An evolving fuzzy inference system for extraction of rule set for planning a product–service strategy

Manufacturing enterprises are collaborating among each other in manufacturing service ecosystems (MSE) with the objective to compose and provision numerous product–services (P–S) on the market. However, many paramount processes outset much before the actual composition, like the strategy planning of those P–S. Such decisions are usually full of ambiguities with complex sets of decisional possibilities, which are extremely hard to encompass even within a decision support system. Thus, the aim of this article is to undergird the development of an effective decision support system (DSS) for solving the challenge of planning a P–S strategy within a MSE, as well to present and apply a relative novel fuzzy inference technique, in order to build the DSS in question. This is achieved by first designing the logical data model that conceptualizes the context of planning a P–S strategy within a MSE, secondly by designing the actual business intelligence (BI) sets of rules and thirdly to build a DSS and test its data. As the input data needed to plan a strategy are often intangible, without a clear delineation among classes (e.g. “Market_1 is more competitive than Market_2”), with more than just binary values that can also overlap among each other and can be expressed using human language, a fuzzy based inference system is used to build the BI rules set. The DSS provides answers to three central uncertainties in P–S strategy planning expressed in the article as performance questions.