1. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 1 Stochastic optimization and modeling of network risk and uncertainty: the case of telecommunication services Alexei A. Gaivoronski Norwegian University of Science and Technology Joint work with Josip Zoric, Denis Becker, Adrian Werner, Paolo Pisciella

2. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 2 Risk adapted performance networks  Electric power generation and distribution  Gas production, transportation, dirstribution  Telecommunications and internet  Transportation 1.Hierarchical networks with nodes of different levels of complexity: from equipment to enterprises 2.Nodes designed to meet local risk adjusted performance targets locally 3.Network should satisfy risk/performance tradeoff globally 4.Inherent uncertainty

3. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 3 Quantitative evaluation of business models for collaborative service provision Work directions  Getting qualitative understanding of business models, input from qualitative part, SPICE scenarios, surveys  Development of quantitative models  Implementation in a prototype of decision support system  Testing on SPICE scenarios, cases  Deliverable on quantitative evaluation

4. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 4 Status Task 1.4, quantitative analysis of business models  The Edition 1 of the set of models for investment business analysis of collaborative service provision has been developed: top static view  Architecture of the prototype of decision support system for analysis of business models is selected  Parts of this prototype is under implementation

5. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 5 Work in progress  Edition 2 of the model set: service lifetime, different constellations of actors  Build up of the prototype of decision support system for business analysis  Analysis of SPICE scenarios using the model set  Analysis of possible business models using qualitative input from other participants  Further dissemination effort

6. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 6 Quantitative business models  What is it?  Well understood in theory of corporate finance and in business practice  BUT focus is on one single enerprise who selects industrial project or project portfolio  Identify and measure and commeasure all cash flows related to a given business activity  Give integrated assessment of cash flow/profit performance based on different business principles  Return on investment  NPV  Risk/performance tradeoff  Decision about business activity  Recent emphasis on risk control

7. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 7 Challenges to this view  Networked industrial environment  Different independent agents are contributing to the common goal being in complex relations of competition and collaboration  How all this functions in such networked environment?  Corporate finance theory needs further development for this case  Risk control issues  Good example: evaluation of business models in context of SPICE

8. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 8 Objective  Starting from theory of corporate finance and optimal decisions under uncertainty and risk develop methods and tools for quantitative evaluation of business models in networked environment  Utilize this methodology in SPICE context for evaluation of collaborative service provision on SPICE platform

9. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 9 Risk/performance networks  State of the art: different attempts but no universally accepted answers  Growing importance in different fields  Telecommunications  Supply chain management  Energy

10. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 10 Example of structural description of service provision

11. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 11 Different constellations of roles

12. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 12 Service architecture

13. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 13 Services, roles and actors users servicesComponents, enablers, roles SPICE actors

14. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 14 Economic requirements  Platform should be attractive for all actors  Actors should feel incentive to join service provision, that is they should want to join cooperative effort because they will benefit from it  Services should provide to actors a competitive source of profit  Risk/return considerations: risk that users will not accept the service as expected, cannibalizing, etc

15. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 15 Approach of modern financial theory  Actors participate in service(s) provision assuming roles and providing components for services  Quantify cash flow, profits and risks  Each actor will select tradeoff between profit and risk exposure according to its preferences  This will result in service portfolio for each actor  Coordination tools should assure that the actors will select on their own accord participation in service provision in required proportion

16. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 16 Risk/return tradeoff Nobel prise winning concept

17. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 17 Quantitative model Description of service  Services consist of components which my be provided by different actors N components indexed by i and M services indexed by j ij - share of component i in service j. Description of service through components:  Service generate revenue v j  Revenue sharing coefficients  Actor who contribures with component i recieves revenue

18. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 18 Description of actors  Actors assume roles by providing service components  This incurs costs and brings revenue K actors indexed by k c ik – unit provision costs for actor k providing component i W ik – provision capability of component i by actor k x ijk – the portion of provision capability for component i of actor k dedicated to participation in provision of service j. Profit model for actor k x ijk W ik - the volume of provision of component i dedicated by actor k to service j

19. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 19 Profit model for actor k  x ijk W ik /λ ij - volume of service j in which the actor k participates  v j x ijk W ki /λ ij - the total revenue from this service  v j x ijk W ki γ ij /λ ij - the part of the revenue which goes to actor k  Profit of actor k:

20. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 20 Profit model for actor k  Basic case: an actor provides only one component  Profit  Return  Portfolio viewpoint: an actor chooses portfolio of services to which contribute

21. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 21 Portfolio viewpoint  Return coefficients associated with participation in each service  expected return coefficients  expected return  Risk that actual return will be different from expected return or even become loss

22. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 22 Efficient service portfolios  Problem to solve for computing eficient frontier

23. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 23 Next level: quantitative coordination  What is necessary is that the whole service provision platform functions properly  And this means that different actors should independently make decisions to participate in different services which nevetherless will provide coordinated result.  Revenue sharing coefficients should be chosen in order to achieve this

24. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 24 Coordinator (service provider) problem Paper is available on Edition 1 of the model set

25. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 25 Architecture of the DSS prototype

26. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 26 Screenshot 1 of demo of DSS prototype

27. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 27 Screenshot 2 of demo of DSS prototype

28. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 28 Example: business person on the move

29. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 29 Risk/performance preferences

30. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 30 Market shares

31. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 31 Price competition

32. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 32 Summary  Modern theory of corporate finance and risk management together with optimization under uncertainty provides a foundation for quantitative analysis of risk/performance networks in the context of collaborative service provision

33. Alexei A. Gaivoronski IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007 33Conclusions  Traditional risk management paradigm should be augmented and developed further: noncommeasurable risks  Modern theory of corporate finance and risk management provides a foundation for quantitative analysis of risk/performance networks but much more work is needed  Many possibilities for stochastic programming approaches  Three components: Modern computing technology, off-shelf optimization software, custom algorithm design  It is possible to solve highly nonlinear and nonconvex problems in industrial quantities