategy laid down in the present. It is obvious that the future is not deterministic but highly probabilistic, and that, depending on events, a company’s strategy will be adopted to achieve maximum value out of its operation. It is important for a company to manage its investment portfolio actively and understand which strategic options generate the highest return on investment. In every technology decision our industry is faced with various embedded options, which needs to be considered together with the ever-prevalent uncertainty and risk of the real world. It is often overlooked that uncertainty creates a wealth of opportunities if the risk can be managed by mitigation and hedging. An important result concerning options is that the higher the uncertainty of the underlying asset, the more valuable could the related option become. This paper will provide the background for conventional project valuation, such as DCF and NPV. Moreover, it will be shown how a deterministic (i.e., conventional) business case easily can be made probabilistic, and what additional information can be gained with simulating the private as well as market-related uncertainties. Finally, real options analysis (ROA) will be presented as a natural extension of the conventional net-present value analysis. This paper will provide several examples of options in technology, such as radio access site-rollout strategies, product development options, and platform architectural choices.

    INTRODUCTION

    In technology, as well as in mainstream finance, business decisions are more often than not based on discounted cash flow (DCF) calculations using net-present value (NPV) as decision rationale for initiating substantial investments. Irrespective of the complexity and multitudes of assumptions made in business modeling the decision is represented by one single figure, the net present value. The NPV basically takes the future cash flows and discount these back to the present, assuming a so-called “risk –adjusted” discount rate. In most conventional analysis the “risk-adjusted” rate is chosen rather arbitrarily (e.g., 10%-25%) and is assumed to represent all project uncertainties and risks.The risk-adjusted rate should always as a good practice be compared with the weighted average cost of capital (WACC) and benchmarked against what Capital Asset Pricing Model (CAPM) would yield. Though in general the base rate will be set by your finance department and not per se something the analyst needs to worry too much about. Suffice to say that I am not a believer that all risk can be accounted for in the discount rate and that including risks/uncertainty into the cash flow model is essential.

     

    It is naïve to believe that the applied discount rate can account for all risk a project may face.

     

    In many respects the conventional valuation can be seen as supporting a one-dimensional decision process. DCF and NPV methodologies are commonly accepted in our industry and the finance community [1]. However, there is a lack of understanding of how uncertainty and risk, which is part of our business, impacts the methodology in use. The bulk of business cases and plans are deterministic by design. It would be far more appropriate to work with probabilistic business models reflecting uncertainty and risk. A probabilistic business model, in the hands of the true practitioner, provides considerable insight useful for steering strategic investment initiatives. It is essential that a proper balance is found between model complexity and result transparency. With available tools, such as Palisade Corporation’s @RISK Microsoft Excel add-in software [2], it is very easy to convert a conventional business case into a probabilistic model. The Analyst would need to converse with subject-matter experts in order to provide a reasonable representation of relevant uncertainties, statistical distributions, and their ranges in the probabilistic business model [3].

     

    In this paper the word Uncertainty will be used as representing the stochastic (i.e., random) nature of the environment. Uncertainty as concept represents events and external factors, which cannot be directly controlled. The word Volatility will be used interchangeably with uncertainty. With Risk is meant the exposure to uncertainty, e.g., uncertain cash-flows resulting in out-of-money and catastrophic business failure. The total risk is determined by the collection of uncertain events and Management’s ability to deal with these uncertainties through mitigation and “luck”. Moreover, the words Option and Choice will also be used interchangeably throughout this paper.

     

    Luck is something that never should be underestimated.

     

    While working on the T-Mobile NL business case for the implementation of Wireless Application Protocol (WAP) for circuit switched data (CSD), a case was presented showing a 10% chance of losing money (over a 3 year period). The business case also showed an expected NPV of €10 Million, as well as a 10% chance of making more than €20 Million over a 3 year period. The spread in the NPV, due to identified uncertainties, were graphically visualized.

     

    Management, however, requested only to be presented with the “normal” business case NPV as this “was what they could make a decision upon”. It is worthwhile to understand that the presenters made the mistake to make the presentation to Management too probabilistic and mathematical which in retrospect was a wrong approach [4]. Furthermore, as WAP was seen as something strategically important for long-term business survival, moving towards mobile data, it is not conceivable that Management would have turned down WAP even if the business case had been negative.

    In retrospect, the WAP business case would have been more useful if it had pointed out the value of the embedded options inherent in the project;

    1. Defer/delay until market conditions became more certain.
    2. Defer/delay until GPRS became available.
    3. Outsource service with option to in-source or terminate depending on market conditions and service uptake.
    4. Defer/delay until technology becomes more mature, etc..

    Financial “wisdom” states that business decisions should be made which targets the creation of value [5]. It is widely accepted that given a positive NPV, monetary value will be created for the company therefore projects with positive NPV should be implemented. Most companies’ investment means are limited. Innovative companies often are in a situation with more funding demand than available. It is therefore reasonable that projects targeting superior NPVs should be chosen. Considering the importance and weight businesses associate with the conventional analysis using DCF and NPV it worthwhile summarizing the key assumptions underlying decisions made using NPV: 

    • As a Decision is made, future cash flow streams are assumed fixed. There is no flexibility as soon as a decision has been made, and the project will be “passively” managed.
    • Cash-flow uncertainty is not considered, other than working with a risk-adjusted discount rate. The discount rate is often arbitrarily chosen (between 9%-25%) reflecting the analyst’s subjective perception of risk (and uncertainty) with the logic being the higher the discount rate the higher the anticipated risk (note: the applied rate should be reasonably consistent with Weighted Average Cost of Capital  and Capital Asset Pricing Model (CAPM)).
    • All risks are completely accounted for in the discount rate (i.e., which is naïve)
    • The discount rate remains constant over the life-time of the project (i.e., which is naïve).
    • There is no consideration of the value of flexibility, choices and different options.
    • Strategic value is rarely incorporated into the analysis. It is well known that many important benefits are difficult (but not impossible) to value in a quantifiable sense, such as intangible assets or strategic positions. If a strategy cannot be valued or quantified it should not be pursued.
    • Different project outcomes and the associated expected NPVs are rarely considered.
    • Cash-flows and investments are discounted with a single discount rate assuming that market risk and private (company) risk is identical. Correct accounting should use the risk-free rate for private risk and cash-flows subject to market risks should make use of market risk-adjusted discount rate.

    In the following several valuation methodologies will be introduced, which build upon and extend the conventional discounted cash flow and net-present value analysis, providing more powerful means for decision and strategic thinking.

     

    TRADITIONAL VALUATION

    The net-present value is defined as the difference between the values assigned to a given asset, the cash-flows, and the cost and capital expenditures of operating the asset. The traditional valuation approach is based on the net-present value (NPV) formulation [6]

    NPV = \sum\limits_{t = 0}^T {\frac{{{C_t}}}{{{{\left( {1 + {r_{ram}}} \right)}^t}}}}  - \sum\limits_{t = 0}^T {\frac{{{I_t}}}{{{{\left( {1 + {r_{rap}}} \right)}^t}}}}  \approx \sum\limits_{t = 0}^T {\frac{{{C_t} - {I_t}}}{{{{\left( {1 + r*} \right)}^t}}}}  = \sum\limits_{t = 1}^T {\frac{{C_t^*}}{{{{\left( {1 + r*} \right)}^t}}}}  - {I_0}clip_image002

    T is the period during which the valuation is considered, Ct is the future cash flow at time t, rram is the risk-adjusted discount rate applied to market-related risk, It is the investment cost at time t, and rrap is the risk-adjusted-discount rate applied to private-related risk. In most analysis it is customary to assume the same discount rate for private as well as market risk as it simplifies the valuation analysis. The “effective” discount rate r* is often arbitrarily chosen. The I0 is the initial investment at time t=0, and Ct* = Ct – It (for t>0) is the difference between future cash flows and investment costs. The approximation (i.e., ≈ sign) only holds in the limit where the rate rrap is close to rram. The private risk-adjusted rate is expected to be lower than the market risk-adjusted rate. Therefore, any future investments and operating costs will weight more than the future cash flows. Eventually value will be destroyed unless value growth can be achieved. It is therefore important to manage incurred cost, and at the same time explore growth aggressively (at minimum cost) over the project period. Assuming a risk-adjusted or effective rate for both market and private risk investment, cost and cash-flows could lead to a even serious over-estimation of a given project’s value. In general, the private risk-adjusted rate rrap would be between the risk-free rate and the market risk-adjusted discount rate rram.

     example1

    EXAMPLE 1: An initial network investment of 20 mio euro needs to be committed to provide a new service for the customer base. It is assumed that sustenance investment per year amounts to 2% of the initial investment and that operations & maintenance is 20% of the accumulated investment (50% in initial year). Other network cost, such as transmission (assumes centralized platform solution) increases with 10% per year due to increased traffic with an initial cost of 150 thousand. The total network investment and cost structure should be discounted according with the risk-free rate (assumed to be 5%). Market assumptions: s-curve consistent growth assumed with a saturation of 5 Million service users after approximately 3 years. It has been assumed that the user pays 0.8 euro per month for the service and that the service price decreases with 10% per year. Cost of acquisition assumed to be 1 euro per customer, increasing with 5% per year. Other market dependent cost assumed initially to be 400 thousand and to increase with 10% per year. It is assumed that the project is terminated after 5 years and that the terminal value amounts to 0 euro. PV stands for present value and FV for future value. The PV has been discounted back to year 0. It can be seen from the table that the project breaks-even after 3 years. The first analysis presents the NPV results (over a 5 year period) when differentiating between private (private risk-adjusted rate) and market (market risk-adjusted rate) risk taking, a positive NPV of 26M is found. This should be compared with the standard approach assuming an effective rate of 12.5%, which (not surprisingly) results in a positive NPV of 46M. The difference between the two approaches amounts to about 19M.

    .

    Example above compares the approach of using an effective discount rate r* with an analysis that differentiates between private rrap and market risk rram in the NPV calculation. The example illustrates a project valuation example of introducing a new service. The introduction results in network investments and costs in order to provide and operate the service.  Future cash-flows arise from growth of customer base (i.e., service users), and is offset by market related costs. All network investments and costs are assumed to be subject to private risk and should be discounted with the risk-free rate. The market-related cost and revenues are subject to market risk and the risk-adjusted rate should be used [7]. Alternatively, all investment, costs and revenues can be treated with an effective discount rate. As seen from the example, the difference between the two valuation approaches can be substantial:

    • NPV = €26M for differentiated market and private risk, and
    • NPV = €46M using an effective discount rate (e.g., difference of €20M assuming the following discount rates rram = 20%, rrap =5%, r* = 12.5%). Obviously, as rram –> r* and rrap –> r* , the difference in the two valuation approaches will tend to zero. 

     

    UNCERTAINTY, RISK & VALUATION

    The traditional valuation methodology presented in the previous section makes no attempt to incorporate uncertainties and risk other than the effective discount-rate r* or risk-adjusted rates rram/rap. It is inherent in the analysis that cash-flows, as well as the future investments and cost structure, are assumed to be certain. The first level of incorporating uncertainty into the investment analysis would be to define market scenarios with an estimated (subjective) chance of occurring. A good introduction to uncertainty and risk modeling is provided in the well-written book by D. Vose [8], S.O. Sugiyama’s training notes [3] and S. Beninga’s “Financial Modeling” [7].

     

    The Business Analyst working on the service introduction, presented in Example 1, assesses that there are 3 main NPV outcomes for the business model; NPV1= 45, NPV2= 20 and NPV3= -30.  The outcomes have been based on 3 different market assumptions related to customer uptake: 1. Optimistic, 2. Base and 3. Pessimistic. The NPVs are associated with the following chances of occurrence: P1 = 25%, P2 = 50% and P3 = 25%.

     

    What would the expected net-present value be given the above scenarios?

     

    The expected NPV (ENPV) would be ENPV=P1×NPV1+ P2×NPV2+ P3×NPV3=25%×45+50%×20+25%×(-30) =14. Example 2 (below) illustrates the process of obtaining the expected NPV.

    example2

    Example 2: illustrates how to calculate the expected NPV (ENPV) when 3 NPV outcomes have been identified resulting from 3 different customer uptake scenarios. The expected NPV calculation assumes that we do not have any flexibility to avoid any of the 3 outcomes. The circular node represents a chance node yielding the expected outcome given the weighted NPVs.

     

    In general the expected NPV can be written as

    ENPV = \sum\limits_{i = 1}^N {NP{V_i} \times {P_i}}

    ,where N is number of possible NPV outcomes, NPVi is the net present value of the ith outcome and Pi is the chance that the ith outcome will occur.  By including scenarios in the valuation analysis, the uncertainty of the real world is being captured. The risk of overestimating or underestimating a project valuation is thereby minimized. Typically, the estimation of P, which is the chance or probability, for a particular outcome is based on subjective “feeling” of the Business Analyst, who obviously still need to build a credible story around his choices of likelihood for the scenarios in questions. Clearly this is not a very satisfactory situation as all kind of heuristic biases are likely to influence the choice of a given scenarios likelihood. Still it is clearly more realistic than a purely deterministic approach with only one locked-in happening.

     example3

    Example 3 shows various market outcomes used to study the uncertainty of market conditions upon the net-present value of Example 1and the project valuation subject these uncertainties. The curve represented by the thick solid line and open squares is the base market scenario used in Example 1, while the other curves represent variations to the base case.  Various uncertainties of the customer growth have been explored. An s-curve (logistic function) approach has been used to model the customer uptake of for the studied service: S(t) = \frac{{{S_{\max }}}}{{1 + b\,Exp( - a\,t)}}Exp[ - c\,max\left\{ {0,\left. {t - {t_d}} \right\}} \right.], t is time period, Smax is the maximum expected number of customer, be determines the slope in the growth phase, and (1/a) is the years to reach the mid-point of the S-curve. The Exp[ - c\;\max \{ 0,t - {t_d}\} ]function models the possible decline in customer base, with c being the rate of decline in the market share, and td the period when the decline sets in. Smax has been varied between 2.5 and 6.25 Million customers, with an average of 5.0 Million, b was chosen to be 50 (arbitrarily), (1/a) was varied between 1/3 and 2 (year), with a mean of 0.5 (year). In modeling the market decline, the rate of decline c was varied between 0% and 25% years, with a chosen mean value of 10%, and the td was varied between 0 and 3 years with a mean of 2 years before market decline starts. In all cases a so-called pert distribution was used to model the parameter variance. Instead of running a limited number of scenarios as shown in Example 2 (3 outcomes), a Monte Carlo (MC) simulation is carried out sampling several thousands of possible outcomes.

     

    As already discussed a valuation analysis often involves many uncertain variables and assumptions. In the above Example 3 different NPV scenarios had been identified, which resulted from studying the customer uptake. Typically, the identified uncertain input variables in a simplified scenario-sensitivity approach would each have at least three possible values; minimum (x), base-line or most-likely (y), and maximum (z). For every uncertain input variable the Analyst has identified a {\left\{ {{x_i},{y_i},{z_i}} \right\}_i} variation, i.e., 3 possible variations. For an analysis with 2 uncertain input variables, each with {\left\{ {{x_i},{y_i},{z_i}} \right\}_i}variation, it is not difficult to show that the outcome is 9 different scenario-combinations, for 3 uncertain input variables the result is 72 scenario-combinations, 4 uncertain input variables results in 479 different scenario permutations, and so forth. In complex models containing 10 or more uncertain input variables, the number of combinations would have exceeded 30 Million permutations [9]. Clearly, if 1 or 2 uncertain input variables have been identified in a model the above presented scenario-sensitivity approach is practical. However, the range of possibilities quickly becomes very large and the simple analysis breaks down. In these situations the Business Analyst should turn to Monte Carlo [10] simulations, where a great number of outcomes and combinations can be sampled in a probabilistic manner and enables proper statistical analysis. Before the Analyst can perform an actual Monte Carlo simulation, a probability density function (pdf) needs to be assigned to each identified uncertain input variable and any correlation between model variables needs to be addressed. It should be emphasized that with the help of subject-matter experts, an experienced Analyst in most cases can identify the proper pdf to use for each uncertain input variable. A tool such as Palisade Corporation’s @RISK toolbox [2] for MS Excel visualizes, supports and greatly simplifies the process of including uncertainty into a deterministic model, and efficiently performs Monte Carlo simulations in Microsoft Excel.

     

    Rather than guessing a given scenarios likelihood, it is preferable to transform the deterministic scenarios into one probabilistic scenario. Substituting important scalars (or drivers) with best practice probability distributions and introduce logical switches that mimic choices or options inherent in different driver outcomes. Statistical sampling across simulated outcomes will provide an effective (or blended) real option value.

     

    In Example 1a standard deterministic valuation analysis was performed for a new service and the corresponding network investments. The inherent assumption was that all future cash-flows as well as cost-structures were known. The analysis yielded a 5-year NPV of 26 mio (using the market-private discount rates). This can be regarded as a pure deterministic outcome. The Business Analyst is requested by Management to study the impact on the project valuation incorporating uncertainties into the business model. Thus, the deterministic business model should be translated into a probabilistic model. It is quickly identified that the market assumptions, the customer intake, is an area which needs more analysis. Example 3shows various possible market outcomes. The reference market model is represented by the thick-solid line and open squares. The market outcome is linked to the business model (cash-flows, cost and net-present value). The deterministic model in Example 1 has now been transformed into a probabilistic model including market uncertainty.

    example4

    Example 4: shows the impact of uncertainty in the marketing forecast of customer growth on the Net Present Value (extending Example 1). A Monte Carlo (MC) simulation was carried out subject to the variations of the market conditions (framed box with MC in right side) described above (Example 2) and the NPV results were sampled. As can be seen in the figure above an expected mean NPV of 22M was found with a standard deviation of 16M. Further, analysis reveals a 10% probability of loss (i.e., NPV £ 0 euro) and an opportunity of up to 46M. Charts below (Example 4b and 4c) show the NPV probability density function and integral (probability), respectively. 

