Case-based reasoning for complexity management in Industry 4.0

• Published in: Journal of manufacturing technology management Vol. 31; no. 5; pp. 999 - 1021
• Main Authors: Schott, Peter, Lederer, Matthias, Eigner, Isabella, Bodendorf, Freimut
• Format: Journal Article
• Language: English
• Published: Bradford Emerald Publishing Limited 18.11.2020; Emerald Group Publishing Limited
• Subjects: Advanced manufacturing technologies; Complexity; Context; Digitization; Industrial applications; Industry 4.0; Information technology; Knowledge; Knowledge management; Knowledge representation; Literature reviews; Machinery; Manufacturing; Optimization; Process controls; Product development; Reasoning; Systematic review; Knowledge based systems; Manufacturing systems; Complexity
• ISSN: 1741-038X; 1758-7786
• DOI: 10.1108/JMTM-08-2018-0262

PurposeIncreasingly, dynamic market environments lead to growing complexity in manufacturing and pose a severe threat for the competitiveness of manufacturing companies. Systematic guidance to manage this complexity, especially in the context of Industry 4.0 and the therewith rising trends such as digitalization and data-driven production optimization, is lacking. To address this deficit a case-based reasoning (CBR) system for providing knowledge about managing complexity in Industry 4.0 is presented.Design/methodology/approachFirst, the explicit knowledge representation for managing complexity in IT-based manufacturing is introduced. Second, the CBR process step to retrieve knowledge from an artificially composed case base with in total 70 cases of data-based complexity management in the context of Industry 4.0 is set out. Third, knowledge transfer alongside several maturity levels of information technology capabilities of manufacturing systems for reuse in new problem scenarios is introduced.FindingsThe paper comprises the conceptual approach for designing a CBR system to support data-based complexity management in manufacturing systems. Furthermore, the appropriateness of the CBR system to provide applicable knowledge for reducing and managing complexity in corporate practice is shown.Research limitations/implicationsThe presented research results are evaluated in the course of an embedded single case study and may therefore lack generalizability. Future research to test and enhance the appropriateness of the developed CBR system will strengthen the research contribution.Originality/valueThe paper provides a novel approach to systematically support knowledge transfer for data-based complexity management by transferring the well-known and established methodology of CBR to the rising application domain of manufacturing systems in the context of Industry 4.0.