Proceedings of the International Conference on Industrial Engineering and Operations Management Dubai, UAE, March 10-12, 2020 © IEOM Society International Optimal EWMA Chart for Monitoring Failure Rate Salah Haridy a Department of Industrial Engineering and Engineering Management, University of Sharjah, Sharjah 27272, United Arab Emirates. Benha Faculty of Engineering, Benha University, Benha, Egypt. a sharidy@sharjah.ac.ae Mohammad Shamsuzzaman b , Imad Alsyouf c , Hamdi Bashir d Department of Industrial Engineering and Engineering Management, University of Sharjah, Sharjah 27272, United Arab Emirates. b mshamsuzzaman@sharjah.ac.ae; c ialsyouf@sharjah.ac.ae; d hbashir@sharjah.ac.ae Ahmed Maged e Department of Systems Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong. Benha Faculty of Engineering, Benha University, Benha, Egypt. e amaged2-c@my.cityu.edu.hk Nadia Bhuiyan f Department of Mechanical and Industrial Engineering, Concordia University, Montreal, Quebec, Canada H3G 1M8 f nadia.bhuiyan@concordia.ca Abstract This research proposes an optimal Exponentially Weighted Moving Average (EWMA) control chart for monitoring the failure rate of buses in a transport company to improve the quality of service, avoid negative impacts and enhance the customers’ satisfaction. The charting parameters of the EWMA chart, including the weighting parameter and the control limit, are optimized to achieve the best detection effectiveness. The proposed control chart is compared with the optimal NP chart in terms of the Average Number of Failures (ANF) since the shift occurs until the control chart can detect it. Failure data were obtained from the company for the implementation of the control charts. The results of the comparative study reveal that the EWMA chart substantially outperforms the NP chart, especially for detecting small and moderate shifts. It is very beneficial for the company to use an effective monitoring tool to guarantee continuous improvement and a high standard of efficiency. Keywords Control chart, Average number of failures, Average time to signal, EWMA chart. 1. Introduction Exponentially Weighted Moving Average (EWMA) chart is one of the most powerful control charts to reduce the variation and improve the quality of manufacturing systems and service sectors. This chart is widely used to detect small and moderate shifts. To detect an upward p shifts, a statistic Ct is updated and plotted for the tth sample in an EWMA chart 0 0 1 0 ( ) (1 ) t t t C C d d C λ λ − = = − + − (1) 1575