    Example 4b                                                                        Example 4c

    example4bexample4c

    Example 4 above summarizes the result of carrying out a Monte Carlo (MC) simulation, using @RISK [2], determining the risks and opportunities of the proposed service and therefore obtaining a better foundation for decision making. In the previous examples the net-present value was represented as a single number; €26M in Example 1 and an expect NPV of €14M in Example 2. In Example 4, the NPV is far richer (see the probability charts of NPV at the bottom of the page) – first note that the mean NPV of €22M agree well with Example 1. Moreover, the Monte Carlo analysis shows the project down-side, that there is a 10% chance of ending up with a poor investment, resulting in value destruction. The opportunity or upside is a chance (i.e., 5%) of gaining more than €46M within a 5-year time-horizon. The project risk profile is represented with the NPV standard deviation, i.e. the project volatility, of €16M. It is Management’s responsibility to weight the risk, downside as well as upside, and ensure that proper mitigation will be considered to reduce the impact of the project downside and potential value destruction.

     

    The presented valuation methodologies so far do not consider flexibility in decision making. Once an investment decision has been taken investment management is assumed to be passive. Thus, should a project turn out to destroy value, which is inevitable if revenue growth becomes limited compared to the operating cost, Management is assumed not to terminate or abandon this project. In reality active Investment Management and Management Decision Making does consider options and their economical and strategic value. In the following a detailed discussion on the valuation of options and the impact on decision making are presented. The Real options analysis (ROA) will be introduced as a natural extension of probabilistic cash flow and net present value analysis. It should be emphasized that ROA is based on some advanced mathematical, as well as statistical concepts, which will not be addressed in this work.

    However, it is possible to get started on ROA with proper re-arrangement of the conventional valuation analysis, as well as incorporating uncertainty where ever appropriate. In the following the goal is to get the reader introduced to thinking about the value of options.

     

    REAL OPTIONS & VALUATION

    An investment option can be seen as a decision flexibility, which depending upon uncertain conditions, might be realized. It should be emphasized, that as with a financial option, it is at the investor’s discretion to realize an option. Any cost or investment for the option itself can be viewed as the premium a company has to pay in order to obtain the option. For example, a company could be looking at an initial technology investment, with the option later on to expand should market conditions be favorable for value growth. Exercising the option, or making the decision to expand the capacity, results in a commitment of additional cost and capital investments – the “strike price” – into realizing the plan/option. Once the option to expand has been exercised, the expected revenue stream becomes the additional value subject to private and market risks. In every technology decision a decision-maker is faced with various options and would need to consider the ever-prevalent uncertainty and risk of real-world decisions.

     

    In the following example, a multinational company is valuing a new service with the idea to commercially launch in all its operations. The cash-flows, associated with the service, are regarded as highly uncertain, and involve significant upfront development cost and investments in infrastructure to support the service. The company studying the service is faced with several options for the initial investment as well as future development of the service. Firstly, the company needs to make the decision to launch the service in all countries in which it is based, or to start-up in one or a few countries to test the service idea before committing to a full international deployment, investing in transport and service capacity. The company also needs to evaluate the architectural options in terms of platform centralization versus de-centralization, platform supplier harmonization or commit to a more-than-one-supplier strategy. In the following, options will be discussed in relation to the service deployment as well as the platform deployment, which supports the new service. In the first instance the Marketing strategy defines a base-line scenario in which the service is launched in all its operations at the same time. The base-line architectural choice is represented by a centralized platform scenario placed in one country, providing the service and initial capacity to the whole group.

    .

    Platform centralization provides for an efficient investment and resourcing; instead of several national platform implementation projects only one country focuses its resources. However, the operating costs might be higher due to need for international leased transmission connectivity to the centralized platform. Due to the uncertainty in the assumed cash-flows, arising from market uncertainties, the following strategy has been identified; The service will be launched initially in a limited number of operations (one or two) with the option to expand should the service be successful (option 1), or should the service fail to generate revenue and growth potential an option to abandon the service after 2 years (option 2). The valuation of the identified options should be assessed in comparison with the base-line scenario of launching the service in all operations. It is clear that the expansion option (option 1) leads to a range of options in terms of platform expansion strategies depending on the traffic volume and cost of the leased international transmission (carrying the traffic) to the centralized platform.

     

    For example, if the cost of transmission exceeds the cost of operating the service platform locally an option to locally deploy the service platform is created. From this example it can be seen that by breaking up the investment decisions into strategic options the company has ensured that it can abandon should the service fail to generate the expected revenue or cash-flows, reducing loses and destruction of wealth. However, more importantly the company, while protecting itself from the downside, has left open the option to expand at the cost of the initial investment. It is evident that as the new service has been launched and cash-flows start being generated (or lack of appropriate cash-flows) the company gains more certainty and better grounds for making decisions on which strategic options should be exercised.

     

    In the previous example, an investment and its associated valuation could be related to the choices which come naturally out of the collection of uncertainties and the resulting risk. In the literature (e.g., [11], [12]) it has been shown that conventional cash-flow analysis, which omits option valuation, tends to under-estimate the project value [13]. The additional project value results from identifying inherent options and valuing these options separately as strategic choices that can be made in a given time-horizon relevant to the project. The consideration of the value of options in the physical world closely relates to financial options theory and treatment of financial securities [14]. The financial options analysis relates to the valuation of derivatives [15] depending on financial assets, whereas the analysis described above identifying options related to physical or real assets, such as investment in tangible projects, is defined as real options analysis (ROA). Real options analysis is a fairly new development in project valuation (see [16], [17], [18], [19], [20], and [21]), and has been adopted to gain a better understanding of the value of flexibility of choice.

     

    One of the most important ideas about options in general and real options in particular, is that uncertainty widens the range of potential outcomes. By proper mitigation and contingency strategy the downside of uncertainty can be significantly reduced, leaving the upside potential. Uncertainty, often feared by Management, can be very valuable, provided the right level of mitigation is exercised. In our industry most committed investments involve a high degree of uncertainty, in particular concerning market forces and revenue expectations, but also technology-related uncertainty and risk is not negligible. The value of an option, or strategic choice, arises from the uncertainty and related risk that real-world projects will be facing during their life-time. The uncertain world, as well as project complexity, results in a portfolio of options, or choice-path, a company can choose from. It has been shown that such options can add significant value to a project – however, presently options are often ignored or valued incorrectly [1121]. In projects, which are inherently uncertain, the Analyst would look for project-valuable options such as, for example:

    1. Defer/Delay – wait and see strategy (call option)
    2. Future growth/ Expand/Extend – resource and capacity expansion (call option)
    3. Replacement – technology obsolescence/end-of-life issues (call option)
    4. Introduction of new technology, service and/or product (call option)
    5. Contraction – capacity decommissioning (put option)
    6. Terminate/abandon – poor cash-flow contribution or market obsolescence (put option)
    7. Switching options – dynamic/real-time decision flexibility (call/put option)
    8. Compound options – phased and sequential investment (call/put option)

    It is instructive to consider a number of examples of options/flexibilities which are representative for the mobile telecommunications industry. Real options or options on physical assets can be divided in to two basic types – calls and puts. A call option gives, the holder of the option, the right to buy an asset, and a put option provides the holder with the right to sell the underlying asset.

     

    First, the call option will be illustrated with a few examples: One of the most important options open to management is the option to Defer or Delay (1) a project. This is a call option, right to buy, on the value of the project. The defer/delay option will be addressed at length later in this paper. The choice to Expand (2) is an option to invest in additional capacity and increase the offered output if conditions are favorable. This is defined as a call option, i.e., the right to buy or invest, on the value of the additional capacity that could enable extra customers, minutes-of-use, and of course additional revenue. The exercise price of the call option is the investment and additional cost of providing the additional capacity discounted to the time of the option exercise. A good example is the expansion of a mobile switching infrastructure to accommodate an increase in the customer base. Another example of expansion could be moving from platform centralization to de-centralization as traffic grows and the cost of centralization becomes higher than the cost of decentralizing a platform. For example, the cost of transporting traffic to a centralized platform location could, depending on cost-structure and traffic volume, become un-economical. Moreover, Management is often faced with the option to extend the life of an asset by re-investing in renewal – this choice is a so-called Replacement Option (3). This is a call option, the right to re-invest, on the assets future value. An example could be the renewal of the GSM base-transceiver stations (BTS), which would extend the life and adding additional revenue streams in the form of options to offer new services and products not possible on the older equipment. Furthermore, there might be additional value in reducing operational cost of old equipment, which typically would have higher running cost, than with new equipment. Terminate/Abandonment (5) in a project is an option to either sell or terminate a project. It is a so-called put option, i.e., it gives the holder the right to sell, on the projects value. The strike price would be the termination value of the project reduced by any closing-down costs.  This option mitigates the impact of a poor investment outcome and increases the valuation of the project. A concrete example could be the option to terminate poorly revenue generating services or products, or abandon a technology where the operating costs results in value destruction. The growth in cash-flows cannot compensate the operating costs. Contraction choices  (6) are options to reduce the scale of a project’s operation. This is a put option, right to “sell”, on the value of the lost capacity. The exercise price is the present value of future cost and investments saved as seen at the time of exercising the option. In reality most real investment projects can be broken up in several phases and therefore also will consist of several options and the proper investment and decision strategy will depend on the combination these options. Phased or sequential investment strategies often include Compounded Options (8), which are a series of options arising sequentially.

     

    The radio access network site-rollout investment strategy is a good example of how compounded options analysis could be applied. The site rollout process can be broken out in (at least) 4 phases: 1. Site identification, 2. Site acquisition, 3. Site preparation (site build/civil work), and finally 4. Equipment installation, commissioning and network integration. Phase 2 depends on phase 1, phase 3 depends on phase 2, and phase 4 depends on phase 3 – a sequence of investment decisions depending on the previous decision, thus the anatomy of the real options is that of Compound Options (8) . Assuming that a given site location has been identified and acquired (call option on the site lease), which is typically the time-consuming and difficult part of the overall rollout process; the option to prepare the site emerges (Phase 3). This option, also a call option, could depend on the market expectations and the competitions strategy, local regulations and site-lease contract clauses. The flexibility arises from deferring/delaying the decision to commit investment to site preparation. The decision or option time-horizon for this deferral/delay option is typically set by the lease contract and its conditions. If the option expires the lease costs have been lost, but the value arises from not investing in a project that would result in negative cash-flow.  As market conditions for the rollout technology becomes more certain, higher confidence in revenue prospects, a decision to move to site preparation (Phase 3) can be made. In terms of investment management after Phase 3 has been completed there is little reason not to pursue Phase 4 and install and integrate the equipment enabling service coverage around the site location. If at the point of Phase 3 the technology or supplier choice still remains uncertain it might be a valuable option to await (deferral/delay option) a decision on supplier and/or technology to be deployed. In the site-rollout example described other options can be identified, such as abandon/terminate option on the lease contract (i.e., a put option). After Phase 4 has been completed there might come a day where an option to replace the existing equipment with new and more efficient / economical equipment arises.  It might even be interesting to consider the option value of terminating the site altogether and de-install the equipment. This could happen when operating costs exceeds the cash-flow. It should be noted that the termination option is quite dramatic with respect to site-rollout as this decision would disrupt network coverage and could aggress existing customers. However, the option to replace the older technology and maybe un-economical services with a new and more economical technology-service option might prove valuable. Most options are driven by various sources of uncertainty. In the site-rollout example, uncertainty might be found with respect to site-lease cost, time-to-secure-site, inflation (impacting the site-build cost), competition, site supply and demand, market uncertainties, and so forth

     

    Going back to Example 1 and Example 4, the platform subject-matter expert (often different from the Analyst) has identified that if the customer base exceeds 4 Million customers and expansion of €10M will be needed. Thus, the previous examples underestimate the potential investments in platform expansion due to customer growth. Given that the base-line market scenario does identify that that this would be the case in the 2nd year of the project the €10M is included in the deterministic conventional business case for the new service. The result of including the €10M in the 2nd year of Example 1 is that the NPV drops from €26M to €8.7M (∆NPV minus €17.6M). Obviously, the conventional Analyst would stop here and still be satisfied that this seems to be a good and solid business case. The approach of Example 4 is applied to the new situation, subject to the same market uncertainty given in Example 3. From the Monte Carlo simulation, it is found that the NPV mean-value only is €4.7M. However, the downside is that the probability of loss (i.e., an NPV less than 0) now is 38%. It is important to realize that in both examples is the assumption that there is no choice or flexibility concerning the €10M investment; the investment will be committed in year two. However, the project has an option – the option to expand provided that the customer base exceeds 4 Million customers. Time wise it is a flexible option in the sense that if the project expected lifetime is 5 years, any time within this time-horizon is there a possibility that the customer base exceeds the critical mass for platform expansion.

    example5

    Example 5: Shows the NPV valuation outcome when an option to expand is included in the model of Example 4. The €10M  is added if and only if the customer base exceeds 4 Million.

    In the above Example 5  the probabilistic model has been changed to add €10M if and only if the customer base exceeds 4 Million. Basically, the option of expansion is being simulated. Treating the expansion as an option is clearly valuable for the business case, as the NPV mean-value has increased from €4.7M to €7.6M. In principle the option value could be taken to €2.9M. It is worthwhile noticing that the probability of loss (from 38% to 25%) has also been reduced by allowing for the option not to expand the platform if the customer base target is not achieved. It should be noted that although the example does illustrate the idea of options and flexibility it is not completely in line with a proper real options analysis.

    example6

    Example 6 Shows the different valuation outcomes depending on whether the €10M platform expansion (when customer base exceeds 4 Million) is considered as un-avoidable (i.e., the “Deterministic No Option” and “Probabilistic No Option”) or as an option or choice to do so (“Probabilistic with Option”). It should be noted that the additional €3M in difference between “Probabilistic No Option” and “Probabilistic With Option” can be regarded as an effective option value, but it does not necessarily agree with a proper real-option valuation analysis of the option to expand. Another difference in the two probabilistic models is that in the model with option to expand an expansion can happen any year if customer base exceeds 4 Million, while the No option model only considers the expansion in year 2 where according with the marketing forecast the base exceeds the 4 Million. Note that Example 6 is different in assumptions than Example 1 and Example 4 as these do not include the additional €10M.

     

    Example 6 above summarizes the three different approaches of valuation analysis; deterministic (essential 1-dimensional), probabilistic with options, and probabilistic including value options.

    The investment analysis of real options as presented in this paper is not a revolution but rather an evolution of the conventional cash-flow and NPV analysis. The approach to valuation is first to understand and proper model the base-line case. After the conventional analysis has been carried out, the analyst, together with subject-matter experts, should determine areas of uncertainty by identifying the most relevant uncertain input parameters and their variation-ranges. As described in the previous section the deterministic business model is being transformed into a probabilistic model. The valuation range, or NPV probability distribution, is obtained by Monte Carlo simulations and the opportunity and risk profile is analyzed. The NPV opportunity-risk profile will identify the need for mitigation strategies, which in itself result in studying the various options inherent in the project. The next step in the valuation analysis is to value the identified project or real options. The qualitatively importance of considering real options in investment decisions has been provided in this paper. It has been shown that conventional investment analysis, represented by net-present value and discounted cash-flow analysis, gives only one side of the valuation analysis. As uncertainty is the “farther” of opportunity and risk it needs to be considered in the valuation process. Are identified options always valuable? The answer to that question is no – if we have certainty about an option movement is not in our favor then the option would be valuable. Think for example of considering a growth option at the onset of severe recession.

     

    The real options analysis is often presented as being difficult and too mathematical; in particular

    due to the involvement of the partial differential equations (PDE) that describes the underlying uncertainty (continuous-time stochastic processes, involvement of Markov processes, diffusion processes, and so forth). Studying PDEs are the basis for the ground-breaking work of the Black-Scholes-Merton [22] [23] on option pricing, which provided the financial community with an analytical expression for valuing financial options. However, “heavy” mathematical analysis is not really needed for getting started on real option.

     

    Real options are a way of thinking, identifying valuable options in a project or potential investment that could create even more value by considering as an option instead of a deterministic given.

     

    Furthermore, Cox et al [24] proposed a simplified algebraic approach, which involves so-called binominal trees representing price, cash-flow, or value movements in time. The binomial approach is very easy to understand and implement, resembling standard decision tree analysis, and visually easy to generate, as well as algebraically straightforward to solve.

     

    SUMMARY

    Real options are everywhere where uncertainty governs investment decisions. It should be clear that uncertainty can be turned into a great advantage for value growth providing proper contingencies are taken for reducing the downside of uncertainty – mitigating risk.  Very few investment decisions are static, as conventional discounted cash-flow analysis otherwise might indicate, but are ever changing due to changes in market conditions (global as well as local), technologies, cultural trends, etc. In order to continue to create wealth and value for the company value growth is needed and should force a dynamic investment management process that continuously looks at the existing as well as future valuable options available for the industry. It is compelling to say that a company’s value should be related to its real-options portfolio, and its track record in mitigating risk, and achieving the uncertain up-side of opportunities.

