Decision Modeling in Service Science
The purpose of this paper is to highlight the need for innovative decision modeling in the field of service science. Each step of the service journey through a service ecosystem is initiated by a decision to integrate resources among actors and engage in a service activity. Consequently, engagement decisions are the driving force of any service journey and decision models are the foundation of service-system models. Each engagement decision must be modeled and executed as joint, adaptive, stochastic and perhaps fuzzy decisions among all actors who are involved in the associated service activity. However, such models are sparse in the research literature, and the current emphasis on predictive analytics and data science seems to distract attention from their development. Three examples of service systems are provided in this paper to illustrate this conclusion.
- 5.Ferrario R, Guarino N, Janiesch C, Kiemes T, Oberle D, Probst F. Towards an ontological foundation of service science: the general service model. In: 10th international conference on Wirtschaftsinformatik. Zurich, Switzerland.Google Scholar
- 7.OMG (Object Management Group). Value delivery modeling language. Accessed 15 June 2018. http://www.omg.org/spec/VDML/1.0.
- 8.Qiu R. Service science: the foundations of service engineering and management. New York: Wiley; 2014.Google Scholar
- 9.Badinelli R. Modeling service systems. Business Expert Press; 2015.Google Scholar
- 12.Barile S. Management sistemico vitale. Torino: G. Giappichelli; 2009.Google Scholar
- 14.Badinelli RD. A stochastic model of resource allocation for service systems. Serv Sci. 2010;2(1):68–83.Google Scholar
- 18.Paulussen TO, Zöller A, Heinzl A, Braubach L, Pokahr A, Lamersdorf W. Agent-based patient scheduling in hospitals. In: Kirn S, Herzog O, Lockemann PC, Spaniol, O editors. Multiagent Engineering Theory and Applications in Enterprises. Heidelberg, Germany: Springer; 2006. PP. 255–276.Google Scholar