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      Data-driven quality improvement approach to reducing waste in manufacturing

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      The TQM Journal
      Emerald

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          Abstract

          Purpose

          Data-driven quality management systems, brought about by the implementation of digitisation and digital technologies, is an integral part of improving supply chain management performance. The purpose of this study is to determine a methodology to aid the implementation of digital technologies and digitisation of the supply chain to enable data-driven quality management and the reduction of waste from manufacturing processes.

          Design/methodology/approach

          Methodologies from both the quality management and data science disciplines were implemented together to test their effectiveness in digitalising a manufacturing process to improve supply chain management performance. The hybrid digitisation approach to process improvement (HyDAPI) methodology was developed using findings from the industrial use case.

          Findings

          Upon assessment of the existing methodologies, Six Sigma and CRISP-DM were found to be the most suitable process improvement and data mining methodologies, respectively. The case study revealed gaps in the implementation of both the Six Sigma and CRISP-DM methodologies in relation to digitisation of the manufacturing process.

          Practical implications

          Valuable practical learnings borne out of the implementation of these methodologies were used to develop the HyDAPI methodology. This methodology offers a pragmatic step by step approach for industrial practitioners to digitally transform their traditional manufacturing processes to enable data-driven quality management and improved supply chain management performance.

          Originality/value

          This study proposes the HyDAPI methodology that utilises key elements of the Six Sigma DMAIC and the CRISP-DM methodologies along with additions proposed by the author, to aid with the digitisation of manufacturing processes leading to data-driven quality management of operations within the supply chain.

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          Most cited references56

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          How to improve firm performance using big data analytics capability and business strategy alignment?

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            A ‘missing’ family of classical orthogonal polynomials

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              The link between Industry 4.0 and lean manufacturing: mapping current research and establishing a research agenda

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                Author and article information

                Contributors
                Journal
                The TQM Journal
                TQM
                Emerald
                1754-2731
                August 03 2021
                January 16 2023
                August 03 2021
                January 16 2023
                : 35
                : 1
                : 51-72
                Article
                10.1108/TQM-02-2021-0061
                8055fb07-486d-4cb1-abc9-fb899420c2cc
                © 2023

                https://www.emerald.com/insight/site-policies

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