     

    ACKNOWEDGEMENT

    I am indebted to Sam Sugiyama (President & Founder of EC Risk USA & Europe) for taking time out from a very busy schedule and having a detailed look at the content of our paper. His insights and hard questions have greatly enriched this work. Moreover, I would also like to thank Maurice Ketel (Manager Network Economics), Jim Burke (who in 2006 was Head of T-Mobile Technology Office) and Norbert Matthes (who in 2007 was Head of Network Economics T-Mobile Deutschland) for their interest and very valuable comments and suggestions.

    ___________________________

    APPENDIX – MATHEMATICS OF VALUE.

    Firstly we note that the Future Value FV (of money) can be defined as the present Value PV (of money) times a relative increase given by an effective rate r* (i.e., that represents the change of money value between time periods), reflecting value increase or of course decrease over a cause of time t;

    F{V_t} = {(1 + r*)^t}\;PV clip_image004 

    So the Present Value given we know the Future Value would be

    PV = \frac{{F{V_t}}}{{{{(1 + r*)}^t}}}

    For a sequence (or series) of future money flow we can write the present value as 

    PV = \sum\limits_{t = 1}^N {\frac{{F{V_t}}}{{{{(1 + r*)}^t}}}}

    If r* is positive time-value-of-money follows naturally, i.e., money received in the future is worth less than today. It is a fundamental assumption that you can create more value with your money today than waiting to get them in the future (i.e., not per se right for majority of human beings but maybe for Homo Economicus).

    First the sequence of future money value (discounted to the present) has the structure of a geometric series: {V_n} = \sum\limits_{k = 0}^n {\frac{{{y_k}}}{{{{\left( {1 + r} \right)}^k}}}} , with yk+1 = g*yk (i.e., g* representing the change in y between two periods k and k+1).

    Define {a_k} = \frac{{{y_k}}}{{{{\left( {1 + r} \right)}^k}}}and note that\frac{{{a_{k + 1}}}}{{{a_k}}} = \frac{{g*}}{{1 + r}} = \frac{{1 + g}}{{1 + r}} = s, thus in this framework we have that{V_n} = \sum\limits_{k = 0}^n {{s^k}} (note: I am doing all kind of “naughty” simplifications to not get too much trouble with the math).

    The following relation is easy to realize:

    \begin{array}{l} {V_n} = 1 + s + {s^2} + {s^3} + .......... + {s^n}\\ s{V_n} = s + {s^2} + {s^3} + .......... + {s^n} + {s^{n + 1}} \end{array}, subtract the two equations from each other and the result is(1 - s){V_n} = (1 - {s^{n + 1}})\quad  \Leftrightarrow \quad {V_n} = \frac{{1 - {s^{n + 1}}}}{{1 - s}}\quad  \Leftrightarrow \quad {V_n} = \frac{{1 + r}}{{r - g}} - \frac{{(1 + g)}}{{r - g}}{\left( {\frac{{1 + g}}{{1 + r}}} \right)^n}

    . In the limit where n goes toward infinity (¥), providing that\left| s \right| < 1\quad  \Leftrightarrow \quad \left| {\frac{{1 + g}}{{1 + r}}} \right| < 1, it can be seen that .

    It is often forgotten that this only is correct if and only if \left| {1 + g} \right| < \left| {1 + r} \right| or in other words, if the discount rate (to present value) is higher than the future value growth rate.{V_\infty } = \frac{1}{{1 - s}}\quad  \Leftrightarrow \quad {V_\infty } = \frac{{1 + r}}{{r - g}}

    You might often hear you finance folks (or M&A jockeys) talk about Terminal Value (they might also call it continuation value or horizon value … for many years I called it Termination Value … though that’s of course slightly out of synch with Homo Financius not to be mistaken for Homo Economicus :-).

    PV = \sum\limits_{t = 1}^T {\frac{{FV{}_t}}{{{{(1 + r*)}^t}}}}  + T{V_{T \to \infty }} = NP{V_T} + \sum\limits_{t = T + 1}^\infty  {\frac{{FV{}_t}}{{{{(1 + r*)}^t}}}} with TV representing the Terminal Value and

    NPV representing the net present value as calculated over a well-defined time span T.

     

    I always found the Terminal Value fascinating as the size (matters?) or relative magnitude can be very substantial and frequently far greater than the NPV in terms of “value contribution” to the present value. Of course we do assume that our business model will survive to “Kingdom Come”. Appears to be a slightly optimistic assumptions (n’est pas mes amis? :-). We also assume that everything in the future is defined by the last year of cash-flow, the cash flow growth rate and our discount rate (hmmm don’t say that Homo Financius isn’t optimistic). Mathematically this is all okay (if \left| {1 + g} \right| < \left| {1 + r} \right|), economically maybe not so. I have had many and intense debates with past finance colleagues about the validity of Terminal Value. However, to date it remains a fairly standard practice to joggle up the enterprise value of a business model with a “bit” of Terminal Value.

    Using the above (i.e., including our somewhat “naughty” simplifications)

    TV = \sum\limits_{t = T + 1}^\infty  {\frac{{{y_t}}}{{{{(1 + r)}^t}}}}

    TV = \frac{{(1 + g)\,{y_T}}}{{{{(1 + r)}^{T + 1}}}}\sum\limits_{j = 0}^\infty  {\frac{{{{(1 + g)}^j}}}{{{{(1 + r)}^j}}}}

    TV \approx \frac{{(1 + g)\,{y_T}}}{{(r - g)\,{{(1 + r)}^T}}}\quad \forall \,\left| {1 + g} \right| < \left| {1 + r} \right|

    It is easy to see why TV can be a very substantial contribution to the total value of a business model. The denominator (r-g) tends to be a lot smaller than 1 (i.e., note that always we have g<r) and though “blows” up the TV contribution to the present value (even when g is chosen to be zero).

    Let’s evaluate the impact on uncertainty of the interest rate x, first re-write the NPV formula:

    NP{V_n} = {V_n} = \sum\limits_{k = 0}^n {\frac{{{y_k}}}{{{{\left( {1 + x} \right)}^k}}}} , yk is the cash-flow of time k (for the moment it remains unspecified), from

    error/uncertainty propagation it is known that the standard deviation can be written as\Delta {z^2} = {\left( {\frac{{\partial f}}{{\partial x}}} \right)^2}\Delta {x^2} + {\left( {\frac{{\partial f}}{{\partial y}}} \right)^2}\Delta {y^2} + ...., where z=f(x,y,z,…) is a multi-variate function. Identifying the terms in the NPV formula is easy: z = Vn and f(x,\left\{ {{y_k}} \right\};k) = \sum\limits_k {\frac{{{y_k}}}{{{{\left( {1 + x} \right)}^k}}}}

    In the first approximation assume that x is the uncertain parameter, while yk is certain (i.e., ∆yk=0), then the following holds for the NPV standard deviation:

    {\left( {\Delta {V_n}} \right)^2} = {\left( {\sum\limits_{k = 0}^n {\frac{{k{y_k}}}{{{{\left( {1 + x} \right)}^{k + 1}}}}} } \right)^2}{\left( {\Delta x} \right)^2}\quad  \Leftrightarrow \Delta {V_n} = \left| {\Delta x} \right|\left| {\sum\limits_{k = 0}^n {\frac{{k{y_k}}}{{{{(1 + x)}^{k + 1}}}}} } \right|,

    in the special case where yk is constant for all k’s,. It can be shown (similar analysis as above) that

    \Delta {V_n} = \left| {\Delta x} \right|\left| {{y_n}} \right|\left| {\frac{{1 - {r^{n + 1}}}}{{{{(1 - r)}^2}}} - \frac{{1 + n\,{r^{n + 1}}}}{{(1 - r)}}} \right| with r = \frac{1}{{1 + x}}.

    In the limit where n goes toward infinity, applying l’Hospital’s rule showing that n\,{r^{n + 1}} \to 0\;for\;n \to \infty , the following holds for propagating uncertainty/errors in the NPV formula:

    \Delta {V_\infty } = \left| {\Delta x} \right|\,\left| y \right|\;\left| {\frac{1}{{{{\left( {1 - r} \right)}^2}}} - \frac{1}{{(1 - r)}}} \right| = \left| {\Delta x} \right|\,\left| y \right|\;\left| {\frac{{ - r}}{{{{(1 - r)}^2}}}} \right| = \left| {\Delta x} \right|\,\left| y \right|\;\left| {\frac{{1 + x}}{{{x^2}}}} \right|

    Let’s take a numerical example, y=1, the interest rate x = 10% and the uncertainty/error is assumed to be no more than ∆x=3% (7%£ x £13%), assume that n®¥ (infinite time-horizon). Using the formula derived above NPV¥=11 and ∆NPV¥=±3.30 or a 30% error on estimated NPV. If the assumed cash-flows (i.e., yk) also uncertain the error will even be greater than 30%. The above analysis becomes more complex when yk is non-constant over time k and as yk to should be regarded as uncertain. The use of for example Microsoft Excel becomes rather useful to gain further insight (although the math is pretty fun too).


    [1] This is likely due to the widespread use of MS Excel and financial pocket calculators allowing for easy NPV calculations, without the necessity for the user to understand the underlying mathematics, treating the formula as “black” box calculation. Note a common mistake using MS Excel NPV function is to include initial investment (t=0) in the formula – this is wrong the NPV formula starts with t=1. Thus, initial investment would be discounted which would lead to an overestimation of value.

    [2] http://www.palisade-europe.com/. For purchases contact Palisade Sales & Training, The Blue House 30, Calvin Street, London E1 6NW, United Kingdom, Tel. +442074269955, Fax +442073751229.

    [3] Sugiyama, S.O., “Risk Assessment Training using The Decision Tools Suite 4.5 – A step-by-step Approach” and “Introduction to Advanced Applications for Decision Tools Suite – Training Notebook – A step-by-step Approach”, Palisade Corporation. The Training Course as well as the training material itself can be highly recommended.

    [4] Most people in general not schooled in probability theory, statistics and mathematical analysis. Great care should be taken to present matters in an intuitive rather than mathematical fashion.

    [5] Hill, A., “Corporate Finance”, Financial Times Pitman Publishing, London, 1998.

    [6]This result comes straight from geometric series calculus. Remember a geometric series is defined asclip_image024, where clip_image026 is constant. For the NPV geometric series it can easily be shown thatclip_image028, r being the interest rate. A very important property is that the series converge ifclip_image030, which is the case for the NPV formula when the interest rate r>1. The convergent series sums to a finite value of clip_image032 for k starting at 1 and summed up to ¥ (infinite).

    [7] Benninga, S., “Financial Modeling”, The MIT Press, Cambridge Massachusetts (2000), pp.27 – 52. Chapter 2 describes procedures for calculating cost of capital. This book is the true practitioners guide to financial modeling in MS Excel.

    [8] Vose, D., “Risk Analysis A Quantitative Guide”, (2nd edition), Wiley, New York, 2000. A very competent book on risk modeling with a lot of examples and insight into competent/correct use of probability distribution functions.

    [9] The number of scenario combinations are calculated as follows: an uncertain input variable can be characterized by the following possibility setclip_image034with length s, in case of k uncertain input variables the number of combinations can be calculated as clip_image036, where clip_image038is the COMBIN function of Microsoft Excel.

    [10] A Monte Carlo simulation refers to the traditional method of sampling random (stochastic) variables in modeling. Samples are chosen completely randomly across the range of the distribution. For highly skewed or long-tailed distributions a large numbers of samples are needed for convergence. The @Risk product from Palisade Corporation (see http://www.palisade.com) supplies the perfect tool-box (Excel add-in) for converting a deterministic business model (or any other model) into a probabilistic one.

    [11] Luehrman, T.A., “Investment Opportunities as Real Options: Getting Started with the Numbers”, Harvard Business Review, (July – August 1998), p.p. 3-15.

    [12] Luehrman, T.A., “Strategy as a Portfolio of Real Options”, Harvard Business Review, (September-October 1998), p.p. 89-99.

    [13] Providing that the business assumptions where not inflated to make the case positive in the first place.

    [14] Hull, J.C., “Options, Futures, and Other Derivatives”, 5th Edition, Prentice Hall, New Jersey, 2003. This is a wonderful book, which provides the basic and advanced material for understanding options.

    [15] A derivative is a financial instrument whose price depends on, or is derived from, the price of another asset.

    [16] Boer, F.P., “The Valuation of Technology Business and Financial Issues in R&D”, Wiley, New York, 1999.

    [17]  Amram, M., and Kulatilaka, N., “Real Options Managing Strategic Investment in an Uncertain World”, Harvard Business School Press, Boston, 1999. Non-mathematical, provides a lot of good insight into real options and qualitative analysis.

    [18] Copeland, T., and V. Antikarov, “Real Options: A Practitioners Guide”, Texere, New York, 2001. While the book provides a lot of insight into the area of practical implementation of Real Options, great care should be taken with the examples in this book. Most of the examples are full of numerical mistakes. Working out the examples and correcting the mistakes provides a great mean of obtaining practical experience.

    [19] Munn, J.C., “Real Options Analysis”, Wiley, New York, 2002.

    [20] Amram. M., “Value Sweep Mapping Corporate Growth Opportunities”, Harvard Business School Press, Boston, 2002.

    [21] Boer, F.P., “The Real Options Solution Finding Total Value in a High-Risk World”, Wiley, New York, 2002.

    [22]] Black, F., and Scholes, M., “The Pricing of Options and Corporate Liabilities”, Journal of Political Economy, 81 (May/June 1973), pp. 637-659.

    [23] Merton, J.C., “Theory of Rational Option Pricing”, Bell Journal of Economics and Management Science, 4 (Spring 1973), 141-183.

    [24] Cox, J.C., Ross, S.A., and Rubinstein, M., “Option Pricing: A Simplified Approach”, Journal of Financial Economics, 7 (October 1979) pp. 229-63.

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    GSM – Gone So Much … or is it?

    • A Billion GSM subscriptions & almost $200 Billion GSM revenue will have gone within the next 5 years.
    • GSM earns a lot less than its “fair” share of the top-line, a trend that will further worsened going forward.
    • GSM revenue are fading out rapidly across a majority of the mobile markets across the Globe.
    • Accelerated GSM phase-out happens when pricing level of the next technology option relative to the GDP per capita drops below 2%.
    • 220 MHz of great spectrum is tied up in GSM, just waiting to be liberated.
    • GSM is horrific spectral in-efficient in comparison to today’s cellular standards.
    • Eventually we will have 1 GSM network across a given market, shared by all operators, supporting fringe legacy devices (e.g., M2M) while allowing operators to re-purpose remaining legacy GSM spectrum.
    • The single Shared-GSM network might survive past any economical justification for its existence merely serving legal and political interests.

    Gone So Much … GSM is ancient, uncool and so 90s … why would anybody bother with that stuff any longer … its synonymous  with the Nokia Handset (which btw is also ancient, uncool and so 90s … and almost no longer among us thanks to our friend Elop …). In many emerging markets GSM-only phones are hardly demanded or sold any longer in the grey markets. Grey market that make up 90% (or more) of  handset sales in many of those emerging markets. Moreover, its not only AT&T in the US talking about 2G phase-out but also an emerging market such as Thailand is believed to be void of GSM within the next couple of years.

    bananaphone

    A bit of Personal History. Some years ago I had the privilege to work with some very smart people in the Telecom Industry on merging two very big mobile operations (ca.140 million in combined customer base). One of our cardinal spectrum strategic and technology arguments were the gain in spectral efficiency such a merger would bring. Anecdotally it is worth mentioning that the technology synergies and spectrum strategic ideas largely would have financed the deal in shear synergies.

    In discussions with the country’s regulator we were asked why we could not “just” switch off GSM? Then use that freed GSM spectrum for new cellular technologies, such as UMTS and even LTE. Thereby gaining sufficient spectral efficiency that merging the two business would become un-necessary. The proposal would have effectively turned off the button of a service that served at ca. 70 Million GSM-only (incl, EDGE & GPRS) subscribers (at the time) across the country. Now that would have been expensive and most likely caused a couple or thousands of class action suits to the beat.

    Here is how one could have thought about the process of clearing out GSM for something better (though overall its is more for richer and poorer). There is no “just …press the off button”, as also Sprint experienced with their iDEN migration.

    customer migration

    Our thoughts (and submitted Declarations) were that by merging the two operators spectrum (and sites pool) we could create sufficient spectral capacity to support both GSM (which we all granted was phasing out) and provide more capacity and customer experience for the Now Generation Technology (i.e., HSPA+ or 4G as they like to call it in that particular market … Heretics! ;-). A recent must read GigaOM blog by Keith Fitchard  “AT&T begins cannibalizing 2G and 3G networks to boost LTE capacity” describes very well the aggressive no-nonsense thinking of US carriers (or simply desperation or both) when it comes to the quest for spectrum efficiency and enhanced customer experience (which co-incidentally also yields the best ARPUs).

    It is worth mentioning that more than 2×110 Mega Hertz is tied up in GSM, Up-to 2×35 MHz at 900MHz (if E-GSM has been evoked) and 2×75 MHz at 1800MHz (yes! I am ignoring US GSM band plans, they are messed up but pretty fun nevertheless … different story for another time). Being able to re-purpose this amount of spectrum to more spectral efficient cellular technologies (e.g., UMTS Voice, HSPA+ and LTE) would clearly leapfrog mobile broadband, increase voice capacity at increased quality, and serve the current billions of GSM-only users as well as the next billion un-connected or under-server customer segments with The Internet. The macro-economical benefits being very substantial.

