Philipp Loacker, Siegfried Pöchtrager, Christian Fikar, Wolfgang Grenzfurtner
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In addition, a data-ecosystem was developed which illustrates a continuous improvement feedback loop and serves as a role model for other applications in the food processing industry. The approach was applied to a real-case study in the processed cheese industry.</p><!--/ Abstract__block -->\n<h3>Findings</h3>\n<p>The findings revealed practical and conceptional contributions which can be used to continuously improve quality management (QM) in food processing. Thereby, the developed data-ecosystem supports production and QM in the decision-making processes. The findings of the analysis are a valuable basis to enhance operational processes, aiming to prevent quality deviations and non-conforming finished products.</p><!--/ Abstract__block -->\n<h3>Originality/value</h3>\n<p>Process mining is still rarely used in the food industry. Thereby, the proposed method helps to identify tacit knowledge in the food processing industry, which was shown by the framework for the preparation of event logs and the data ecosystem.</p><!--/ Abstract__block -->","PeriodicalId":47944,"journal":{"name":"International Journal of Productivity and Performance Management","volume":"10 1","pages":""},"PeriodicalIF":3.6000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Process mining-enhanced quality management in food processing industries\",\"authors\":\"Philipp Loacker, Siegfried Pöchtrager, Christian Fikar, Wolfgang Grenzfurtner\",\"doi\":\"10.1108/ijppm-06-2024-0377\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3>Purpose</h3>\\n<p>The purpose of this study is to present a methodical procedure on how to prepare event logs and analyse them through process mining, statistics and visualisations. 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Process mining-enhanced quality management in food processing industries
Purpose
The purpose of this study is to present a methodical procedure on how to prepare event logs and analyse them through process mining, statistics and visualisations. The aim is to derive roots and patterns of quality deviations and non-conforming finished products as well as best practice facilitating employee training in the food processing industry. Thereby, a key focus is on recognising tacit knowledge hidden in event logs to improve quality processes.
Design/methodology/approach
This study applied process mining to detect root causes of quality deviations in operational process of food production. In addition, a data-ecosystem was developed which illustrates a continuous improvement feedback loop and serves as a role model for other applications in the food processing industry. The approach was applied to a real-case study in the processed cheese industry.
Findings
The findings revealed practical and conceptional contributions which can be used to continuously improve quality management (QM) in food processing. Thereby, the developed data-ecosystem supports production and QM in the decision-making processes. The findings of the analysis are a valuable basis to enhance operational processes, aiming to prevent quality deviations and non-conforming finished products.
Originality/value
Process mining is still rarely used in the food industry. Thereby, the proposed method helps to identify tacit knowledge in the food processing industry, which was shown by the framework for the preparation of event logs and the data ecosystem.
期刊介绍:
■Organisational design and methods ■Performance management ■Performance measurement tools and techniques ■Process analysis, engineering and re-engineering ■Quality and business excellence management Articles can address these topics theoretically or empirically through either a descriptive or critical approach. The co-Editors support articles that significantly bring new knowledge to the area both for academics and practitioners. The material for publication in IJPPM should be written in a manner which makes it accessible to its entire wide-ranging readership. Submissions of highly technical or mathematically-oriented papers are discouraged.