On the optimization of maintenance storage cost in industry a fuzzy logic application

Purpose The purpose of this paper is to construct a fuzzy logic model that acts as a decision support system to minimize inventory-related costs in the field of industrial maintenance. Achieving a balance between the unavailability and over-storage of spare parts is a problem with potentially significant consequences. That significance increases proportionally with the ever-increasing challenge of reducing overall cost. Either scenario can result in substantial financial losses because of the interruption of production or the costs of tied-up capital, also called the solidification of capital. Moreover, there is that additional problem of the expiry of parts on the shelf. Design/methodology/approach The proposed approach relies on inputs from experts with consideration for incompleteness and inaccuracy. Two levels of decision are considered simultaneously. The first is whether a part should be stored or ordered when needed. The second involves comparing suppliers with their batch-size offers based on user-determined criteria. A mathematical model is developed in parallel for validation. Findings The results indicate that the fuzzy logic approach is accurate and satisfactory for this application and that it is advantageous because of its limited sensitivity to the inaccuracy and/or incompleteness of data. In addition, the approach is practical because it requires minimal user effort. Originality/value To the best of the authors' knowledge, the exploitation of fuzzy-logic altogether with limited sensitivity experts' inputs were never combined for the solution of this particular problem; however, this approach's positive impact is expected to be highly significant in solving a chronic problem in industry.

Automated summary

The purpose of this paper is to construct a fuzzy logic model that acts as a decision support system to minimize inventory-related costs in the field of industrial maintenance. The proposed approach relies on inputs from experts with consideration for incompleteness and inaccuracy, and involves two levels of decision making simultaneously. The results suggest that the fuzzy logic approach is accurate, satisfactory, and practical for this application, with limited sensitivity to data inaccuracies.