Open AccessMaintenance Strategy Selection in Spinning Mills Industry Using Fuzzy AHP
Author(s) -
Gabriel Sianturi,
Agus Riyanto,
Racka Maulana
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/879/1/012171
Subject(s) - analytic hierarchy process , predictive maintenance , reliability (semiconductor) , reliability engineering , quality (philosophy) , fuzzy logic , preventive maintenance , selection (genetic algorithm) , rank (graph theory) , optimal maintenance , total productive maintenance , operations research , production (economics) , process (computing) , multiple criteria decision analysis , corrective maintenance , computer science , risk analysis (engineering) , engineering , mathematics , business , artificial intelligence , economics , power (physics) , philosophy , physics , combinatorics , epistemology , quantum mechanics , macroeconomics , operating system
The aim of this paper is to develop a multi-criteria decision making based on the Fuzzy Analytical Hierarchy Process (Fuzzy AHP) to select the best maintenance strategy for a spinning mill industry. The maintenance process can enhance reliability, quality of the products, cost, and other aspects. Therefore, the selection of appropriate maintenance strategies is a critical issue for manufacturers. Fuzzy AHP approach is proposed as the selection problem includes uncertainties and difficulty in evaluating alternatives and criteria with definite expressions. The process of decision making involves the comparison of three alternatives of feasible maintenance strategy which are corrective maintenance, periodic maintenance, and predictive maintenance. Each alternative is evaluated against criteria according to the priorities of the decision-makers. The criteria are feasibility, cost, reliability, safety and production quality. The result shows that reliability is the most important criterion with the weight of 0.309, followed by safety (0.235), quality (0.193), cost (0.190), and feasibility (0.074). Periodic maintenance has the highest alternative with a total score of 0.423 and it is ranked first, predictive maintenance is ranked second with a score of 0.355, and corrective maintenance has the lowest rank with a score of 0.222. According to the results, periodic maintenance is chosen as the best maintenance strategy. Finally, the proposed method is successfully applicable in maintenance strategy selection.