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A new class of information system architecture, decision-oriented service systems, is spreading more and more. Decision-oriented service systems provide services that support decisions in business processes and products based on the capabilities of cloud-computing environments. To pave the way for the creation of design methods of business processes and products based on decision-oriented service systems, this article introduces a capability-oriented approach. Starting from technological capabilities, more abstract operational and dynamic capabilities are created. The framework created is based on an integrated conceptualization of decision-oriented service systems that allows capturing synergetic effects. By creating the framework, the gap between the technological capabilities of technologies and the strategic goals of enterprises shall be narrowed.
Context: Organizations increasingly develop software in a distributed manner. The cloud provides an environment to create and maintain software-based products and services. Currently, it is unknown which software processes are suited for cloud-based development and what their effects in specific contexts are.
Objective: We aim at better understanding the software process applied to distributed software development using the cloud as development environment. We further aim at providing an instrument which helps project managers comparing different solution approaches and to adapt team processes to improve future project activities and outcomes.
Method: We provide a simulation model which helps analyzing different project parameters and their impact on projects performed in the cloud. To evaluate the simulation model, we conduct different analyses using a Scrumban process and data from a project executed in Finland and Spain. An extra adaptation of the simulation model for Scrum and Kanban was used to evaluate the suitability of the simulation model to cover further process models.
Results: A comparison of the real project data with the results obtaind from the different simulation runs shows the simulation producing results close to the real data, and we could successfully replicate a distributed software project. Furthermore, we could show that the simulation model is suitable to address further process models.
Conclusion: The simulator helps reproducing activities, developers, and events in the project, and it helps analyzing potential tradeoffs, e.g., regarding throughput, total time, project size, team size and work-in-progress limits. Furthermore, the simulation model supports project managers selecting the most suitable planning alternative thus supporting decision-making processes.