By Stuart Boardman, Senior Business Consultant, Business & IT Advisory, KPN Consulting and Ed Harrington, Senior Consulting Associate, Conexiam
The Open Group Open Platform 3.0™ (OP3.0) services often involve a complex network of interdependent parties. Each party has its own concept of the value it expects from the service. One consequence of this is that each party depends on the value other parties place on the service. If it’s not core business for one of them, its availability and reliability could be in doubt. So the others need to be aware of this and have some idea of how much that matters to them.
In a previous post, we used the analogy of an onion to model various degrees of relationship between parties. At a high level the onion looks like this:
Every player has their own version of this onion. Every player’s own perspective is from the middle of it. The complete set of players will be distributed across different layers of the onion depending on whose onion we are looking at.
In a short series of blogs, we’re going to use a concrete use-case to explore what various players’ onions look like. To understand that onion involves working from the middle out. We all know that you can’t peel an onion starting in the middle, so let’s not get hung up on the metaphor. It’s only useful in as far as it fits with our real business objective. In this case the objective is to have the best possible chance of understanding and then realizing the potential value of a service.
Defining and Realizing Value
Earlier this year, The Open Group published a set of Open Platform 3.0 use cases. One of these use cases (#15) considers the energy market ecosystem involved in smart charging of electric vehicles. The players in this use case include:
- The Vehicle User
- Supplier/Charging Operator(s)
- Distribution Service Operator (DSO).
- Electricity Bulk Generators
- Transmission (National Grid) Operator
- Local Government
The use case describes a scenario involving these players:
A local controller (a device – known in OP3.0 as part of the Internet of Things) controls one or more charging stations. The Charging Operator informs the vehicle (and possibly the Vehicle User) via the local controller how much capacity is available to it. If the battery is nearly full the vehicle can inform the local controller that it needs less capacity and this capacity can then be made available to other vehicles at other charging stations.
The Charging Operator determines the capacity to be made available on the basis of information provided by the DSO (maximum allowable capacity at that time), possibly combined with commercial information (e.g., current spot prices, predicted trends, flexibility agreements with vehicle-owners/customers where applicable). The DSO has predicted available capacity on the basis of currently predicted weather conditions and long-term usage patterns in the relevant area. The DSO is able to adapt to unexpected changes in real-time and restrict or increase the locally available capacity.
Value For The Various Parties
The Vehicle User
For the sake of making it interesting let’s say that the vehicle user is a taxi driver. For her, the value is primarily in being able to charge the vehicle at a convenient time, place, speed and cost. But the perception of what constitutes value in those categories may vary depending on whether she uses a public charging station or charges at home. In either case the service she uses is focused on the Supplier/Charging operator, because that is who she pays for the service. The bill includes generic DSO costs but the customer has no direct relationship with a DSO and is only really aware of them when maintenance is carried out. Factors like convenient time and place may bring Local Government into the picture, because they are often the party who make parking spaces for electric vehicles available.
Local government is then also responsible for policing the proper use of these spaces. The importance assigned by local government to making these facilities available is a question of policy balanced by cost/gain (licenses and parking fees). Policy is influenced by the economy, by the convictions of the councilors, by lobbyists (especially those connected with the DSO, Bulk Generators and Transmission Operators), by innovation and natural resources and by the attitude of the public towards electric vehicles, which in turn may be influenced by national government policy. In some countries (e.g. The Netherlands) there are tax incentives for the acquisition of electric cars. If this policy changes in a country, the number of electric vehicles could increase or decrease dramatically. Local government has a dependency on and formal relationship with the Supplier that manages the Charging Stations. The relationship with the DSO is indirect unless they have been partners in an initiative to promote electric vehicles.
Value for the DSO involves balancing its regulatory obligation to provide continuity of energy supply with the cost of investment to achieve that and with the public perception of the value of that service. The DSO also gains value in terms of reputation from investing in innovation and energy saving. That value is expressed in its own long-term future as an enterprise. The DSO, being very much the hub in this use case, is dependent on the Supplier and the Vehicle User (with the vehicle’s battery as proxy) to provide the information needed to ensure continuity – and of course on the Transmission Operator the Bulk Generators to provide power. It does not, however, have any direct relationship with any Bulk Generator or even necessarily know who they are or where they are located.
The Bulk Generator
The Bulk Generator has no direct involvement in this use case but has an indirect dependency on anything affecting the level of usage of electricity, as this affects the market price and long-term future of its product. So there is generic value (or anti-value) in the use case if it is widely implemented.
To be continued…
Those were the basics of the approach. There’s a lot more to be done before you can say you have a grip on value realization in such a scenario.
In the next blog, we’ll dive deeper into the use case, identify other relevant stakeholders and look at other dependencies that may influence value across the chain.
 Open Platform 3.0 refers to this as a “wider business ecosystem”. In fact such ecosystems exist for all kinds of services. We just happen to be focusing on this kind of service.
Stuart Boardman is a Senior Business Consultant with KPN Consulting where he leads the Enterprise Architecture practice and consults to clients on Cloud Computing, Enterprise Mobility and The Internet of Everything. He is Co-Chair of The Open Group Open Platform 3.0™ Forum and was Co-Chair of the Cloud Computing Work Group’s Security for the Cloud and SOA project and a founding member of both The Open Group Cloud Computing Work Group and The Open Group SOA Work Group. Stuart is the author of publications by KPN, the Information Security Platform (PvIB) in The Netherlands and of his previous employer, CGI as well as several Open Group white papers, guides and standards. He is a frequent speaker at conferences on the topics of Open Platform 3.0 and Identity.
Ed Harrington is a Senior Consulting Associate with Conexiam, a Calgary, Canada headquartered consultancy. He also heads his own consultancy, EPH Associates. Prior positions include Principle Consultant with Architecting the Enterprise where he provided TOGAF and other Enterprise Architecture (EA) discipline training and consultancy; EVP and COO for Model Driven Solutions, an EA, SOA and Model Driven Architecture Consulting and Software Development company; various positions for two UK based companies, Nexor and ICL and 18 years at General Electric in various marketing and financial management positions. Ed has been an active member of The Open Group since 2000 when the EMA became part of The Open Group and is past chair of various Open Group Forums (including past Vice Chair of the Architecture Forum). Ed is TOGAF® 9 certified.