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

Jean Khalil, Ashraf Labib

Research output: Contribution to journalArticlepeer-review

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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.
Original languageEnglish
Pages (from-to)1020-1039
Number of pages20
JournalInternational Journal of Quality & Reliability Management
Issue number4
Early online date31 May 2021
Publication statusPublished - 7 Mar 2022


  • fuzzy logic modeling
  • decision support systems
  • industrial maintenance
  • cost optimization
  • maintenance engineering


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