Fuzzy set theory–based model for identifying the potential for improving process KPI in production logistics area
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Abstract
The high complexity of today´s manufacturing environment brings many problems with planning and managing, especially production, logistic and other key business processes. In many cases, it is quite complicated to identify the real causes of problems that enterprise faces or to decide which one of them should be solved as a first. Especially, in the case of large enterprises, it is quite complicated to access expertise among all departments and employed professionals in order to solve the problems in the most efficient way. The purpose of our fuzzy model is to provide a simple tool for easy identification of the most significant problems of observed processes that causes their low performance according to the measured values of their key performance indicators (KPIs). The model is based on data gained through the interviews with production managers, industry experts and other professionals, and it was verified by real data from one model company. The results are presented in the form of case study in this contribution.
Keywords: Production logistics, key performance indicators (KPI), productivity, problem identification, fuzzy set theory, process.
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