    220 MHz of great spectrum is tied up in GSM, just waiting to be liberated.

    Back in the days of 2003 I did my first detailed GSM phase-out techno-economical analysis (a bit premature one might add). I was very interested in questions such as “when can we switched off GSM?”, “what are the economical premises of exiting GSM?”, “Why do operators today still continue to encourage subscriber growth on their GSM networks?”, “Today … if you got your hands on GSM usable spectrum, would you start a GSM operation?”, “Why?” and “Why not?”, etc..

    So why don’t we “just” switch off GSM? and let go of that old in-efficient cellular technology?

    How in-efficient? you may ask? … Pending a little bit on what state the GSM is in, we can have ca. 3 times more voice users in WCDMA (i.e., UMTS) compared to GSM with Adaptive Multi-Rate (AMR) codec support. Newer technology releases supports even more dramatic leaps in voice handling capabilities.

    voice efficiency GSM vs wcdma

    Data? what about cellular data? That GSM, including its data handling enhancements GPRS and EDGE, is light-bits away from the data handling capabilities of WCDMA, HSPA+, LTE and so forth is at this point a well establish fact.

    Clearly GSM is horrific spectral in-efficient in comparison to later cellular standards such as WCDMA, HSPA(+) and LTE(+) and its only light (in a very dark tunnel) is that it is supported at lower frequencies (i.e., more economical deployment in rural areas and for large surface area countries). Though today that no longer unique as UMTS and LTE are available in similar or even lower frequency ranges. … of course there are other economical issues at plays as well, which we will see below.

    Why do we still bother with a 27+ year old technology? a technology that has very poor spectral efficiency in comparison with later cellular technologies. GSM after all “only” provides Voice, SMS and pretty low bandwidth mobile data (while better than nothing, still very close to nothing).

    Well for one thing! there is of course the money thing? (and we know that that makes the world go around) ca. 4+ Billion GSM subscriptions worldwide (incl. GPRS & EDGE) generating a total GSM turnover of 280+ Billion US$.

    In 2017 we anticipate to have a little less than 3 Billion GSM subscriptions generating ca. 100+ Billion US$. So ….a Billion GSM subscriptions and almost 200 Billion US$ GSM revenue will have dis-appeared within the next 5 years (and for the sake of mobile operators hopefully replaced by something better).

    In this trend APAC, takes its lion share of the GSM subscription loss with ca. 65% (ca, 800 Million) of the total loss and ca. 50% of the GSM top-line loss (ca. 100 Billion US$).

    The share of GSM revenue is rapidly declining across (almost) all markets;

    gsm revenue share

    The GSM revenue as share of the total revenue (as well as in absolute terms) rapidly diminishes, as 3G and LTE are introduced and customer migrate to those more modern technologies.

    If the should be any doubts GSM does not get its fair share revenue compared to its share of the subscriptions (or subscribers for that matter):

    2012 GSM RS vs MS

    While the above data does contain two main clusters, it still pretty well illustrates (what should be no real surprise to any one) that GSM earns back a lot less than its “fair” share (whatever that really means). And again if anyone would be in doubt that picture will be grimmer as the we fast forward to the near future;

    2017 GSM RS vs MS

    Grim, Grimmer, Grimmest!

    Today GSM earns a lot less than its “fair” share of the top-line, this trend will be further worsened going forward.

    So we can soon phase-out GSM? Right? hmmmm! Maybe not so fast!

    Well while GSM revenue has certainly declined and expected to continue the decline, in many markets the GSM-only (e.g., here defined as a customers that only have GSM Voice, GPRS and/or EDGE available) customers have not declined in proportion to the related revenue might fool us to believe.

    gsm market share

    The above statistics illustrates the GSM-only subscription share of the total cellular business.

    There is more to GSM than market and revenue share … and we do need to have a look at the actual decline of GSM subscriptions (or unique users which is not per se the same) and revenue;GSM_actual_decline

    The GSM revenue are expected to massively free fall over the next 5 years!

    However, also observe (in the chart above) that we need to sustain the network and its associated cost as a considerable amount of customers remain on the network, despite generating a lot less top line.

    As we have already seen above, in the next 5 years there will be many markets where GSM subscription and subscriber share will remain reasonable strong albeit the technology’s ability to turn-over revenue will be in free-fall in most markets.

    Analyzing data from Pyramid Research (actual & projection for the period 2013 to 2017), including other analyst data sets (particular on actual data), extrapolating the data beyond 2017 by diffusion models approximating the dynamics of technology migration in the various market, we can get an idea about the remaining (residual) life of GSM. In other words we can make GSM phase-out projections as well as get a feel for the terminal revenue (or residual value) left in GSM. Further get an appreciation of how that terminal value compares to the total mobile turnover over the same GSM phase-out period.

    The chart below provide the results of such a comprehensive analysis. The colored bars illustrate the various years of onset of GSM phase-out; (a) the earliest year which is equal to the lower end of light-blue bar is typically the year where migration off GSM accelerates, (b) the upper end of the light-blue bar is a most-likely year where after GSM no longer would be profitable, and (c) the upper end of the red bar illustrates the maximum expected life of GSM. It should be noted that the GSM Phase-out chart below might not be shown in its entirety (particular right side of the chart). Clicking on the Chart itself will display it in full.

    gsm phase-out projections

    Taking the above GSM phase-out years, we can get a feeling for how many useful years GSM has left in terms of economical-life and customer life-time defined as which event comes first of (i) less than 1 Million GSM subscriptions or (ii) 5% GSM market-share. 2014 has been taken as the reference year;

    remaining usefull life of GSM

    It should be noted that the Useful Life-span of GSM chart above might not be shown in its entirety (particular right side of the chart). Clicking on the Chart itself will display it in full.

    AREAS #MARKETS GSM –
    REMAINING LIFE
    Western Europe               16       4.1 +/- 3.3 years
    Asia Pacific               13       6.4 +/- 5.0 years
    Middle East & Africa               17     11.0 +/- 6.2 years
    Central Eastern Europe                 8       6.9 +/- 4.8 years
    Latin America               19       6.6 +/- 3.7 years

    That Western Europe (and US which has not been shown here) has the most aggressive time-lines for GSM phase-out should come as no surprise. The 3G/UMTS has been deployed there the longest and the 3G price level to GDP has come down to a level where there is hardly any barrier for most mobile users to switch from GSM to UMTS. Also the WEU region has the most extensive UMTS coverage which also removes the GSM to UMTS switching barrier. Central Eastern Europe average is pulled up (i.e., longer useful life) substantial by Russia and Ukraine that shows fairly extreme laggardness in GSM phase-out (in comparison with the other CEE markets). For Middle East and Africa it should be noted that there are two very strong clusters of data distinguishing the Gulf States from the African Countries. Most of the Gulf States have only a very few years of remaining useful life of GSM. In general the GSM remaining life trend can be described fairly well with the amount of time UMTS has been in a given market (i.e., though smartphone introduction did kick-start the migration from GSM more than anything else), the extend of UMTS coverage (i.e., degree of pop and geo coverage) and the basic economics of UMTS.

    In my analysis I have assumed 4 major triggers for GSM phase-out;

    1. Analysis shows that once the 3G (or non-2G) ARPU is below 2% of the nominal GDP per capita an acceleration of migration away from GSM speeds up. I have (somewhat arbitrarily) chosen 1% as my limit where there is no longer any essential barrier of customer migrating off GSM.
    2. When GSM penetration is below 5% as a decision point for converting (by possible subsidies) GSM customers to a more modern and efficient technology. This obviously does depend on total customer base and the local economical framework and as such is only a heuristics rather than a universal rule.
    3. Last but not least, my 3rd criteria for phasing out GSM is when its base is below 1 million subscriptions (i.e., typically 500 to 800 thousand subscribers).
    4. Last but not least, before complete phase-out of GSM can commence, operators obviously need to provide the alternative technology (e.g., UMTS or LTE) coverage that can replace the existing GSM coverage. This is in general only economical if comparable frequency range can be used and thus for example for UMTS coverage replacement of GSM in many cases re-farming/re-purposing 900MHz from GSM to UMTS. This last point can be a very substantial bottleneck and show stopper for migration from GSM to UMTS, particular in rural areas or in countries with very substantial rural populations on GSM.

    Interestingly enough, extensive data analysis on more than 70 markets, shows that the GSM phase—out dynamics appears to have little or no dependency on (a) the 2G ARPU level, (b) 2G ARPU level relative to 3G ARPU and (c) handset pricing (although I should point out that I have not had a lot of data here to be firm in this conclusion, in particular reliable data for grey market handset pricing across the emerging markets is a challenge).

    One of the important trigger points for onset of accelerated GSM phase-out is the pricing level of the next technology (e.g., 3G) option relative to the GDP per capita.

    Migration decision appears less to do with the legacy price of the old technology or old technology price relative to new technology pricing.

    gsm market share vs 3G arpu to gdp

    Above chart illustrates an analysis made on 2012 actual data for more than 70+ markets all across WEU, CEE, APAC, EMEA and LA (i.e., coinciding with markets covered by Pyramid Research). It is very interesting to observe the dynamics as the markets develop into the future and the data moves towards the left indicating more affordable 3G pricing (relative to GDP per capita) and increasingly faster GSM phase-out as is evident from the chart below providing the same markets as above but fast forwarded 5 years (i.e., 2017).

    5yrs add gsm market share vs 3G arpu to gdp

    Firstly the GSM ARPU level across most markets is below 2% of a given markets GDP per capita. There is no clear evidence in the country data available that the GSM ARPU development has had any effect on slowing down or accelerating GSM phase-out. Most likely an indication that GSM has reached (or will reach shortly) a cost level where customers become insensitive.

    gsm market share vs 2G arpu to gdp

    Conceptually we can visualize the GSM phase-out dynamics in the following way were as the 3G gets increasingly affordable (which may or should include the device cost depending on taste), GSM phase-out accelerates (i.e., moving from right to left in the illustrative chart below). While the chart illustration below is more attuned to emerging market migration dynamics of GSM phase-out it can of course with minor adaptations be used for other more balanced prepaid-postpaid markets.

    We should keep in mind that unless the mobile operators new technology coverage (e.g., UMTS, LTE, ..) at the very least overlap the GSM coverage, the migration from GSM to UMTS (or LTE) will eventually stop. This can in countries with a substantial rural population in particular become a blocking stone for an effective 100% migration. Resulting in large areas and population share that will remain underserved (i.e., only GSM available) and thus depend on an in-efficient and ancient technology without the macro-economical benefits (i.e., boost of rural GDP) new and far more efficient cellular technologies could bring.

    share of gsm and 3G affordability

    That’s all fine … what a surprise that customers wants better when it gets affordable (like to have wanted that even more when it was not affordable)… and that affordability is relative is hardly a surprising either.

    In order for an operator to make an informed opinion about when to switch off GSM, it would need to evaluated the remaining business opportunity, or residual GSM value, against the value for re-purposing the GSM spectrum to a better technology, i.e., with a superior customer experience potential, and with a substantial higher ARPU utilization.

    Counting from 2014, the remaining life-time aka terminal aka residual GSM revenue will be in the order of 850 Billion US$ … agreeable an apparently dramatic number … however, the residual GSM revenue is on average no more than 5% of total cellular turnover and for many countries a lower than that. Actual 45 markets out of the 73 studied will have a terminal GSM revenue lower than 5%.

    terminal gsm revenue share histogram

    The chart below provides an overview of the Residual GSM Revenues in Billion of US$ (on a logarithmic scale) and the percentage of Residual GSM value out of the total cellular turnover (linear scale) for 75 top markets spread across Western Europe, Central Eastern Europe, Asia Pacific, Middle East & Africa, and Latin America.

    gsm terminal revenue & share

    Do note that the GSM Terminal Revenue chart above might not be shown in its entirety (right side of the chart). Clicking on the Chart itself will display it in full.

    It is quiet clear from the above chart that, apart from a few outliers, GSM revenue are fading out rapidly across a majority of the mobile markets across the globe. Even if the residual GSM topline might appear tempting, it obviously need to be compared to the operating expenses for sustaining the legacy technology as well as considering that a more modern technology would create higher efficiency (and possible ARPU arbitrage) and therefor mitigate margin decline sustaining more traffic and customers.

    Emerging APAC MNO Example: an emerging market in APAC has 100 Million subscriptions and ca. 70 Million unique cellular user base.One of the Mobile Network Operators (MNO) in this market has approx. 33% market share (revenue share slightly larger). in 2012 its EBITDA margin was 42%. Technology cost share of overall Opex is 25% and for the sake of simplicity the corresponding GSM cost share is in 2012 assumed to be 50% of the Total Technology Opex. As the business evolves it is assumed that the GSM cost base grows slower than non-GSM technology cost elements. This particular market has a residual GSM revenue potential of approx. 4 Billion US$ and the MNO under the loop has 1.3 Billion US$ remaining GSM revenue potential.

    Our analysis shows that the GSM business would start to breakdown (within the assumed economical framework or template) at around 5 Million GSM subscriptions or 3.5 Million unique users. This would happen around 2019 (+/- 2 years, with a bias towards earlier years) and thus leave the business with another 3 to 5 years of likely profitable GSM operation. See the chart below.

    mno gsm phase-out example

    This illustration shows (not surprisingly) that there is a point where even if the phasing-out GSM turns-over revenue, from an economical perspective it makes no sense for a single mobile operator to keep its GSM network alive for a diminishing customer base and even faster evaporating top-line.

    In the example above it is clear that the MNO should start planning for the inevitable – the demise of GSM. Having a clear GSM phase-out strategy as soon as possible and targeting GSM termination no later than 2018 to 2019 just makes pretty good sense. Looking at risks to the dynamics of the market development in this particular market there is a higher likelihood of no-profit being reached earlier rather than later.

    Would it make sense to startup a new GSM business in the market above? Given the 3 to 5 years that the existing mobile operators have to meet retire GSM before it becomes un-profitable, it hardly make much sense for a Greenfield operator to get started on the GSM idea (seem to be better ways for spending cash).

    However, if that Greenfield operator could become The GSM Operator for all existing MNO players in the market, allowing those legacy MNOs to re-purpose their existing GSM spectrum (and possible with a retro-active wholesale deal), then maybe in the short term it might make a little sense. However, it quiet frankly would be like peeing in your trousers on a cold winter day, it will be warm for a short while but then it really gets cold (as my Grandmother used to say).

    What GSM strategies makes really sense in its autumn days?

    Quit clearly GSM Network Sharing would make a lot of sense economically and operationally as it would allow re-purposing of legacy spectrum to more modern and substantially more efficient cellular technologies.

    The single Shared-GSM network would act as a bridge for legacy GSM M2M devices, extreme laggards and problematic coverage areas that might not be economical to replace in the shorter – medium term. Thus mobile operators could then solve possible long-term contractual obligations to businesses and consumers having fringe devices connecting with GSM (i.e., metering, alarms, etc..). The single Shared-GSM network might very well survive for a considerable time past any economical justification for its existence merely serving legal and political interests. Thanks to Stein Erik Paulsen who pointed this problem out for GSM phase-out.

    I am not (too) hanged up about the general Capex & Opex benefits of Network Sharing in this context (yet another story for another day). The compelling logical step of having 1 (ONE) GSM network across a given market, shared by all operators, supporting the phase-out of GSM while allowing to re-purpose legacy GSM spectrum for UMTS/HSPA and eventually  LTE(+), is almost screamingly obvious. This furthermore would feed a faster migration pace and phase-out as legacy spectrum would be available for re-purposing and customer migration.

    Of course Regulatory authorities would need to endorse such a scenario as it de-facto would result in a smelling-like creating a monopolistic GSM operator albeit serving all in a given market.

    The Regulatory Authority should obviously be very interested in this strategy as it would ensure substantial better utilization  of scarce spectral resources.  Furthermore, not only gaining in spectral efficiency but also winning the macro-economical boost from connecting the unconnected and under-served population groups to mobile data networks, and by that, the internet.

    ACKNOWLEDGEMENT

    I have made extensive use of historical and actual data from Pyramid Research country data bases. Wherever possible this data has been cross checked with other sources. In my opinion Pyramid Research have some of the best and most detailed mobile technology projections that would satisfy even the most data savvy analysts. The very extensive data analysis on Pyramid Research data sets are my own and any short falls in the analysis clearly should only be attributed to myself.

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    SMS – Assimilation is inevitable, Resistance is Futile!

    Short Message Service or SMS for short, one of the corner stones of mobile services, just turned 20 years old in 2012.

    Talk about “Live Fast, Die Young” and the chances are that you are talking about SMS!

    The demise of SMS has already been heralded … Mobile operators rightfully are shedding tears of the (taken-for-granted?) decline of the most profitable 140 Bytes there ever was and possible ever will be.

    Before we completely kill off SMS, let’s have a brief look at

    SMS2012

    The average SMS user (across the world) consumed 136 SMS (ca. 19kByte) per month and paid 4.6 US$-cent per SMS and 2.6 US$ per month. Of course this is a worldwide average and should not be over interpreted. For example in the Philippines an average SMS user consumes 650+ SMS per month pays 0.258 US$-cent per SMS or 1.17 $ per month.The other extreme end of the SMS usage distribution we find in Cameroon with 4.6 SMS per month paying 8.19 US$-cent per SMS.

