Future Mobile Networks: Business Models

Transcription

1 Future Mobile Networks: Business Models Presentation held at 1st Caspian and Black Sea Regulatory Conference, Istanbul May 2006 Erik Bohlin, Chalmers University of Technology Contact: Erik Bohlin Contribution & proposal builds upon results developed in the EC/DG JRC/IPTS Project Future Mobile Services (see The usual disclaimer apply - this contribution is of the authors, and does not necessarily reflect the view of the European Commission. 1

2 Aim & Contribution The aim is to provide a financial simulation of future mobile services Provide an interrelated set of technology, geography, costing and demand levels, based on scenarios and sensitivity analyses Contributes with an early financial analysis of 4G networks Outline of presentation 1. Trends in mobile communications 2. Overview of model 3. Main assumptions and results (incl. sensitivity analysis) 4. Conclusions 5. Appendix showing more detailed assumptions 2

3 Telecommunications Subscribers worldwide in billions Note that the fixed line ITU projection made in 2002 (dashed blue line) is an overestimate as mobile substitutes Mobile phone subscribers globally total nearly 1.5 billion by mid- 2004, about one quarter of the world's population Source: ITU 2004 (Reuters report) REF Stop Press 2Bn December 2005 August 2005: 1.8 Billion Mobile Users REF Fixed: 1.0 Billion in Fixed line Fixed: mid billion Mobile ITU Fixed line estimate 2002 Source: ITU World Telecommunication Development Report, 2002; ITU World Telecommunication Indicators Database and ITU projections 3

4 Global Mobile user population growth Billions of mobile services users worldwide Bn users, K users, 2000 lowest 30% + M2M Sub- $ M2M Saturated + M2M NICs middle classes Indicative strong growth for Internet of Things NB Confidence in estimates Reduces with time With globalisation, OECD cost levels of services and handsets will slowly be rebalanced, being set by average world prices Saturation is set by affordable price for the majority of users numbers which may reach 65% of a global population of over 8.2Bn by 2030 (US Census Bureau, 2005) if world usage goes towards average OECD levels of saturation today: 5.3Bn users 4

5 4 Generations of Mobile Radio are appearing Richness of services Complete e-office and consumer multimedia environment at broadband speeds with secure transactons, streaming and download 4G - Drivers : 1 lower cost 2; Radio performance - data rates 150Mbps to 1Gbps, also coverage, range, capacity, cell density; adaptive optimisation; 3 New applications 4 spectrum availability; 5 Integration of different technologies (Bluetooth, WiFi, WAN, W-LAN etc) 3G evolution (3.5G) - better download data rates Mbps, lower cost, more robust pure IP network 3G - Mobile Web, Multimedia IP services- Multimedia Messaging, LBS, office, cct sw & IP networks Plain simple voice 2G Digital voice, mobile data 2G evolution G : GPRS, EDGE etc Packet data, always connected 1G Analog voice telephony ( Nokia, others)

6 The basis for the business modelling: What is 4G? Important characteristics used in our models: Licensed AND unlicensed spectrum Increased data usage Multi technology usage Decreased cell sizes Introduction of ad-hoc (mesh) network capabilities Leading to reduced density of radio equipment Increased bit rates Increased importance of software (e.g. SDR) New pricing schemes In our models we have used two radio access points: APs and UAPs UAPs are defined to function as ordinary base stations with backhaul connections APs are defined to function as repeaters and signal amplifiers, relaying radio signals to allow extensions of hops. 6

7 Model characteristics 4G network overview All 4G network elements have mesh capabilities Multihop paths between handsets, APs and UAPs, and handsets have relay functionality (and with SDR) Redundant paths are often available A AP B AP C UAP Backbone network (BN) UAP BN 7

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9 Model characteristics - overview The simulated 4G network is divided into a number of corresponding network units Urban, suburban, and rural The network units have distinctively different characteristics E.g. Cell size, average distance between users, number of users in each network unit, etc. Each network unit is composed of a number of base stations, UAPs, etc., using various technologies The characteristics of each network unit are defined regarding e.g. number of users coverage, average distance between users, number of users in each network unit, etc. leading to different investment costs By spreading the network units like a puzzle over the simulated geographical area, the investment costs can be approximated e.g. 10 rural units, 16 suburban units, and 24 rural units to cover a certain area 9

