alexei a.gaivoronski ikt Økonomi1 provision of mobile data services: portfolio analysis norwegian...
DESCRIPTION
Alexei A.Gaivoronski IKT Økonomi3 Example of structural description of service provisionTRANSCRIPT
Alexei A.Gaivoronski
IKT Økonomi 1
Provision of mobile data services: portfolio analysis
Norwegian University of Science and Technology, Trondheim, Norway
Alexei A. Gaivoronski
Alexei A.Gaivoronski
IKT Økonomi 2
Design of composite service
• Telecommunications• IT services • ....
Alexei A.Gaivoronski
IKT Økonomi 3
Example of structural description of service provision
Alexei A.Gaivoronski
IKT Økonomi 4
Different constellations of roles
Alexei A.Gaivoronski
IKT Økonomi 5
Service architecture
risk
return
feasible set
R
x
eff icient f rontier
x0x1
x2
risk
return
feasi ble set
R
x
efficient f rontier
x0x1
x2
risk
return
feasi ble set
R
x
eff icient f ron tier
x0x1
x2
risk
return
feasible set
R
x
eff icien t frontier
x0x1
x2
risk
re turn
feasib le set
R
x
efficient frontier
x0x1
x2
risk
re turn
feasi ble set
R
x
eff icient f rontier
x0x1
x2
risk
return
feasible se t
R
x
efficient f rontier
x0x1
x2
risk
re turn
feasib le set
R
x
efficient frontier
x0x1
x2
risk
return
feasible se t
R
x
efficient f rontier
x0x1
x2
risk
return
feasible se t
R
x
efficient f rontier
x0x1
x2
Alexei A.Gaivoronski
IKT Økonomi 6
Services, roles and actorsusers services Components, enablers, roles
SPICE
actors
Alexei A.Gaivoronski
IKT Økonomi 7
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
Alexei A.Gaivoronski
IKT Økonomi 8
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
Alexei A.Gaivoronski
IKT Økonomi 9
Risk/return tradeoff
risk
return
feasible set
R
x
efficient frontier
x0x1
x2
Alexei A.Gaivoronski
IKT Økonomi 10
Quantitative modelDescription of service
• Services consist of components which my be provided by different actorsN 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 vj
– Revenue sharing coefficients
– Actor who contribures with component i recieves revenue
j 1j , . . , Nj
j 1j , . . , Nj
ijv i
Alexei A.Gaivoronski
IKT Økonomi 11
Description of actors• Actors assume roles by providing service components• This incurs costs and brings revenue
K actors indexed by kcik – unit provision costs for actor k providing component i
Wik – provision capability of component i by actor k
xijk – the portion of provision capability for component i of actor k dedicated to participation in provision of service j.
Profit model for actor kxijkWik - the volume of provision of component i dedicated by actor k to
service j
Alexei A.Gaivoronski
IKT Økonomi 12
Profit model for actor k
– xijkWik/λij- volume of service j in which the actor k participates
– vjxijkWki/λij - the total revenue from this service
– vjxijkWkiγij/λij - the part of the revenue which goes to actor k
– Profit of actor k:
k j 1
M
v jx ijkW ik ij
ij x ijkc ikWik
j 1
M
x ijkW ikc ikv j ij
cik ij 1
Alexei A.Gaivoronski
IKT Økonomi 13
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
i W ic i j 1
M
x ijv j ij
ci ij 1
r i j 1
M
x ijv j ij
ci ij 1
x i x i1, . . . , x iM
Alexei A.Gaivoronski
IKT Økonomi 14
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
r ij v j ij
ci ij 1
ij ijE v j
ci ij 1
r i j 1
M
ijx ij j 1
M
x ij ijE v j
ci ij 1
Alexei A.Gaivoronski
IKT Økonomi 15
Efficient service portfolios
• Problem to solve for computing eficient frontier
minx StDev2 j 1
M
x ijv j ij
ci ij 1
j 1
M
x ij ijE v j
ci ij 1
j 1
M
x ij 1, x ij 0
Alexei A.Gaivoronski
IKT Økonomi 16
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
Alexei A.Gaivoronski
IKT Økonomi 17
Coordinator (service provider) problem
Paper is available on Edition 1 of the model set
Alexei A.Gaivoronski
IKT Økonomi 18
Architecture of the DSS prototype
Mathematicalmodel
Top level algorithmsScenario generation
Postprocessing
Problem solvers
Data and userinterface
Data User interaction
Results presentation
Excel MATLAB
XPRESS
SQGCPLEX
results
dataUser intervention
Service modelDetailed service
structure, resources
Service description
Alexei A.Gaivoronski
IKT Økonomi 19
Screenshot 1 of demo of DSS prototype
Alexei A.Gaivoronski
IKT Økonomi 20
Screenshot 2 of demo of DSS prototype
Alexei A.Gaivoronski
IKT Økonomi 21
Example: business person on the move
Alexei A.Gaivoronski
IKT Økonomi 22
Risk/performance preferences
0.05
0.1
0.15
0.2
0.25
0.3
0 0.2 0.4 0.6 0.8
risk
prof
it
Alexei A.Gaivoronski
IKT Økonomi 23
Market shares
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8
risk
plat
form
ser
vice
s
Alexei A.Gaivoronski
IKT Økonomi 24
Price competition
0
0.2
0.4
0.6
0.8
1
0.18 0.2 0.22 0.24 0.26 0.28 0.3 0.32 0.34
risk
plat
form
ser
vice
s
-10%-5%+510
Alexei A.Gaivoronski
IKT Økonomi 25
Summary
• Service design and planning constitutes a rich source of optimization problems under uncertainty
• They are not necessarily linear but they are worth solving