    We have all seen the headlines throughout 2012 (and better part of 2011) of SMS Dying, SMS Disaster, SMS usage dropping and revenues being annihilated by OTT applications offering messaging for free, etcetcetc… & blablabla … “Mobile Operators almost clueless and definitely blameless of the SMS challenges” … Right? … hmmmm maybe not so fast!

    All major market regions (i.e., WEU, CEE, NA, MEA, APAC, LA) have experienced a substantial slow down of SMS revenues in 2011 and 2012. A trend that is expected to continue and accelerate with mobile operators push for mobile broadband. Last but not least SMS volumes have slowed down as well (though less severe than the revenue slow down) as signalling-based short messaging service assimilates to IP-based messaging via mobile applications.

    Irrespective of all the drama! SMS phase-out is obvious (and has been for many years) … with the introduction of LTE, SMS will be retired.

    Resistance is (as the Borg’s would say) Futile!

    It should be clear that the phase out of SMS does Absolutely Not mean that messaging is dead or in decline. Far far from it!

    Messaging is Stronger than Ever and just got so many more communication channels beyond the signalling network of our legacy 2G & 3G networks.

    Its however important to understand how long the assimilation of SMS will take and what drivers impact the speed of the SMS assimilation. From an operator strategic perspective such considerations will provide insights into how quickly they will need to replace SMS Legacy Revenues with proportional Data Revenues or suffer increasingly on both Top and Bottom line.

    SMS2012 AND ITS GROWTH DYNAMICS

    So lets just have a look at the numbers (with the cautionary note that some care needs to be taken with exchange rate effects between US Dollar and Local Currencies across the various markets being wrapped up in a regional and a world view. Further, due to the structure of bundling propositions, product-based revenues such as SMS Revenues, can be and often are somewhat uncertain depending on the sophistication of a given market):

    2012 is expected worldwide to deliver more than 100 billion US Dollars in SMS revenues on more than 7 trillion revenue generating SMS.

    The 100 Billion US Dollars is ca. 10% of total worldwide mobile turnover. This is not much different from the 3 years prior and 1+ percentage-point up compared to 2008. Data revenues excluding SMS is expected in 2012 to be beyond 350 Billion US Dollar or 3.5 times that of SMS Revenues or 30+% of total worldwide mobile turnover (5 years ago this was 20% and ca. 2+ times SMS Revenues).

    SMS growth has slowed down over the last 5 years. Last 5 years SMS revenues CAGR was ca. 7% (worldwide). Between 2011 and 2012 SMS revenue growth is expected to be no more than 3%. Western Europe and Central Eastern Europe are both expected to generate less SMS revenues in 2012 than in 2011. SMS Volume grew with more than 20% per annum the last 5 years but generated SMS in 2012 is not expected to more than 10% higher than 2012.

    For the ones who like to compare SMS to Data Consumption (and please safe us from ludicrous claims of the benefits of satellites and other ideas out of too many visits to Dutch Coffee shops)

    2012 SMS Volume corresponds to 2.7 Terra Byte of daily data (not a lot! Really it is not!)

    Don’t be terrible exited about this number! It is like Nano-Dust compared to the total mobile data volume generated worldwide.

    The monthly Byte equivalent of SMS consumption is no more than 20 kilo Byte per individual mobile user in Western Europe.

    Let us have a look at how this distributes across the world broken down in Western Europe (WEU), Central Eastern Europe (CEE), North America (NA), Asia Pacific (APAC), Latin America (LA) and Middle East & Africa (MEA):

    sms_revenues_2012 sms_volume_2012

    From the above chart we see that

    Western Europe takes almost 30% of total worldwide SMS revenues but its share of total SMS generated is less than 10%.

    And to some extend also explains why Western Europe might be more exposed to SMS phase out than some other markets. We have already seen the evidence of Western Europe sensitivity to SMS revenues back in 2011, a trend that will spread in many more markets in 2012 and lead to an overall negative SMS revenue story of Western Europe in 2012. We will see that within some of the other regions there are countries that substantially more exposed to SMS phase-out than others in terms of SMS share of total mobile turnover.

    sms_pricing sms_per_individual

    In Western Europe a consumer would  for an SMS pay more than 7 times the price compared to a consumer in North America (i.e., Canada or USA). It is quiet clear that Western Europe has been very successful in charging for SMS compared to any other market in the World. An consumers have gladly paid the price (well I assume so;-).

    SMS Revenues in Western Europe are proportionally much more important in Western Europe than in other regions (maybe with the exception of Latin America).

    In 2012 17% of Total Western Europe Mobile Turnover is expected to come from SMS Revenues (was ca. 13% in 2008).

    WHAT DRIVES SMS GROWTH?

    It is interesting to ask what drives SMS behaviour across various markets and countries.

    Prior to reasonable good quality 3G networks and as importantly prior to the emergence of the Smartphone the SMS usage dynamics between different markets could easily be explained by relative few drivers, such as

    (1) Price decline year on year (the higher decline the faster does SMS per user grow, though rate and impact will depend on Smartphone penetration & 3G quality of coverage).

    (2) Price of an SMS relative to the price of a Minute (the lower the more SMS per User, in many countries there is a clear arbitrage in sending an SMS versus making a call which on average last between 60 – 120 seconds).

    (3) Prepaid to Contract ratios (higher prepaid ratios tend to result in fewer SMS, though this relationship is not per se very strong).

    (4) SMS ARPU to GDP (or average income if available) (The lower the higher higher the usage tend to be).

    (5) 2G penetration/adaptation and

    (6) literacy ratios (particular important in emerging markets. the lower the literacy rate is the lower the amount of SMS per user tend to be).

    Finer detailed models can be build with many more parameters. However, the 6 given here will provide a very decent worldview of SMS dynamics (i.e., amount and growth) across countries and cultures. So for mature markets we really talk about a time before 2009 – 2010 where Smartphone penetration started to approach or exceed 20% – 30% (beyond which the model becomes a bit more complex).

    In markets where the Smartphone penetration is beyond 30% and 3G networks has reached a certain coverage quality level the models describing SMS usage and growth changes to include Smartphone Penetration and to a lesser degree 3G Uptake (not Smartphone penetration and 3G uptake are not independent parameters and as such one or the other often suffice from a modelling perspective).

    Looking SMS usage and growth dynamics after 2008, I have found high quality statistical and descriptive models for SMS growth using the following parameters;

    (a) SMS Price Decline.

    (b) SMS price to MoU Price.

    (c) Prepaid percentage.

    (d) Smartphone penetration (Smartphone penetration has a negative impact on SMS growth and usage – unsurprisingly!)

    (e) SMS ARPU to GDP

    (f) 3G penetration/uptake (Higher the 3G penetration combined with very good coverage has a negative impact on SMS growth and usage. Less important though than Smartphone penetration).

    It should be noted that each of these parameters are varying with time and there for in extracting those from a comprehensive dataset time variation should be considered in order to produce a high quality descriptive model for SMS usage and growth.

    If a Market and its Mobile Operators would like to protect their SMS revenues or at least slow down the assimilation of SMS, the mobile operators clearly need to understand whether pushing Smartphones and Mobile Data can make up for the decline in SMS revenues that is bound to happen with the hard push of mobile broadband devices and services.

    EXPOSURE TO LOSS OF SMS REVENUE – A MARKET BY MARKET VIEW!

    As we have already seen and discussed it is not surprising that SMS is declining or stagnating. At least within its present form and business model. Mobile Broadband, the Smartphone and its many applications have created a multi-verse of alternatives to the SMS. Where in the past SMS was a clear convenience and often a much cheaper alternative to an equivalent voice call, today SMS has become in-convenient and not per se a cost-efficient alternative to Voice and certainly not when compared with IP-based messaging via a given data plan.

    exposure_to_SMS_decline

    74 countries (or markets) have been analysed for their exposure to SMS decline in terms of the share of SMS Revenues out of the Total Mobile Turnover. 4 categories have been identified (1) Very high risk >20%, (2) High risk for 10% – 20%, (3) Medium risk for 5% – 10% and (4) Lower risk when the SMS Revenues are below 5% of total mobile turnover.

    As Mobile operators push hard for mobile broadband and inevitably increases rapidly the Smartphone penetration, SMS will decline. In the “end-game” of LTE, SMS has been altogether phased out.

    Based on 2012 expectations lets look at the risk exposure that SMS phase-out brings in a market by market out-look;

    We see from the above analysis that 9 markets (out of a total 74 analyzed), with Philippines taking the pole position, are having what could be characterized as a very high exposure to SMS Decline. The UK market, with more than 30% of revenues tied up in SMS, have aggressively pushed for mobile broadband and LTE. It will be very interesting to follow how UK operators will mitigate the exposure to SMS decline as LTE is penetrating the market.  We will see whether LTE (and other mobile broadband propositions) can make up for the SMS decline.

    More than 40 markets have an SMS revenue dependency of more than 10% of total mobile turnover and thus do have a substantial exposure to SMS decline that needs to be mitigated by changes to the messaging business model.

    Mobile operators around the world still need to crack this SMS assimilation challenge … a good starting point would be to stop blaming OTT for all the evils and instead either manage their mobile broadband push and/or start changing their SMS business model to an IP-messaging business model.

    IS THERE A MARGIN EXPOSURE BEYOND LOSS OF SMS REVENUES?

    There is no doubt that SMS is a high-margin service, if not the highest, for The Mobile Industry.

    A small de-tour into the price for SMS and the comparison with the price of mobile data!

    The Basic: an SMS is 140 Bytes and max 160 characters.

    On average (worldwide) an SMS user pays (i.e., in 2012) ca. 4.615 US$-cent per short message.

    A Mega-Byte of data is equivalent to 7,490 SMSs which would have a “value” of ca. 345 US Dollars.

    Expensive?

    Yes! It would be if that was the price a user would pay for mobile broadband data (particular for average consumptions of 100 Mega Bytes per month of Smartphone consumption) …

    However, remember that an average user (worldwide) consumes no more than 20 kilo Byte per Month.

    One Mega-Byte of SMS would supposedly last for more than 50 month or more than 4 years.

    This is just to illustrate the silliness of getting into SMS value comparison with mobile data.

    A Byte is not just a Byte but depends what that Byte caries!

    Its quiet clear that an SMS equivalent IP-based messaging does not pose much of a challenge to a mobile broadband network being it either HSPA-based or LTE-based. To some extend IP-based messaging (as long as its equivalent to 140 Bytes) should be able to be delivered at better or similar margin as in a legacy based 2G mobile network.

    Thus, in my opinion a 140 Byte message should not cost more to deliver in an LTE or HSPA based network. In fact due to better spectral efficiency and at equivalent service levels, the cost of delivering 140 Bytes in LTE or HSPA should be a lot less than in GSM (or CS-3G).

    However, if the mobile operators are not able to adapt their messaging business models to recover the SMS revenues (which with the margin argument above might not be $ to $ recovery but could be less) at risk of being lost to the assimilation process of pushing mobile data … well then substantial margin decline will be experienced.

    Operators in the danger zone of SMS revenue exposure, and thus with the SMS revenue share exceeding 10% of the total mobile turnover, should urgently start strategizing on how they can control the SMS assimilation process without substantial financial loss to their operations.

    ACKNOWLEDGEMENT

    I have made extensive use of historical and actual data from Pyramid Research country data bases. Wherever possible this data has been cross checked with other sources. Pyramid Research have some of the best and most detailed mobile technology projections that would satisfy most data savvy analysts. The very extensive data analysis on Pyramid Research data sets are my own and any short falls in the analysis clearly should only be attributed to myself.

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    The Economics of the Thousand Times Challenge: Spectrum, Efficiency and Small Cells

    By now the biggest challenge of the “1,000x challenge” is to read yet another story about the “1,000x challenge”.

    This said, Qualcomm has made many beautiful presentations on The Challenge. It leaves the reader with an impression that it is much less of a real challenge, as there is a solution for everything and then some.

    So bear with me while we take a look at the Economics and in particular the Economical Boundaries around the Thousand Times “Challenge” of providing (1) More spectrum, (2) Better efficiency and last but not least (3) Many more Small Cells.

    THE MISSING LINK

    While (almost) every technical challenge is solvable by clever engineering (i.e., something Qualcomm obviously have in abundance), it is not following naturally that such solutions are also feasible within the economical framework imposed by real world economics. At the very least, any technical solution should also be reasonable within the world of economics (and of course within a practical time-frame) or it becomes a clever solution but irrelevant to a real world business.

    A  Business will (maybe should is more in line with reality) care about customer happiness. However a business needs to do that within healthy financial boundaries of margin, cash and shareholder value. Not only should the customer be happy, but the happiness should extend to investors and shareholders that have trusted the Business with their livelihood.

    While technically, and almost mathematically, it follows that massive network densification would be required in the next 10 years IF WE KEEP FEEDING CUSTOMER DEMAND it might not be very economical to do so or at the very least such densification only make sense within a reasonable financial envelope.

    Its obvious that massive network densification, by means of macro-cellular expansion, is unrealistic, impractically as well as uneconomically. Thus Small Cell concepts including WiFi has been brought to the Telecoms Scene as an alternative and credible solution. While Small Cells are much more practical, the question whether they addresses sufficiently the economical boundaries, the Telecommunications Industry is facing, remains pretty much unanswered.

    PRE-AMP

    The Thousand Times Challenge, as it has been PR’ed by Qualcomm, states that the cellular capacity required in 2020 will be at least 1,000 times that of “today”. Actually, the 1,000 times challenge is referenced to the cellular demand & supply in 2010, so doing the math

    the 1,000x might “only” be a 100 times challenge between now and 2020 in the world of Qualcomm’s and alike. Not that it matters! … We still talk about the same demand, just referenced to a later (and maybe less “sexy” year).

    In my previous Blogs, I have accounted for the dubious affair (and non-nonsensical discussion) of over-emphasizing cellular data growth rates (see “The Thousand Times Challenge: The answer to everything about mobile data”) as well as the much more intelligent discussion about how the Mobile Industry provides for more cellular data capacity starting with the existing mobile networks (see “The Thousand Time Challenge: How to provide cellular data capacity?”).

    As it turns out  Cellular Network Capacity C can be described by 3 major components; (1) available bandwidth B, (2) (effective) spectral efficiency E and (3) number of cells deployed N.

    The SUPPLIED NETWORK CAPACITY in Mbps (i.e., C) is equal to  the AMOUNT OF SPECTRUM, i.e., available bandwidth, in MHz (i..e, B) multiplied with the SPECTRAL EFFICIENCY PER CELL in Mbps/MHz (i.e., E) multiplied by the NUMBER OF CELLS (i.e., N). For more details on how and when to apply the Cellular Network Capacity Equation read my previous Blog on “How to provide Cellular Data Capacity?”).

    SK Telekom (SK Telekom’s presentation at the 3GPP workshop on “Future Radio in 3GPP” is worth a careful study) , Mallinson (@WiseHarbor) and Qualcomm (@Qualcomm_tech, and many others as of late) have used the above capacity equation to impose a Target amount of cellular network capacity a mobile network should be able to supply by 2020: Realistic or Not, this target comes to a 1,000 times the supplied capacity level in 2010 (i.e., I assume that 2010 – 2020 sounds nicer than 2012 – 2022 … although the later would have been a lot more logical to aim for if one really would like to look at 10 years … of course that might not give 1,000 times which might ruin the marketing message?).

    So we have the following 2020 Cellular Network Capacity Challenge:

    Thus a cellular network in 2020 should have 3 times more spectral bandwidth B available (that’s fairly easy!), 6 times higher spectral efficiency E (so so … but not impossible, particular compared with 2010) and 56 times higher cell site density N (this one might  be a “real killer challenge” in more than one way), compared to 2010!.

    Personally I would not get too hanged up about whether its 3 x 6 x 56 or 6 x 3 x 56 or some other “multiplicators” resulting in a 1,000 times gain (though some combinations might be a lot more feasible than others!)

    Obviously we do NOT need a lot of insights to see that the 1,000x challenge is a

    Rally call for Small & then Smaller Cell Deployment!

    Also we do not need to be particular visionary (or have visited a Dutch Coffee Shop) to predict that by 2020 (aka The Future) compared to today (i.e., October 2012)?

    Data demand from mobile devices will be a lot higher in 2020!

    Cellular Networks have to (and will!) supply a lot more data capacity in 2020!

    Footnote: the observant reader will have seen that I am not making the claim that there will be hugely more data traffic on the cellular network in comparison to today. The WiFi path might (and most likely will) take a lot of the traffic growth away from the cellular network.

    BUT

    how economical will this journey be for the Mobile Network Operator?

    THE ECONOMICS OF THE THOUSAND TIMES CHALLENGE

    Mobile Network Operators (MNOs) will not have the luxury of getting the Cellular Data Supply and Demand Equation Wrong.

    The MNO will need to balance network investments with pricing strategies, churn & customer experience management as well as overall profitability and corporate financial well being:

    Growth, if not manage, will lead to capacity & cash crunch and destruction of share holder value!

    So for the Thousand Times Challenge, we need to look at the Total Cost of Ownership (TCO) or Total Investment required to get to a cellular network with 1,000 times more network capacity than today. We need to look at:

    Investment I(B) in additional bandwidth B, which would include (a) the price of spectral re-farming (i.e., re-purposing legacy spectrum to a new and more efficient technology), (b) technology migration (e.g., moving customers off 2G and onto 3G or LTE or both) and (c) possible acquisition of new spectrum (i..e, via auction, beauty contests, or M&As).

