Application of data mining for spare parts information in maintenance schedule: a case study U.C. Moharana, S.P. Sarmah and Pradeep Kumar Rathore Department of Industrial and Systems Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India Abstract Purpose – The purpose of this paper is to suggest a framework for extracting the sequential patterns of maintenance activities and related spare parts information from historical records of maintenance data with pre-defined support or threshold values. Design/methodology/approach – A data mining approach has been adopted for predicting the maintenance activity along with spare parts. It starts with a collection of spare parts and maintenance data, and then the development of sequential patterns followed by formation of frequent spare part groups, and finally, integration of sequential maintenance activities with the associated spare parts. Findings – This study suggests a framework for extracting the sequential patterns of maintenance activities from historical records of maintenance data with pre-defined support or threshold values. A rule-based approach is proposed in this paper to predict the occurrence of next maintenance activity along with the information of spare parts consumption for that maintenance activity. Research limitations/implications – Presented model can be extended for analyzing the failure maintenance activities and performing root cause analysis that can give more valuable suggestion to maintenance managers to take corrective actions prior to next occurrence of failures. In addition, the timestamp information can be utilized to prioritize the maintenance activity that is ignored in this study. Practical implications – The proposed model has a high potential for industrial applications and is validated through a case study. The study suggests that the model gives a better approach for selecting spare parts based on their similarity or correlation, considering their actual occurrence during maintenance activities. Apart from this, the clustering of spare parts also trains maintenance manager to learn about the dependency among the spares for group stocking and maintaining the parts availability during maintenance activities. Originality/value – This study has used the technique of data mining to find dependent spare parts itemset from the database of the company and developed the model for associated spare parts requirement for subsequent maintenance activity. Keywords Data mining, Case study, Maintenance management, Association rule, Sequential pattern Paper type Research paper 1. Introduction On many occasions, the sudden failure of one part of an equipment can lead to subsequent failures of other parts in the system. Ignoring this failure information may lead to an increase in downtime and the cost of next maintenance activity. One can prevent downtime by just replacing the failed part at the correct time. However, by acquiring this knowledge, a maintenance manager can take necessary actions to prevent the occurrence of similar issues in the future by maintaining a proper corrective maintenance schedule. It is observed that many industries face problems regarding the scheduling and predicting the sequence of maintenance activities along with the usage of parts during the corrective maintenance of their equipment. For example, in the case of open cast mining industry, heavy types of equipment such as bucket wheel excavator, spreader, tripper car, conveyors, etc., are employed in different areas of mining locations. Maintenance activities of this equipment in actual working locations are difficult in terms of predicting the sequence and requirement of different types of spare parts with their quantities. In such cases, the industry faces huge production downtime situations and also incurs an extra cost due to increased frequency of transporting spares from stores to maintenance locations. Therefore, there is a need to learn Journal of Manufacturing Technology Management Vol. 30 No. 7, 2019 pp. 1055-1072 © Emerald Publishing Limited 1741-038X DOI 10.1108/JMTM-09-2018-0303 Received 13 September 2018 Revised 28 December 2018 8 February 2019 21 February 2019 Accepted 22 February 2019 The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/1741-038X.htm 1055 Spare parts information in maintenance schedule