An Application of Association Rule Mining in Total Productive Maintenance Strategy: An Analysis and Modelling in Wooden Door Manufacturing Industry

Abstract

One of the challenges in the implementation of Total Productive Maintenance (TPM) in the manufacturing industry is a slow managerial decision-making to respond the condition in the factory. This research investigates the answers of these challenges by analysing and modelling the equipment condition and the response of actions required in a wooden door manufacturing industry. TPM implementation in this company has deployed the Overall Equipment Effectiveness (OEE) measurement as an indicator of the equipment utilization and condition. Through an analysis and modelling of the OEE value obtained from the factory, the formulation of Association Rule Mining (ARM) aims to find a rule that shows the well computed relationship between measurable indicators of OEE with the response of action required to take in certain condition of machine utilization. Results obtained from ARM accelerate the decision to establish an appropriate TPM management strategy based on the rules. The generated dynamic rules form and facilitate the process of decision-making by related stakeholders. Furthermore, relying on these rules the action taken by the company induced to a higher reliable and increasing the effectiveness of response and efficiency of time and costs.

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