    Improving a cellular networks spectral efficiency I(E) is also likely to result in additional investments. In order to get an improved effective spectral efficiency, an operator would be required to (a) modernize its infrastructure, (b) invest into better antenna technologies, and (c) ensure that customer migration from older spectral in-efficient technologies into more spectral efficient technologies occurs at an appropriate pace.

    Last but NOT Least the investment in cell density I(N):

    Needing 56 times additional cell density is most likely NOT going to be FREE,

    even with clever small cell deployment strategies.

    Though I am pretty sure that some will make a very positive business case, out there in the Operator space, (note: the difference between Pest & Cholera might come out in favor of Cholera … though we would rather avoid both of them) comparing a macro-cellular expansion to Small Cell deployment, avoiding massive churn in case of outrageous cell congestion, rather than focusing on managing growth before such an event would occur.

    The Real “1,000x” Challenge will be Economical in nature and will relate to the following considerations:

    tco 2020

    In other words:

    Mobile Networks required to supply a 1,000 times present day cellular capacity are also required to provide that capacity gain at substantially less ABSOLUTE Total Cost of Ownership.

    I emphasize the ABSOLUTE aspects of the Total Cost of Ownership (TCO), as I have too many times seen our Mobile Industry providing financial benefits in relative terms (i.e., relative to a given quality improvement) and then fail to mention that in absolute cost the industry will incur increased Opex (compared to pre-improvement situation). Thus a margin decline (i.e., unless proportional revenue is gained … and how likely is that?) as well as negative cash impact due to increased investments to gain the improvements (i.e., again assuming that proportional revenue gain remains wishful thinking).

    Never Trust relative financial improvements! Absolutes don’t Lie!

    THE ECONOMICS OF SPECTRUM.

    Spectrum economics can be captured by three major themes: (A) ACQUISITION, (B) RETENTION and (C) PERFECTION. These 3 major themes should be well considered in any credible business plan: Short, Medium and Long-term.

    It is fairly clear that there will not be a lot new lower frequency (defined here as <2.5GHz) spectrum available in the next 10+ years (unless we get a real breakthrough in white-space). The biggest relative increase in cellular bandwidth dedicated to mobile data services will come from re-purposing (i.e., perfecting) existing legacy spectrum (i.e., by re-farming). Acquisition of some new bandwidth in the low frequency range (<800MHz), which per definition will not be a lot of bandwidth and will take time to become available. There are opportunities in the very high frequency range (>3GHz) which contains a lot of bandwidth. However this is only interesting for Small Cell and Femto Cell like deployments (feeding frenzy for small cells!).

    As many European Countries re-auction existing legacy spectrum after the set expiration period (typical 10 -15 years), it is paramount for a mobile operator to retain as much as possible of its existing legacy spectrum. Not only is current traffic tied up in the legacy bands, but future growth of mobile data will critical depend on its availability. Retention of existing spectrum position should be a very important element of an Operators  business plan and strategy.

    Most real-world mobile network operators that I have looked at can expect by acquisition & perfection to gain between 3 to 8 times spectral bandwidth for cellular data compared to today’s situation.

    For example, a typical Western European MNO have

    1. Max. 2x10MHz @ 900MHz primarily used for GSM. Though some operators are having UMTS 900 in operation or plans to re-farm to UMTS pending regulatory approval.
    2. 2×20 MHz @ 1800MHz, though here the variation tend to be fairly large in the MNO spectrum landscape, i.e., between 2x30MHz down-to 2x5MHz. Today this is exclusively in use for GSM. This is going to be a key LTE band in Europe and already supported in iPhone 5 for LTE.
    3. 2×10 – 15 MHz @ 2100MHz is the main 3G-band (UMTS/HSPA+) in Europe and is expected to remain so for at least the next 10 years.
    4. 2×10 @ 800 MHz per operator and typically distributed across 3 operator and dedicated to LTE. In countries with more than 3 operators typically some MNOs will have no position in this band.
    5. 40 MHz @ 2.6 GHz per operator and dedicated to LTE (FDD and/or TDD). From a coverage perspective this spectrum would in general be earmarked for capacity enhancements rather than coverage.

    Note that most European mobile operators did not have 800MHz and/or 2.6GHz in their spectrum portfolios prior to 2011. The above list has been visualized in the Figure below (though only for FDD and showing the single side of the frequency duplex).

    spectrum_details

    The 700MHz will eventually become available in Europe (already in use for LTE in USA via AT&T and VRZ) for LTE advanced. Though the time frame for 700MHz cellular deployment in Europe is still expected take maybe up to 8 years (or more) to get it fully cleared and perfected.

    Today (as of 2012) a typical European MNO would have approximately (a) 60 MHz (i.e., DL+UL) for GSM, (b) 20 – 30 MHz for UMTS and (c) between 40MHz – 60MHz for LTE (note that in 2010 this would have been 0MHz for most operators!). By 2020 it would be fair to assume that same MNO could have (d) 40 – 50 MHz for UMTS/HSPA+ and (e) 80MHz – 100MHz for LTE. Of course it is likely that mobile operators still would have a thin GSM layer to support roaming traffic and extreme laggards (this is however likely to be a shared resource among several operators). If by 2020 10MHz to 20MHz would be required to support voice capacity, then the MNO would have at least 100MHz and up-to 130MHz for data.

    Note if we Fast-Backward to 2010, assume that no 2.6GHz or 800MHz auction had happened and that only 2×10 – 15 MHz @ 2.1GHz provided for cellular data capacity, then we easily get a factor 3 to 5 boost in spectral capacity for data over the period. This just to illustrate the meaningless of relativizing the challenge of providing network capacity.

    So what’s the economical aspects of spectrum? Well show me the money!

    Spectrum:

    1. needs to be Acquired (including re-acquired = Retention) via (a) Auction, (b) Beauty contest or (c) Private transaction if allowed by the regulatory authorities (i.e., spectrum trading); Usually spectrum (in Europe at least) will be time-limited right-to-use! (e.g., 10 – 15 years) => Capital investments to (re)purchase spectrum.
    2. might need to be Perfected & Re-farmed to another more spectral efficient technology => new infrastructure investments & customer migration cost (incl. acquisition, retention & churn).
    3. new deployment with coverage & service obligations => new capital investments and associated operational cost.
    4. demand could result in joint ventures or mergers to acquire sufficient spectrum for growth.
    5. often has a re-occurring usage fee associate with its deployment => Operational expense burden.

    First 3 bullet points can be attributed mainly to Capital expenditures and point 5. would typically be an Operational expense. As we have seen in US with the failed AT&T – T-Mobile US merger, bullet point 4. can result in very high cost of spectrum acquisition. Though usually a merger brings with it many beneficial synergies, other than spectrum, that justifies such a merger.

    spectrum_cost

    Above Figure provides a historical view on spectrum pricing in US$ per MHz-pop. As we can see, not all spectrum have been borne equal and depending on timing of acquisition, premium might have been paid for some spectrum (e.g., Western European UMTS hyper pricing of 2000 – 2001).

    Some general spectrum acquisition heuristics can be derived by above historical overview (see my presentation “Techno-Economical Aspects of Mobile Broadband from 800MHz to 2.6GHz” on @slideshare for more in depth analysis).

    spectrum_heuristics

    Most of the operator cost associated with Spectrum Acquisition, Spectrum Retention and Spectrum Perfection should be more or less included in a Mobile Network Operators Business Plans. Though the demand for more spectrum can be accelerated (1) in highly competitive markets, (2) spectrum starved operations, and/or (3) if customer demand is being poorly managed within the spectral resources available to the MNO.

    WiFi, or in general any open radio-access technology operating in ISM bands (i.e., freely available frequency bands such as 2.4GHz, 5.8GHz), can be a source of mitigating costly controlled-spectrum resources by stimulating higher usage of such open-technologies and open-bands.

    The cash prevention or cash optimization from open-access technologies and frequency bands should not be under-estimated or forgotten. Even if such open-access deployment models does not make standalone economical sense, is likely to make good sense to use as an integral part for the Next Generation Mobile Data Network perfecting & optimizing open- & controlled radio-access technologies.

    The Economics of Spectrum Acquisition, Spectrum Retention & Spectrum Perfection is of such tremendous benefits that it should be on any Operators business plans: short, medium and long-term.

    THE ECONOMICS OF SPECTRAL EFFICIENCY

    The relative gain in spectral efficiency (as well as other radio performance metrics) with new 3GPP releases has been amazing between R99 and recent HSDPA releases. Lots of progress have been booked on the account of increased receiver and antenna sophistication.

    spectral_efficiency_gain_per_technology

    If we compare HSDPA 3.6Mbps (see above Figure) with the first Release of LTE, the spectral efficiency has been improved with a factor 4. Combined with more available bandwidth for LTE, provides an even larger relative boost of supplied bandwidth for increased capacity and customer quality. Do note above relative representation of spectral efficiency gain largely takes away the usual (almost religious) discussions of what is the right spectral efficiency and at what load. The effective (what that may be in your network) spectral efficiency gain moving from one radio-access release or generation to the next would be represented by the above Figure.

    Theoretically this is all great! However,

    Having the radio-access infrastructure supporting the most spectral efficient technology is the easy part (i.e., thousands of radio nodes), getting your customer base migrated to the most spectral efficient technology is where the challenge starts (i.e., millions of devices).

    In other words, to get maximum benefits of a given 3GPP Release gains, an operator needs to migrate his customer-base terminal equipment to that more Efficient Release. This will take time and might be costly, particular if accelerated. Irrespective, migrating a customer base from radio-access A (e.g., GSM) to radio-access B (e.g., LTE), will take time and adhere to normal market dynamics of churn, retention, replacement factors, and gross-adds. The migration to a better radio-access technology can be stimulated by above-market-average acquisition & retention investments and higher-than-market-average terminal equipment subsidies. In the end competitors market reactions to your market actions, will influence the migration time scale very substantially (this is typically under-estimate as competitive driving forces are ignored in most analysis of this problem).

    The typical radio-access network modernization cycle has so-far been around 5 years. Modernization is mainly driven by hardware obsolescence and need for more capacity per unit area than older (first & second) generation equipment could provide. The most recent and ongoing modernization cycle combines the need for LTE introduction with 2G and possibly 3G modernization. In some instances retiring relative modern 3G equipment on the expense of getting the latest multi-mode, so-called Single-RAN equipment, deployed, has been assessed to be worth the financial cost of write-off.  This new cycle of infrastructure improvements will in relative terms far exceed past upgrades. Software Definable Radios (SDR) with multi-mode (i.e., 2G, 3G, LTE) capabilities are being deployed in one integrated hardware platform, instead of the older generations that were separated with the associated floor space penalty and operational complexity. In theory only Software Maintenance & simple HW upgrades (i.e., CPU, memory, etc..) would be required to migrate from one radio-access technology to another. Have we seen the last HW modernization cycle? … I doubt it very much! (i.e., we still have Cloud and Virtualization concepts going out to the radio node blurring out the need for own core network).

    Multi-mode SDRs should in principle provide a more graceful software-dominated radio-evolution to increasingly more efficient radio access; as cellular networks and customers migrate from HSPA to HSPA+ to LTE and to LTE-advanced. However, in order to enable those spectral-efficient superior radio-access technologies, a Mobile Network Operator will have to follow through with high investments (or incur high incremental operational cost) into vastly improved backhaul-solutions and new antenna capabilities than the past access technologies required.

    Whilst the radio access network infrastructure has gotten a lot more efficient from a cash perspective, the peripheral supporting parts (i.e., antenna, backhaul, etc..) has gotten a lot more costly in absolute terms (irrespective of relative cost per Byte might be perfectly OKAY).

    Thus most of the economics of spectral efficiency can and will be captured within the modernization cycles and new software releases without much ado. However, backhaul and antenna technology investments and increased operational cost is likely to burden cash in the peak of new equipment (including modernization) deployment. Margin pressure is therefor likely if the Opex of supporting the increased performance is not well managed.

    To recapture the most important issues of Spectrum Efficiency Economics:

    • network infrastructure upgrades, from a hardware as well as software perspective, are required => capital investments, though typically result in better Operational cost.
    • optimal customer migration to better and more efficient radio-access technologies => market invest and terminal subsidies.

    Boosting spectrum much beyond 6 times today’s mobile data dedicated spectrum position is unlikely to happen within a foreseeable time frame. It is also unlikely to happen in bands that would be very interesting for both providing both excellent depth of coverage and at the same time depth of capacity (i.e., lower frequency bands with lots of bandwidth available). Spectral efficiency will improve with both next generation HSPA+ as well as with LTE and its evolutionary path. However, depending on how we count the relative improvement, it is not going to be sufficient to substantially boost capacity and performance to the level a “1,000 times challenge” would require.

    This brings us to the topic of vastly increased cell site density and of course Small Cell Economics.

    THE ECONOMICS OF INCREASED CELL SITE DENSITY

    It is fairly clear that there will not be a lot new spectrum available in the next 10+ years. The relative increase in cellular bandwidth will come from re-purposing & perfecting existing legacy spectrum (i.e., by re-farming) and acquiring some new bandwidth in the low frequency range (<800MHz) which per definition is not going to provide a lot of bandwidth.  The very high-frequency range (>3GHz) will contain a lot of bandwidth, but is only interesting for Small Cell and Femto-cell like deployments (feeding frenzy for Small Cells).

    Financially Mobile Operators in mature markets, such as Western Europe, will be lucky to keep their earning and margins stable over the next 8 – 10 years. Mobile revenues are likely to stagnate and possible even decline. Opex pressure will continue to increase (e.g., just simply from inflationary pressures alone). MNOs are unlikely to increase cell site density, if it leads to incremental cost & cash pressure that cannot be recovered by proportional Topline increases. Therefor it should be clear that adding many more cell sites (being it Macro, Pico, Nano or Femto) to meet increasing (often un-managed & unprofitable) cellular demand is economically unwise and unlikely to happen unless followed by Topline benefits.

    Increasing cell density dramatically (i.e., 56 times is dramatic!) to meet cellular data demand will only happen if it can be done with little incremental cost & cash pressure.

    I have no doubt that distributing mobile data traffic over more and smaller nodes (i.e., decrease traffic per node) and utilize open-access technologies to manage data traffic loads are likely to mitigate some of the cash and margin pressure from supporting the higher performance radio-access technologies.

    So let me emphasize that there will always be situations and geographical localized areas where cell site density will be increased disregarding the economics, in order to increase urgent capacity needs or to provide specialized-coverage needs. If an operator has substantially less spectral overhead (e.g., AT&T) than a competitor (e.g., T-Mobile US), the spectrum-starved operator might decide to densify with Small Cells and/or Distributed Antenna Systems (DAS) to be able to continue providing a competitive level of service (e.g., AT&T’s situation in many of its top markets). Such a spectrum starved operator might even have to rely on massive WiFi deployments to continue to provide a decent level of customer service in extreme hot traffic zones (e.g., Times Square in NYC) and remain competitive as well as having a credible future growth story to tell shareholders.

    Spectrum-starved mobile operators will move faster and more aggressively to Small Cell Network solutions including advanced (and not-so-advanced) WiFi solutions. This fast learning-curve might in the longer term make up for a poorer spectrum position.

    In the following I will consider Small Cells in the widest sense, including solutions based both on controlled frequency spectrum (e.g., HSPA+, LTE bands) as well in the ISM frequency bands (i.e., 2.4GHz and 5.8GHz). The differences between the various Small Cell options will in general translate into more or less cells due to radio-access link-budget differences.

    As I have been involved in many projects over the last couple of years looking at WiFi & Small Cell substitution for macro-cellular coverage, I would like to make clear that in my opinion:

    A Small Cells Network is not a good technical (or economical viable) solution for substituting macro-cellular coverage for a mobile network operator.

    However, Small Cells however are Great for

    • Specialized coverage solutions difficult to reach & capture with standard macro-cellular means.
    • Localized capacity addition in hot traffic zones.
    • Coverage & capacity underlay when macro-cellular cell split options have been exhausted.

    The last point in particular becomes important when mobile traffic exceeds the means for macro-cellular expansion possibilities, i.e., typically urban & dense-urban macro-cellular ranges below 200 meters and in some instances maybe below 500 meters pending on the radio-access choice of the Small Cell solution.

    Interference concerns will limit the transmit power and coverage range. However our focus are small localized and tailor-made coverage-capacity solutions, not a substituting macro-cellular coverage, range limitation is of lesser concern.

    For great accounts of Small Cell network designs please check out Iris Barcia (@IBTwi) & Simon Chapman (@simonchapman) both from Keima Wireless. I recommend the very insightful presentation from Iris “Radio Challenges and Opportunities for Large Scale Small Cell Deployments” which you can find at “3G & 4G Wireless Blog” by Zahid Ghadialy (@zahidtg, a solid telecom knowledge source for our Industry).

    When considering small cell deployment it makes good sense to understand the traffic behavior of your customer base. The Figure below illustrates a typical daily data and voice traffic profile across a (mature) cellular network:

    a_typical_traffic_day_in_europe

    • up-to 80% of cellular data traffic happens either at home or at work.+

    Currently there is an important trend, indicating that the evening cellular-data peak is disappearing coinciding with the WiFi-peak usage taking over the previous cellular peak hour.

