Pusan National University Researchers Identify Key Barriers Hindering Data-Driven Smart Manufacturing Adoption
A comprehensive set of issues, covering different aspects of manufacturing data analytics, can help manufacturers transition to smart manufacturing
The MDA process typically involves five main steps: data preparation, data analysis, evaluation and interpretation of results, and implementation of results into manufacturing systems. Unique issues can arise at each of these steps. Additionally, there are broader issues related to technological, organizational, and environmental (TOE) contexts.
To address these issues, a research team led by Assistant Professor
The team first started by identifying relevant literature from the SCOPUS database. They identified 35 papers that addressed various issues related to MDA implementation. By systematically reviewing these papers, they identified a comprehensive set of 29 issues, grouped into 9 categories, each mapped to the relevant TOE context and step of the MDA process. Of these, 26 issues are related to technological context, 11 to organizational context, and 4 to environmental context. The 9 categories of CISM reflect different aspects of the MDA process, from understanding the problem and preparing data, to identifying the knowledge gap between data scientists and domain experts, and aligning MDA models to real-world manufacturing systems.
To validate CISM, the research team applied it to three real-world case studies in the rubber manufacturing industry, focusing on optimizing recipe formulation and mixing processes to ensure consistent, high-quality production. The framework effectively captured all implementation challenges encountered during the projects, demonstrating its comprehensiveness and practical applicability.
The authors also highlight directions for future research: ranking the relative importance of each issue, exploring their relevance across different manufacturing contexts, and developing tailored strategies to address them.
"CISM can help manufacturers establish clear guidelines for identifying and prioritizing the issues that need to be proactively addressed to ensure effective MDA implementation," notes
Reference
Title of original paper: | Comprehensive issue identification for manufacturing data analytics implementation: Systematic literature review and case studies |
Journal: | Journal of Manufacturing Systems |
DOI: |
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SOURCE Pusan National University
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