A New Robust Multivariate EWMA Dispersion Control Chart for Individual Observations

Jimoh Olawale Ajadi, Inez Maria Zwetsloot and Kwok-Leung Tsui
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Jimoh Olawale Ajadi: Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Inez Maria Zwetsloot: Department of Systems Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong
Kwok-Leung Tsui: Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA 24061-0002, USA

Mathematics, 2021, vol. 9, issue 9, 1-18

Abstract: A multivariate control chart is proposed to detect changes in the process dispersion of multiple correlated quality characteristics. We focus on individual observations, where we monitor the data vector-by-vector rather than in (rational) subgroups. The proposed control chart is developed by applying the logarithm to the diagonal elements of the estimated covariance matrix. Then, this vector is incorporated in an exponentially weighted moving average (EWMA) statistic. This design makes the chart robust to non-normality in the underlying data. We compare the performance of the proposed control chart with popular alternatives. The simulation studies show that the proposed control chart outperforms the existing procedures when there is an overall decrease in the covariance matrix. In addition, the proposed chart is the most robust to changes in the data distribution, where we focus on small deviations which are difficult to detect. Finally, the compared control charts are applied to two case studies.

Keywords: individual observations; covariance matrix; non-normality; multivariate dispersion chart; EWMA (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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