    A great source of WiFi behavioral data, as it relates to Smartphone usage, you will find in Thomas Wehmeier’s (Principal Analyst, Informa: @Twehmeier) two pivotal white papers on  “Understanding Today’s Smatphone User” Part I and Part II.

    The above daily cellular-traffic profile combined with the below Figure on cellular-data usage per customer distributed across network cells

    traffic_over_network_distribution

    shows us something important when it comes to small cells:

    • Most cellular data traffic (per user) is limited to very few cells.
    • 80% (50%) of the cellular data traffic (per user) is limited to 3 (1) main cells.
    • The higher the cellular data usage (per user) the fewer cells are being used.

    It is not only important to understand how data traffic (on a per user) behaves across the cellular network. It is likewise very important to understand how the cellular-data traffic multiplex or aggregate across the cells in the mobile network.

    We find in most Western European Mature 3G networks the following trend:

    traffic_over_cell_distribution

    • 20% of the 3G Cells carries 60+% of the 3G data traffic.
    • 50% of the 3G Cells carriers 95% or more of the 3G data traffic.

    Thus relative few cells carries the bulk of the cellular data traffic. Not surprising really as this trend was even more skewed for GSM voice.

    The above trends are all good news for Small Cell deployment. It provides confidence that small cells can be effective means to taking traffic away from macro-cellular areas, where there is no longer an option for conventional capacity expansions (i.e., sectorization, additional carrier or conventional cell splits).

    For the Mobile Network Operator, Small Cell Economics is a Total Cost of Ownership exercise comparing Small Cell Network Deployment  to other means of adding capacity to the existing mobile network.

    The Small Cell Network needs (at least) to be compared to the following alternatives;

    1. Greenfield Macro-cellular solutions (assuming this is feasible).
    2. Overlay (co-locate) on existing network grid.
    3. Sectorization of an existing site solution (i.e., moving from 3 sectors to 3 + n on same site).

    Obviously, in the “extreme” cellular-demand limit where non of the above conventional means of providing additional cellular capacity are feasible, Small Cell deployment is the only alternative (besides doing nothing and letting the customer suffer). Irrespective we still need to understand how the economics will work out, as there might be instances where the most reasonable strategy is to let your customer “suffer” best-effort services. This would in particular be the case if there is no real competitive and incremental Topline incentive by adding more capacity.

    However,

    Competitive circumstances could force some spectrum-starved operators to deploy small cells irrespective of it being financially unfavorable to do so.

    Lets begin with the cost structure of a macro-cellular 3G Greenfield Rooftop Site Solution. We take the relevant cost structure of a configuration that we would be most likely to encounter in a Hot Traffic Zone / Metropolitan high-population density area which also is likely to be a candidate area for Small Cell deployment. The Figure below shows the Total Cost of Ownership, broken down in Annualized Capex and Annual Opex, for a Metropolitan 3G macro-cellular rooftop solution:

    tco_greenfield_rooftop_site

    Note 1: The annualized Capex has been estimated assuming 5 years for RAN Infra, Backaul & Core, and 10 years for Build. It is further assumed that the site is supported by leased-fiber backhaul. Opex is the annual operational expense for maintaining the site solution.

    Note 2: Operations Opex category covers Maintenance, Field-Services, Staff cost for Ops, Planning & optimization. The RAN infra Capex category covers: electronics, aggregation, antenna, cabling, installation & commissioning, etc..

    Note 3: The above illustrated cost structure reflects what one should expect from a typical European operation. North American or APAC operators will have different cost distributions. Though it is not expected to change conclusions substantially (just redo the math).

    When we discuss Small Cell deployment, particular as it relates to WiFi-based small cell deployment, with Infrastructure Suppliers as well as Chip Manufacturers you will get the impression that Small Cell deployment is Almost Free of Capex and Opex; i.e., hardly any build cost, free backhaul and extremely cheap infrastructure supported by no site rental, little maintenance and ultra-low energy consumption.

    Obviously if Small Cells cost almost nothing, increasing cell site density with 56 times or more becomes very interesting economics … Unfortunately such ideas are wishful thinking.

    For Small Cells not to substantially pressure margins and cash, Small Cell Cost Scaling needs to be very aggressive. If we talk about a 56x increase in cell site density the incremental total cost of ownership should at least be 56 times better than to deploy a macro-cellular expansion. Though let’s not fool ourselves!

    No mobile operator would densify their macro cellular network 56 times if absolute cost would proportionally increase!

    No Mobile operator would upsize their cellular network in any way unless it is at least margin, cost & cash neutral.

    (I have no doubt that out there some are making relative business cases for small cells comparing an equivalent macro-cellular expansion versus deploying Small Cells and coming up with great cases … This would be silly of course, not that this have ever prevented such cases to be made and presented to Boards and CxOs).

    The most problematic cost areas from a scaling perspective (relative to a macro-cellular Greenfield Site) are (a) Site Rental (lamp posts, shopping malls,), (b) Backhaul Cost (if relying on Cable, xDSL or Fiber connectivity), (c) Operational Cost (complexity in numbers, safety & security) and (d) Site Build Cost (legal requirements, safety & security,..).

    In most realistic cases (I have seen) we will find a 1:12 to 1:20 Total Cost of Ownership difference between a Small Cell unit cost and that of a Macro-Cellular Rooftop’s unit cost. While unit Capex can be reduced very substantially, the Operational Expense scaling is a lot harder to get down to the level required for very extensive Small Cell deployments.

    EXAMPLE:

    For a typical metropolitan rooftop (in Western Europe) we have the annualized capital expense (Capex) of ca. 15,000 Euro and operational expenses (Opex) in the order of 30,000 Euro per annum. The site-related Opex distribution would look something like this;

    • Macro-cellular Rooftop 3G Site Unit Annual Opex:
    • Site lease would be ca. 10,500EUR.
    • Backhaul would be ca. 9,000EUR.
    • Energy would be ca. 3,000EUR.
    • Operations would be ca. 7,500EUR.
    • i.e., total unit Opex of 30,000EUR (for average major metropolitan area)

    Assuming that all cost categories could be scaled back with a factor 56 (note: very big assumption that all cost elements can be scaled back with same factor!)

    • Target Unit Annual Opex cost for a Small Cell:
    • Site lease should be less than 200EUR (lamp post leases substantially higher)
    • Backhaul should be  less than 150EUR (doable though not for carrier grade QoS).
    • Energy should be less than 50EUR (very challenging for todays electronics)
    • Operations should be less than 150EUR (ca. 1 hour FTE per year … challenging).
    • Annual unit Opex should be less than 550EUR (not very likely to be realizable).

    Similar for the Small Cell unit Capital expense (Capex) would need to be done for less than 270EUR to be fully scalable with a macro-cellular rooftop (i.e., based on 56 times scaling).

    • Target Unit Annualized Capex cost for a Small Cell:
    • RAN Infra should be less than 100EUR (Simple WiFi maybe doable, Cellular challenging)
    • Backhaul would be less than 50EUR (simple router/switch/microwave maybe doable).
    • Build would be less than 100EUR (very challenging even to cover labor).
    • Core would be less than 20EUR (doable at scale).
    • Annualized Capex should be less than 270EUR (very challenging to meet this target)
    • Note: annualization factor: 5 years for all including Build.

    So we have a Total Cost of Ownership TARGET for a Small Cell of ca. 800EUR

    Inspecting the various capital as well as operational expense categories illustrates the huge challenge to be TCO comparable to a macro-cellular urban/dense-urban 3G-site configuration.

    Massive Small Cell Deployment needs to be almost without incremental cost to the Mobile Network Operator to be a reasonable scenario for the 1,000 times challenge.

    Most the analysis I have seen, as well as carried out myself, on real cost structure and aggressive pricing & solution designs shows that the if the Small Cell Network can be kept between 12 to 20 Cells (or Nodes) the TCO compares favorably to (i.e., beating) an equivalent macro-cellular solution. If the Mobile Operator is also a Fixed Broadband Operator (or have favorable partnership with one) there are in general better cost scaling possible than above would assume (e.g., another AT&T advantage in their DAS / Small Cell strategy).

    In realistic costing scenarios so far, Small Cell economical boundaries are given by the Figure below:

    Let me emphasize that above obviously assumes that an operator have a choice between deploying a Small Cell Network and conventional Cell Split, Nodal Overlay (or co-location on existing cellular site) or Sectorization (if spectral capacity allows). In the Future and in Hot Traffic Zones this might not be the case. Leaving Small Cell Network deployment or letting the customers “suffer” poorer QoS be the only options left to the mobile network operator.

    So how can we (i.e., the Mobile Operator) improve the Economics of Small Cell deployment?

    Having access fixed broadband such as fiber or high-quality cable infrastructure would make the backhaul scaling a lot better. Being a mobile and fixed broadband provider does become very advantageous for Small Cell Network Economics. However, the site lease (and maintenance) scaling remains a problem as lampposts or other interesting Small Cell locations might not scale very aggressively (e.g., there are examples of lamppost leases being as expensive as regular rooftop locations). From a capital investment point of view, I have my doubts whether the price will scale downwards as favorable as they would need to be. Much of the capacity gain comes from very sophisticated antenna configurations that is difficult to see being extremely cheap:

    Small Cell Equipment Suppliers would need to provide a Carrier-grade solution priced at  maximum 1,000EUR all included! to have a fighting chance of making massive small cell network deployment really economical.

    We could assume that most of the “Small Cells” are in fact customers existing private access points (or our customers employers access points) and simply push (almost) all cellular data traffic onto those whenever a customer is in vicinity of such. All those existing and future private access points are (at least in Western Europe) connected to at least fairly good quality fixed backhaul in the form of VDSL, Cable (DOCSIS3), and eventually Fiber. This would obviously improve the TCO of “Small Cells” tremendously … Right?

    Well it would reduce the MNOs TCO (as it shift the cost burden to the operator’s customer or employers of those customers) …Well … This picture also would  not really be Small Cells in the sense of proper designed and integrated cells in the Cellular sense of the word, providing the operator end-2-end control of his customers service experience. In fact taking the above scenario to the extreme we might not need Small Cells at all, in the Cellular sense, or at least dramatically less than using the standard cellular capacity formula above.

    In Qualcomm (as well as many infrastructure suppliers) ultimate vision the 1,000x challenge is solved by moving towards a super-heterogeneous network that consist of everything from Cellular Small Cells, Public & Private WiFi access points as well as Femto cells thrown into the equation as well.

    Such an ultimate picture might indeed make the Small Cell challenge economically feasible. However, it does very fundamentally change the current operational MNO business model and it is not clear that transition comes without cost and only benefits.

    Last but not least it is pretty clear than instead of 3 – 5 MNOs all going out plastering walls and lampposts with Small Cell Nodes & Antennas sharing might be an incredible clever idea. In fact I would not be altogether surprised if we will see new independent business models providing Shared Small Cell solutions for incumbent Mobile Network Operators.

    Before closing the Blog, I do find it instructive to pause and reflect on lessons from Japan’s massive WiFi deployment. It might serves as a lesson to massive Small Cell Network deployment as well and an indication that collaboration might be a lot smarter than competition when it comes to such deployment:

    softband_wifi_deployment

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    The Thousand Times Challenge: PART 2 … How to provide cellular data capacity?

    CELLULAR DATA CAPACITY … A THOUSAND TIMES CHALLENGE?

    It should be obvious that I am somewhat skeptical about all the excitement around cellular data growth rates and whether its a 1,000x or 250x or 42x (see my blog on “The Thousand Times Challenge … The answer to everything about mobile data?”). In this I share very much Dean Bubley’s (Disruptive Wireless) critical view on the “cellular growth rate craze”. See Dean’s account in his recent Blog “Mobile data traffic growth – a thought experiment and forecast”.

    This obsession with cellular data growth rates is Largely Irrelevant or only serves Hysteria and Cool Blogs, Twittter and Press Headlines (which is for nothing else occasionally entertaining).

    What IS Important! is how to provide more (economical) cellular capacity, avoiding;

    • Massive Congestion and loss of customer service.
    • Economical devastation as operator tries to supply network resources for an un-managed cellular growth profile.

    (Source: adapted from K.K. Larsen “Spectrum Limitations Migrating to LTE … a Growth Market Dilemma?“)

    To me the discussion of how to Increase Network Capacity with a factor THOUSAND is an altogether more interesting discussion than what the cellular growth rate might or might not be in 2020 (or any other arbitrary chosen year).

    Mallinson article “The 2020 Vision for LTE”  in FierceWirelessEurope gives a good summary of this effort. Though my favorite account on how to increase network capacity focusing on small cell deployment  is from Iris Barcia (@ibtwi) & Simon Chapman (@simonchapman) from Keima Wireless.

    So how can we simply describe cellular network capacity?

    Well … it turns out that Cellular Network Capacity can be described by 3 major components; (1) available bandwidth B, (2) (effective) spectral efficiency and (3) number of cells deployed N.

    The SUPPLIED NETWORK CAPACITY in Mbps (i.e., C) is equal to  the AMOUNT OF SPECTRUM, i.e., available bandwidth, in MHz (i..e, B) multiplied with the  SPECTRAL EFFICIENCY PER CELL in Mbps/MHz (i.e., E) multiplied by the NUMBER OF CELLS (i.e., N).

    It should be understood that the best approach is to apply the formula on a per radio access technology basis, rather than across all access technologies. Also separate the analysis in Downlink capacity (i.e., from Base Station to Customer Device) and in Uplink (from consumer Device to Base Station). If averages across many access technologies or you are considering the total bandwidth B including spectrum both for Uplink and for Downlink, the spectral efficiency needs to be averaged accordingly. Also bear in mind that there could be some inter-dependency between the (effective) spectral efficiency and number cells deployed. Though it  depends on what approach you choose to take to Spectral Efficiency.

    It should be remembered that not all supplied capacity is being equally utilized. Most operators have 95% of their cellular traffic confined to 50% of less of their Cells. So supplied capacity in half (or more) of most cellular operator’s network remains substantially under-utilized (i.e., 50% or more of radio network carries 5% or less of the cellular traffic … if you thought that Network Sharing would make sense … yeah it does … but its a different story;-).

    Therefore I prefer to apply the cellular capacity formula to geographical limited areas of the mobile network, rather than network wide. This allows for more meaningful analysis and should avoid silly averaging effects.

    So we see that providing network capacity is “pretty easy”: The more bandwidth or available spectrum we have the more cellular capacity can be provided. The better and more efficient air-interface technology the more cellular capacity and quality can we provide to our customers. Last (but not least) the more cells we have build into our mobile network the more capacity can be provided (though economics does limit this one).

    The Cellular Network Capacity formula allow us to breakdown the important factors to solve the “1,000x Challenge”, which we should remember is based on a year 2010 reference (i.e., feels a little bit like cheating! right?;-) …

    The Cellular Capacity Gain formula:

    Basically the Cellular Network Capacity Gain in 2020 (over 2010) or the Capacity we can supply in 2020 is related to how much spectrum we have available (compared to today or 2010), the effective spectral efficiency relative improvement over today (or 2010) and the number of cells deployed in 2020 relative to today (or 2010).

    According with Mallinson’s article the “1,000x Challenge” looks the following (courtesy of SK Telekom);

    According with Mallinson (and SK Telekom, see “Efficient Spectrum Resource Usage for Next Generation NW” by H. Park, presented at 3GPP Workshop “on Rel.-12 and onwards”, Ljubljana, Slovenia, 11-12 June 2012) one should expect to have 3 times more spectrum available in 2020 (compared to 2010 for Cellular Data), 6 times more efficient access technology (compared to what was available in 2010) and 56 times higher cell density compared to 2010. Another important thing to remember when digesting the 3 x 6 x 56 is: this is an estimate from South Korea and SK Telekom and to a large extend driven by South Korean conditions.

    Above I have emphasized the 2010 reference. It is important to remember this reference to better appreciate where the high ratios come from in the above. For example in 2010 most mobile operators where using 1 to maximum 2 carriers or in the process to upgrade to 2 carriers to credible support HSPA+. Further many operators had not transitioned to HSPA+ and few not even added HSUPA to their access layer. Furthermore, most Western European operators had on average 2 carriers for UMTS (i.e., 2×10 MHz @ 2100MHz). Some operators with a little excess 900MHz may have deployed a single carrier and either postponed 2100MHz or only very lightly deployed the higher frequency UMTS carrier in their top cities. In 2010, the 3G population coverage (defined as having minimum HSDPA) was in Western Europe at maximum 80% and in Central Eastern & Southern Europe most places maximum 60%. 3G geographical coverage always on average across the European Union was in 2010 less than 60% (in Western Europe up-to 80% and in CEE up-to 50%).

    OPERATOR EXAMPLE:

    Take an European Operator with 4,000 site locations in 2010.

    In 2010 this operator had deployed 3 carriers supporting HSPA @ 2100MHz (i..e, total bandwidth of 2x15MHz)

    Further in 2010 the Operator also had:

    • 2×10 MHz GSM @ 900MHz (with possible migration path to UMTS900).
    • 2×30 MHz GSM @ 1800MHz (with possible migration path to LTE1800).

    By 2020 it retained all its spectrum and gained

    • 2×10 MHz @ 800MHz for LTE.
    • 2×20 MHz @ 2.6GHz for LTE.

    For simplicity (and idealistic reasons) let’s assume that by 2020 2G has finally been retired. Moreover, lets concern ourselves with cellular data at 3G and above service levels (i.e., ignoring GPRS & EDGE). Thus I do not distinguish between whether the air-interface is HSPA+ or LTE/LTE advanced.

