Conceptual Framework for Measuring Project Benefits Using Belief—Plausibility and Type 1 Fuzzy Inference System

Springer, Cham
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Recent Developments and the New Direction in Soft-Computing Foundations and Applications, 2021, 393, pp. 243-256
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We propose a combination of belief and plausibility (Bl–Pl) and Type 1 fuzzy inference system (FIS) methods to measure benefits realization in this reserach. This approach can help line managers trace the project outcomes and validate the benefits and return on investment. BI–Pl computations are centered around an expert’s belief as a focal element or a power set of a classical set. This is part of Type 1 FIS, which is embraced by concepts of partial belief and fuzzy sets due to the approximate reasoning of the experts and the fuzzy rule base system. The project’s output can be ranked based on the difference between Bl-Pl, while Type 1 FIS allows us to transform expert knowledge or experience and then trace the project benefits automatically. A commentary on various governing parameters in enterprise benefit management, expert classification and an illustrative example using belief and plausibility form an integral part of this Chapter.
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