10 Network unit overview Urban Suburban Rural Different wireless technologies used The network units differ in several ways, e.g.: Size Population density Technology choices Cost structures 10

11 Model characteristics - overview The model used is schematically presented to the right By using researched estimates for input data reasonable cost structures can be approximated The aim is to evaluate 4G business models by calculating ARPU levels needed to cover OPEX and CAPEX for the different socio/economic scenarios developed in the project Technology limitations/capabilities Population density Urban NU Urban NU Population data # Urban Urban NU NU Needed CAPEX data Base station costs Handset subsidies Equipment replacement Installation Etc. Suburban NU Suburban NU # Suburban NU NU Needed Total Costs Total Costs Geography data OPEX data Marketing Backhaul Site Rent Etc. Rural NU Rural NU # Rural Rural NU NU Needed Factors from scenarios developed in previous Work Packages ARPU ARPU needed to to cover cover costs costs 11

12 Network units assumptions on population / km 2 A geographical area is divided into three subareas, differing regarding e.g. technology needs, population density, service usage, etc.: Urban areas Suburban areas Rural areas Population density data for real countries have been used to construct the network units Singapore and Hong Kong have been used to model Urban areas The Netherlands have been used to model Suburban areas Sparsely populated countries as Sweden and Estonia have been used to model Rural areas Population Land area (km2) Population/ km2 Urban Singapore Hong Kong Suburban 500 The Netherlands Rural 30 Estonia Sweden

13 Network unit assumptions on roll-out The network units are constructed with the following characteristics: (note: rounding errors appear in table) Area type # Subs./ km2 # Subs./ Network unit Average area reach (km2) Network unit Radius (km2) Urban Suburban Rural # needed Component Urban Suburban Rural APs UAPs SDR Handsets (add. inv.)

14 Eurolandia assumptions and network units A fictive European country, Eurolandia, has been constructed for the financial simulations of a 4G network Population: Population figure is the average of France, Germany, Italy, Netherlands, Spain, Sweden & UK populations 50 % are modelled to live in urban areas 35 % are modelled to live in suburban areas 15 % are modelled to live in rural areas Network Units needed to cover Eurolandia population: Urban NUs needed 232 Suburban NUs needed 325 Rural NUs needed

15 Assumptions - CAPEX APs & UAPs Through usage of mesh networking capabilities fewer APs and UAPs will be needed, leading to lower equipment costs per subscriber 10% replacement investments annually for UAPs and APs are estimated Installation costs are not yet included in the model SDR Handsets Additional cost for a SDR Handset with mesh networking capabilities is estimated to maximum 160 The 160 /subscriber are considered operator investment costs in the model The additional handset cost will most likely have to be subsidized by the operator in order to acquire subscribers With increased intelligence in the handsets, handsets adjust depending on which technology the base station it temporarily communicates with uses 15

16 Assumptions - CAPEX Equipment investment costs are expected to decrease rapidly Starting cost estimates (year 0) for network equipment: UAP = /unit AP = /unit SDR = 160 /handset An additional 100 per subscriber in acquisition cost, same during whole period Cost reductions during period according to figures to the right Network Coverage 36% coverage in year 0 64% in year 1 83% coverage in year 2 90% coverage in year Year UAP prices, Estimation Year AP price, Estimate Year Cost for handset SDR capabilities, Estimation 16

17 Assumptions - OPEX Data Backhaul Data backhaul costs are estimated to be 700 per site per month Only UAPs are assumed to have backhaul connections, APs are considered to be repeaters Site Rent Annual rental cost for AP and UAP sites are assumed to be 3000 in Urban Areas (or 250 /month) 1500 in Suburban Areas 1000 in Rural Areas Rental costs include electricity. Urban area cost figures are based on research made by Björkdahl & Bohlin (2004) Maintenance Each maintenance personnel is estimated to handle service of 50 APs/UAPs Each maintenance personnel is estimated to cost annually (including material, salary, vehicle, etc.) Maintenance cost figures are based on research made by Björkdahl & Bohlin (2004) 17

