Statistical thinking and its impact on operational performance in manufacturing companies: an empirical study
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Statistical thinking and its impact on operational performance in manufacturing companies: an empirical study Fabiane Letícia Lizarelli1 · Jiju Antony2 · José Carlos Toledo1 Accepted: 15 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Statistical thinking (ST) is a concept that has been discussed in the literature as principles and concepts disseminated in companies to conduct a statistical approach and it is often associated with using continuous improvement programs (CIPs), such as Six Sigma and Lean Six Sigma since the 1990s. There is a lack of empirical studies in the literature that address this topic concerning the impacts generated by ST in operational performance (OP) of industrial companies, as well as the relationships between ST and CIPs. The paper proposes and tests the hypothesis that the use of ST principles is positively associated with OP in manufacturing companies in the context of CIPs. The empirical research was conducted in a sample of 243 manufacturing companies and used structural equation modelling—partial least squares for data analysis. Positive and statistically significant relationships between ST and OP were observed. ST principles can reduce non-conformities, lower production costs and increase the stability of the process. The findings also support that CIPs have a positive effect on the use of ST principles and a positive effect on OP. This indicates the managerial importance of implementing CIPs, including Six Sigma, Lean Six Sigma, lean and total quality management, and the emphasis on developing mechanisms that operationalize the understanding and practice of the ST principles and statistical techniques for better OP in the manufacturing environment. Keywords Statistical thinking · Statistical techniques · Improvement programs · Operational performance · Structural equation modelling · Partial least squares · Manufacturing companies
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Fabiane Letícia Lizarelli [email protected] Jiju Antony [email protected]
1
Department of Production Engineering, Universidade Federal de São Carlos, São Carlos 13565-905, Brazil
2
Department of Business Management, Heriot-Watt University, Edinburgh EH14 4AS, UK
123
Annals of Operations Research
1 Introduction In the Quality Management (QM) domain (encompassing quality control and process improvement), Statistical Thinking (ST) can be defined as thought processes that recognize the existence of variation in all activities; work as interconnected processes; and the identification, characterization, quantification, control and reduction of variation in processes providing opportunities for improvement (Coleman 2013; Hoerl and Snee 2012; Snee 1990; Vining et al. 2016). ST is the vanguard of QM literature (Coleman 2013; Goh 2015; Grigg and Walls 2007; Pfannkuch and Wild 2004) and is widespread in the QM field, which focuses on systematic approaches to improve processes (Pfannkuch and Wild 2004). ST is the strategic aspect of statistics approach, the understanding of the reasons for using statisti
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