    OPERATOR EXAMPLE: BANDWIDTH GAIN 2010 – 2020:

    The Bandwidth Gain part of the “Cellular Capacity Gain” formula is in general specific to individual operators and the particular future regulatory environment (i.e., in terms of new spectrum being released for cellular use). One should not expect a universally applicable ratio here. It will vary with a given operator’s spectrum position … Past, Present & Future.

    In 2010 our Operator had 15MHz (for either DL or UL) supporting cellular data.

    In 2020 the Operator should have 85MHz (for either DL or UL), which is a almost a factor 6 more than in 2010. Don’t be concerned about this not being 3! After all why should it be? Every country and operator will face different constraints and opportunities and therefor there is no reason why 3 x 6 x 56 would be a universal truth!

    If Regulator’s and Lawmakers would be more friendly towards spectrum sharing the boost of available spectrum for cellular data could be a lot more.

    SPECTRAL EFFICIENCY GAIN 2010 – 2020:

    The Spectral Efficiency Gain part of the “Cellular Capacity Gain” formula is more universally applicable to cellular operators at the same technology stage and with a similar customer mix. Thus in general for apples and apple comparison more or less same gains should be expected.

    In my experience Spectral Efficiency almost always gets experts emotions running high. More often than not there is a divide between those experts (across Operators, Suppliers, etc.) towards what would be an appropriate spectral efficiency to use in capacity assessments. Clearly everybody understands that the theoretical peak spectral efficiency is not reflecting the real service experience of customers or the amount of capacity an operator has in his Mobile Network. Thus, in general an effective (or average) spectral efficiency is being applied often based on real network measurements or estimates based on such.

    When LTE was initially specified its performance targets was referenced to HSxPA Release 6. The LTE aim was to get 3 -4 times the DL spectral efficiency and 2 – 3 times the UL spectral efficiency. LTE advanced targets to double the peak spectral efficiency for both DL and UL.

    At maximum expect the spectral efficiency to be:

    • @Downlink to be 6 – 8 times that of Release 6.
    • @Uplink to be 4 – 6 times that of Release 6.

    Note that this comparison is assuming an operator’s LTE deployment would move 4×4 MiMo to 8×8 MiMo in Downlink and from 64QAM SiSo to 4×4 MiMo in Uplink. Thus a quantum leap in antenna technology and substantial antenna upgrades over the period from LTE to LTE-advanced would be on the to-do list of the mobile operators.

    In theory for LTE-advanced (and depending on the 2010 starting point) one could expect a factor 6 boost in spectral efficiency  by 2020 compared to 2010, as put down in the “1,000x challenge”.

    However, it is highly unlikely that all devices by 2020 would be LTE-advanced. Most markets would be have at least 40% 3G penetration, some laggard markets would still have a very substantial 2G base. While LTE would be growing rapidly the share of LTE-advanced terminals might be fairly low even at 2020.

    Using a x6 spectral efficiency factor by 2020 is likely being extremely optimistic.

    A more realistic assessment would be a factor 3 – 4 by 2020 considering the blend of technologies in play at that time.

    INTERLUDE

    The critical observer sees that we have reached a capacity gain (compared to 2010) of 6 x (3-4) or 18 to 24 times. Thus to reach 1,000x we still need between 40 and 56 times the cell density.

    and that translate into a lot of additional cells!

    CELL DENSITY GAIN 2010 – 2020:

    The Cell Density Gain part of the “Cellular Capacity Gain” formula is in general specific to individual operators and the cellular traffic demand they might experience, i.e., there is no unique universal number to be expected here.

    So to get to 1,000x the capacity of 2010 we need either magic or a 50+x increase in cell density (which some may argue would amount to magic as well) …

    Obviously … this sounds like a real challenge … getting more spectrum and high spectral efficiency is piece of cake compared to a 50+ times more cell density. Clearly our Mobile Operator would go broke if it would be required to finance 50 x 4000 = 200,000 sites (or cells, i.e., 3 cells = 1 macro site ). The Opex and Capex requirements would simply NOT BE PERMISSIBLE.

    50+ times site density on a macro scale is Economical & Practical Nonsense … The Cellular Network Capacity heuristics in such a limit works ONLY for localized areas of a Mobile Network!

    The good news is that such macro level densification would also not be required … this is where Small Cells enter the Scene. This is where you run to experts such as Simon Chapman (@simonchapman) from Keima Wireless or similar companies specialized in providing intelligent small cell deployment. Its clear that this is better done early on in the network design rather than when the capacity pressure becomes a real problem.

    Note that I am currently assuming that Economics and Deployment Complexity will not become challenging with Small Cell deployment strategy … this (as we shall see) is not necessarily a reasonable assumption in all deployment scenarios.

    Traffic is not equally distributed across a mobile network as the chart below clearly shows (see also Kim K Larsen’s “Capacity Planning in Mobile Data Networks Experiencing Exponential Growh in Demand”):

    20% of the 3G-cells carries 60% of the data traffic and 50% of the 3G-cells carries as much as 95% of the 3G traffic.

    Good news is that I might not need to worry too much about half of my cellular network that only carries 5% of my traffic.

    Bad news is that up-to 50% of my cells might actually give me a substantial headache if I don’t have sufficient spectral capacity and enough customers on the most efficient access technology. Leaving me little choice but to increase my cellular network density, i.e., build more cells to my existing cellular grid.

    Further, most of the data traffic is carried within the densest macro-cellular network grid (at least if an operator starts exhausting its spectral capacity with a traditional coverage grid). In a typical European City ca. 20% of Macro Cells will have a range of 300 meter or less and 50% of the Macro Cells will have a range of 500 meter or less (see below chart on “Cell ranges in a typical European City”).

    Finding suitable and permissible candidates for Macro cellular cell splits below 300 meter is rather unlikely.  Between 300 and 500 meter there might still be macro cellular split optionallity and if so would make the most sense to commence on (pending on future anticipated traffic growth). Above 500 meter its usually fairly likely to find suitable macro cellular site candidates (i.e., in most European Cities).

    Clearly if the cellular data traffic increase would require a densification ratio of 50+ times current macro-cellular density a macro cellular alternative might be out of the question even for cell ranges up-to 2 km.

    A new cellular network paradigm is required as the classical cellular network design brakes down!

    Small Cell implementation is often the only alternative a Mobile Operator has to provide more capacity in a dense urban or high-traffic urban environment.

    As Mobile Operators changes their cellular design, in dense urban and urban environments, to respond to the increasing cellular data demand, what kind of economical boundaries would need to be imposed to make a factor 50x increase in cell density work out.

    No Mobile Operator can afford to see its Opex and Capex pressure rise! (i.e., unless revenue follows or exceed which might not be that likely).

    For a moment … remember that this site density challenge is not limited to a single mobile operator … imagining that all operators (i.e., typical 3 -5 except for India with 13+;-) in a given market needs to increase their cellular site density with a factor 50. Even if there is (in theory) lots of space on the street level for Small Cells … one could imagine the regulatory resistance (not to mention consumer resistance) if a city would see a demand for Small Cell locations increase with a factor 150 – 200.

    Thus, Sharing Small Cell Locations and Supporting Infrastructure will become an important trend … which should also lead to Better Economics.

    This bring us to The Economics of the “1,000x Challenge” … Stay tuned!

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    The Thousand Times Challenge: PART 1 … The answer to everything about mobile data?

    This is not PART 2 of “Mobile Data Growth…The Perfect Storm” … This is the story of the Thousand Times Challenge!

    It is not unthinkable that some mobile operators will face very substantial problems with their cellular data networks due to rapid, uncontrollable or un-managed cellular data growth. Once cellular data demand exceeds the installed base supply of network resources, the customer experience will likely suffer and cellular data consumers will no longer get the same service level that they had prior to the onset of over-demand.

    One might of course argue that consumers were (and in some instances still are) spoiled during the period when mobile operators had plenty of spectral capacity available (relative to their active customer base) with unlimited data plans and very little cellular network load . As more and more customers migrate to smartphones and 3G data services, it follows naturally that there will be increasingly less spectral resources available per customer.

    The above chart (from “Capacity Planning in Mobile Data Networks Experience Exponential Growth in Demand” illustrates such a situation where customers cellular data demand eventually exceeds the network capacity … which leads to a congested situation and less network resources per customer.

    A mobile operator have several options that can mitigate emergence of capacity and spectrum crunch:

    1. Keep expand and densify the cellular network.
    2. Free up legacy (i.e. “old-technology”) spectrum and deploy for technology facing demand pressure.
    3. Introduce policy and active demand management on per user / segment level.
    4. Allow customers service to degrade as provider of best-effort cellular data.
    5. Stimulate and design for structural off-loading (levering fixed as well as cellular networks).
    6. etc..

    DEMAND … A THOUSAND TIMES FABLE?

    Let me start with saying that cellular data growth does pose a formidable challenge for many mobile operators … already today … its easy to show that even at modest growth rates cellular data demand gets pretty close or beyond cellular network resources available today and in the future. Unless we fundamentally changes the way we design, plan and build networks.

    However, Today The Challenge is Not network wide … At present, its limited to particular areas of the cellular networks … though as the cellular data traffic growths, the demand challenge does spread outwards and addresses an ever higher share of the cellular network.

    Lately 1,000 has become a very important number. It has become the answer to the Smartphone Challenge and exponential growth of mobile data. 1000 seems to represent both demand as well as supply. Qualcomm has made it their “mission in life” (at at least the next 8 years) to solve the magic 1000 challenge. Mallinson article “The 2020 Vision for LTE”  in FierceWirelessEurope gives a slightly more balanced view on demand and target supply of cellular resources: “Virtually all commentators expect a 15 to 30-fold traffic increase over five years and several expect this growth trend to last a decade to 2020, representing a 250-1,000-fold increase.” (note: the cynic in wonders about the several, its more than 2, but is it much more than 3?)

    The observant reader will see that the range between minimum and maximum to be a factor of 4 … a reasonably larger error of margin to plan for. If by 2020 the demand would be 1,000 times that of demand in 2010, our Technologies better be a lot better than that as that would be an average with a long tail.

    Of course most of us know that the answer really is 42! NOT 1000!

    Joke aside … And let’s get serious about this 1000 Fable!

    Firstly, 1,000 is (according with Qualcomm) the expected growth of data between 2010 and 2020 … Thus if data was 42 in 2010 it would be 1000×42 by 2020. That would be a CAGR of 100% over the period or a doubling of demanded data year in year our for 10 years.

    … Well not really!

    Qualcomm states that data demand in 2012 would be 10x that of 2010 . Thus, it follows that data demand between 2012 and 2020 “only” would be 100x or a CAGR of 78% over that period.

    So in 2021 (1 year after we had 1,000x) we would see demand of ca. 1,800x, in 2022 (2 years after we solved the 1000x challenge) we would experience a demand of more than 3,000x, and so forth …

    So great to solve the 1,000x challenge by 2020 but it’s going to be like “peeing in your trouser on a cold winter day” . Yes it will be warm, for a little while. Then its going to be really cold. In other words not going to help much structurally.

    Could it be that this 1,000x challenge might be somewhat flawed?

    1. If All Commentators and Several Experts are to be believed, the growth worldwide is almost perfectly exponential with an annual growth rate between 70% and 100%.
    2. Growth is “unstoppable” -> unlimited sources for growth.

    Actually most projections (from several expert sources;-) that I have seen does show substantial deceleration as the main source for growth exhaust, i.e., as Early & Late Majority of customers adapt to mobile data. Even Cisco own “Global Mobile Data Traffic Forecast Update, 2011 – 2016” shows an average deceleration of growth with an average of 20% per anno between 2010 and their 2014 projections (note: it’s sort of “funny” that Cisco then decide that after 2014 growth no longer slows down but stays put at 78% … alas artistic freedom I suppose?).

    CELLULAR CUSTOMER MIGRATION

    The following provides projection of 2G, 3G and LTE uptake between 2010 (Actual) and 2020 (Expected). The dynamics is based on latest Pyramid Research cellular projections for WEU, US, APAC, LA & CEE between 2010 to 2017. The “Last Mile”, 2018 – 2020, is based on reasonable dynamic extrapolations based on the prior period with a stronger imposed emphasis on LTE growth. Of course Pyramid Research provides one view of the technology migration and given the uncertainty on market dynamics and pricing policies are simply one view on how the cellular telco world will develop. This said, I tend to find Pyramid Research getting reasonably close to actual developments and the trends across the various markets are not that counter-intuitive.

    For the US Market LTE is expected to grow very fast and  reach a penetration level beyond 60% by 2020. For the other markets LTE is expected to evolve relative sluggish with an uptake percentage of 20%+/-5% by 2020. It should be remembered that all projections are averages. Thus within a market, for a specific country or operator, the technology shares could very well differ somewhat from the above.

    The growth rates for LTE customer uptake over the period; 2010/2011 – 2020, 2015 – 2020 and respective LTE share in 2020.

    WEU 2010-2020: 87%, 2015 – 2020: 24%, share in 2020: 20%.

    USA 2010-2020: 48%, 2015 – 2020: 19%, share in 2020: 62%.

    APAC 2010-2020: 118%, 2015 – 2020: 61%, share in 2020: 30%.

    CEE 2011-2020: 168%, 2015 – 2020: 37%, share in 2020: 20%.

    LA 2010-2020: 144%, 2015 – 2020: 37%, share in 2020: 40%.

    Yes the LTE growth rates are very impressive when compared to the initial launch year with the very initial uptake. As already pointed out in my Blog …. growth rates in referenced back to a penetration less than 2% has little practical meaning. The average LTE uptake rate across all the above markets between 2012 to 2020 is 53%+/-17% (highest being APAC and Lowest being USA).

    What should be evident from the above technology uptake charts are that

    • 3G remains strong even in 2020 (though likely dominated by prepaid at that time).
    • 2G will remain for a longtime in both CEE & APAC, even toward 2020.

    In the scenario where we have a factor 100 in growth of usage between 2012 and 2020, which is a CAGR of 78%, the growth of usage per user would to be 16% pa at an annual uptake rate of 53%. However, without knowing the starting point of the LTE data usage (which initially will be very low as there is almost not users), these growth rates are not of much use and certainly cannot be used to make up any conclusions about congestion or network dire straits.

    Example based on European Growth Figures:

    A cellular networks have 5 mio customers, 50% Postpaid.

    Network has 4,000 cell sites (12,000 sectors) that by 2020 covers both UMTS & LTE to the same depth.

    in 2020 the operator have allocated 2×20 MHz for 3G & 2×20 MHz for LTE. Remaining 2G customers are one a single shared GSM network support all GSM traffic in country with no more than 2x5MHz.

    By 2020 the cellular operator have ca. 4Mio 3G users and ca. 0.9Mio LTE users (remaining 100 thousand GSM customers are the real Laggards).

    The 3G uptake growth rate ‘2010 – ‘2020 was 7%, between ’10 – ’12 it was 25%. 3G usage growth would not be very strong as its a blend of Late Majority and Laggards (including a fairly large Prepaid segment that appear hardly to use Cellular data).

    The LTE uptake growth rate ‘2010 – ‘2020 was 87%, between ’10 – ’12 it was 458%. The first 20% of LTE would like be consisting of Innovators and Early Adopters. Thus, usage growth of LTE should be expected to be more aggressive than for 3G.

    Let’s assume that 20% of the cell sites carries 50% of the devices and for simplicity also data traffic (see for example my Slideshare presentation “Capacity Planning in Mobile Data Networks Experiencing Exponential Growth in Demand” which provides evidence for such distribution).

    So we have ca. 800 3G users per sector (or ca. 40 3G users per sector per MHz). By 2020, one would likewise for LTE anticipate ca. 200 LTE users per sector (or ca. 10 LTE users per sector per MHz). Note that no assumptions of activity rate has been imposed.

    Irrespective of growth rate we need to ask ourselves whether 10 LTE users per sector per MHz would pose a congested situation (in the busy hour). Assume that the effective LTE spectral efficiency across a macro cellular cell would be 5Mbps/MHz/Sector. So the 10 LTE users could on average share up-to 100Mbps (@ 20MHz DL).

    For 3G, where we would have 40 3G users per sector per MHz. Similar (very simple) considerations allows to conclude that the 40 4G users would have no more than 40Mbps (under semi-ideal radio conditions and @ 20MHz DL). This could be a lot more demanding and customer affecting than the resulting LTE demand, despite LTE having substantially higher growth rate than we saw for 3G over the same period.

    High growth rates does not default result in cellular network breakdown. It is the absolute traffic load (in the Busy Hour) that matters.

    The growth of of cellular data usage between 2010 and 2020 is likewise going to be awesome (it would be higher than above technology uptake rates).. but also pretty meaningless.

    Growth rates only matter in as much as growth brings an absolute demanded traffic level above the capability of the existing network and spectral resources (supplied traffic capacity).

    Irrespective of a growth rate is high, medium or low … all can cause havoc in a cellular network … some networks will handle a 1,000x without much ado, others will tumble at 250x whatever the reference point level (which also includes the network design and planning maturity levels).

    However, what is important is how to provide more (economical) cellular capacity avoiding;

    • Massive Congestion and loss of customer service.
    • Economical devastation as operator tries to supply network resources for an un-managed cellular growth profile.

    (Source: adapted from K.K. Larsen “Spectrum Limitations Migrating to LTE … a Growth Market Dilemma?“)

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