An expert system prototype for inventory capacity planning: An approximate reasoning approach

Abstract

An approximate reasoning framework is suggested for the development of an expert system prototype to aid management in planning inventory capacities. The development is considered to be a stage that comes after the analysis of a stochastic model. Such a model would provide the requisite insight and knowledge about the inventory system under specific assumptions. As a consequence, the model builder(s) would act as expert(s). The restructuring process from the stochastic model into the approximate reasoning framework is described in a case study analysis for a Markovian production model. The stochastic model considers a relatively simplified production process: one machine, constant production rate, a compound Poisson demand process for the product together with the reliability feature comprising the machine failure process and the ensuing repair action. In this context, the authors propose an approximate reasoning framework and describe (1) the identification of the managerial decision-making rules, which usually contain uncertain (vague, ambiguous, fuzzy) linguistic terms; and (2) the specification of membership functions that represent the meaning of such linguistic terms within context-dependent domains of concern. They then define a new universal logic incorporating these rules and functions and apply it to inventory capacity planning. Two case examples and a simulation experiment consisting of 21 cases are summarized with a discussion of results.