18 Assumptions - OPEX Marketing Costs Marketing costs are assumed to be ~1.9 per inhabitant in country/operator/year The figure is based on advertising figures for telecom operators in Sweden in 2003 & 2004 (not including customer acquisition costs!) Administrative Costs Administration costs are assumed to be 10% of other costs The low-cost telecom company Tele2 is used as a benchmark. Tele2 had administrative expenses equivalent to 10% of other costs in 2003 and 9% in 2002 (according to annual income statements) 18

19 Scenario assumptions - economic and sociological conditions Favour the take up of services Scenario 1: Smooth Development Main scenarios Economic conditions Scenario 3: Constant Change Scenario 2: Economic stagnation Inhibit take the up of services Favourable Social/ cultural/ political conditions Unfavourable 19

20 Assumptions - CAPEX The network is built in 4 years in all scenarios (year 0-3) All UAPs and APs installed first 4 years (covering 90% of the population) Investments in mesh capable handsets are made in line with customer diffusion each year Replacement investments (10% annually) are made each year Differences in maximum diffusion levels 90% in Scenario 1 50% in scenario 2 75% in scenario 3 Differences in starting years Scenario 1 starts 2010 Scenario 2 starts % Scenario 3 starts % For easy comparison, scenarios have been set to base line, Year 0-Year 11 60% 40% 20% 0% Scenario 1 - Smooth Development (High diffusion rate) Scenario 2 - Economic Stagnation (Low diffusion rate) Scenario 3 - Constant Change (Medium Diffusion rate) 20

21 Assumptions - scenarios impact on subscriber diffusion Diffusion more rapid in urban areas Slowest in rural areas Overall diffusion reaches 90%, 50% & 75% respectively in year % 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Subscriber diffusion Scenario Diffusion - overall Diffusion - Suburban areas Diffusion - Urban areas Diffusion - Rural areas Subscriber diffusion Scenario 2 Subscriber diffusion Scenario 3 60% 90% 50% 80% 70% 40% 60% 30% 50% 40% 20% 30% 10% 20% 10% 0% 0% Diffusion - overall Diffusion - Urban areas Diffusion - overall Diffusion - Urban areas Diffusion - Suburban areas Diffusion - Rural areas Diffusion - Suburban areas Diffusion - Rural areas 21

22 Results - CAPEX development for Scenarios 1,2 & 3 Initial network investments in year 0-3 Highest number of users added in years 5-7 Replacement costs significant late in period CAPEX development Scenario Total CAPEX (network, offset) Total CAPEX (handsets) CAPEX (Replacement) CAPEX development Scenario CAPEX development Scenario Total CAPEX (network, offset) Total CAPEX (handsets) CAPEX (Replacement) Total CAPEX (network, offset) Total CAPEX (handsets) CAPEX (Replacement) 22

23 Results - OPEX development for scenarios 1,2 & 3 Total monthly OPEX/subscriber curve bathtub-shaped Initial OPEX/subscriber very high in scenario 2 due to low number of subscribers in first years Total monthly OPEX per subscriber Scenario 1 - Smooth Development (High diffusion rate) Scenario 2 - Economic Stagnation (Low diffusion rate) Scenario 3 - Constant Change (Medium Diffusion rate) Total yearly OPEX/subscriber Follows same pattern as monthly OPEX/subscriber Total yearly OPEX per subscriber Scenario 1 - Smooth Development (High diffusion rate) Scenario 2 - Economic Stagnation (Low diffusion rate) Scenario 3 - Constant Change (Medium Diffusion rate) 23

24 Results - CAPEX & OPEX comparisons Accumulated CAPEX in each scenario Scenario figures similar first 4 years (build-out phase) Scenario 1 highest (due to high number of subs.) 0 Accumulated CAPEX Scenario 1 - Smooth Development (High diffusion rate) Scenario 2 - Economic Stagnation (Low diffusion rate) Scenario 3 - Constant Change (Medium Diffusion rate) Billion Accumulated OPEX Scenario 1 - Smooth Development (High diffusion rate) Scenario 2 - Economic Stagnation (Low diffusion rate) Scenario 3 - Constant Change (Medium Diffusion rate) Accumulated OPEX in each scenario Similar to CAPEX developments 24

25 Results and assumptions for ARPU calculations Interest rate used to discount figures: 12% Interest rates are currently very low (Euroswap, STIBOR, etc. 2-3%) A risk premium of 9-10% has been added Both CAPEX and OPEX have been discounted to NPV NPV has been annualized to calculate ARPU levels needed Monthly ARPU needed to cover CAPEX+OPEX 15.8 in Scenario in Scenario in Scenario Monthly ARPU needed Scenario 1 Scenario 2 Scenario 3 25

26 Sensitivity analysis Different cost development scenarios Three different price scenarios have been simulated Base scenario: High starting cost, low cost reduction Sensitivity check scenarios: Low starting cost, very slow cost reduction Medium starting costs, slow cost reduction UAPs UAPs cost development curves Low starting cost, very slow cost reduction Medium starting cost, slow cost reduction High starting cost, rapid cost reduction APs cost development curves SDR cost development curves Low starting cost, very slow cost reduction Low starting cost, very slow cost reduction APs Medium starting cost, slow cost reduction High starting cost, rapid cost reduction SDR Medium starting cost, slow cost reduction High starting cost, rapid cost reduction 26

27 Sensitivity analysis Changed cost curves and license cost Changed cost development patterns has small impact on monthly ARPU levels needed Maximum of 1.1 difference (Scenario 2) If considering both diffusion patterns and cost development patterns impact increases Maximum of 3.5 difference between lowest and highest ARPU needed ARPU needed with different cost curves Low starting cost, very slow cost reduction Medium starting cost, slow cost reduction Scenario 1 Scenario 2 Scenario 3 High starting cost, rapid cost reduction ARPU needed with added license fee* 652 /capita 600 /capita 500 /capita 400 /capita 300 /capita 200 /capita 100 /capita 0 /capita Scenario 1 Scenario 2 Scenario 3 Adding a license fee has strong effects on ARPU levels needed Impact depends on diffusion. strongest impact in scenario 2 A license fee of 652 per capita (3G license cost in the UK) increases the ARPU needed by 130% in scenario 1 200% in scenario 2 150% in scenario 3 *Based on High starting cost, rapid cost reduction dor components 27

28 Conclusions Our simulation indicates minimum ARPU levels of monthly needed for 4G business cases Depending on diffusion levels and equipment cost developments Excluding spectrum license costs Impact on 4G business cases indicated by sensitivity analyses: The cost of spectrum licenses High The 4G diffusion levels Medium Equipment cost development curves Low Implications is that spectrum management methods may be far more critical than market uptake for profitability 28

29 Appendix More details on FMS Scenarios 29

30 Scenarios assumptions on economic development Scenario SMOOTH 150% 2 ECONOMIC STAGNATION 100% 100% 90% 3 CHANGE Changing regions of prosperity 80% 110% 100% 90% EU Economic output Mean Disposable income

31 1 Smooth development EU economy provides growth and positive progress in development, but in a fair and managed way that brings prosperity across all 25 members and any new accessions Scenario SMOOTH 150% 100% Generating knowledge work through affordable re-training. Ubiquitous employment discovery, access and retraining. Control of everyday life -ease of access to convenience services. Support for extended family tribes. Ease of trade at low cost. Expansion of business reach geographically. Advanced support services at low cost for health, social requirements, education International co-ordinated working across the EU / globally EU Economic output Mean Disposable income

32 2 Stagnation economic stagnation with progressive deflation as in Japan from late 1980s, continuing to 2004, with enormous overhang of loans on assets, especially in private housing Scenario DECLINE economic stagnation 100% 75% 50% Belts tighten in the family and the firm. Disposable income and consumption restricted. Search for lower costs of doing business. Simple SME support. Seek security. Reducing outlays. Seeking employment either in conventional or in underground economies. Operating business at very low cost. Simple business services. Support for rural communities at low cost Simple lifestyle at minimal cost. Opting out. Support for trading and bartering in a non-enterprise based economy. Support for migrant workers overseas outside the EU including financial remittances. EU Economic output Mean Disposable income

33 3 Change Constant changes, up and down with ad hoc growth and recession, often in parallel in different geographic markets, but moderately positive overall with geographic and sector pockets of stability, and some of failure, all randomly distributed Scenario CHANGE 110% Changing regions of prosperity 100% 90% Access employment -find - retrain as required Simpler infrastructure and laws for SMEs Support to (re)build life in new regions Support for clustered communities of SMEs Conserve cash to live through downturn Build remote environment through constant contact with remote family, perhaps remote work and training Distributed business operations across EU EU Economic output Mean Disposable income