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Complexity in manufacturing systems and its measures: a literature review

Author

Listed:
  • Bugra Alkan
  • Daniel A. Vera
  • Mussawar Ahmad
  • Bilal Ahmad
  • Robert Harrison

Abstract

Complexity in manufacturing systems still remains a challenge and leads to operational issues and increased production cost. In this paper, drivers of complexity and typical symptoms of complex manufacturing systems are identified. A comprehensive review of studies published within the last two decades to assess manufacturing system complexity are presented. The key contributions of this review are: 1) a classification of complexity assessment methods based on perceived complexity symptoms; 2) a comprehensive review of assessment methods with cross-evaluation to identify appropriate use based on available data; 3) recommendations for the wider academic and industrial community, based on research trends identified in the literature, as to how complexity assessment should be addressed in the future. It is concluded that the assessment of complexity is necessary so that it can be controlled effectively, however the industry suffers from a lack of practical tools to support in this endeavour. [Received 23 December 2016; Revised 18 August, 14 October 2017; Accepted 22 October, 2017]

Suggested Citation

  • Bugra Alkan & Daniel A. Vera & Mussawar Ahmad & Bilal Ahmad & Robert Harrison, 2018. "Complexity in manufacturing systems and its measures: a literature review," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 12(1), pages 116-150.
    • Handle: RePEc:ids:eujine:v:12:y:2018:i:1:p:116-150
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    Citations

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    Cited by:

    1. Wu, Chia-Hung & Chou, Che-Wei & Chien, Chen-Fu & Lin, Yun-Siang, 2024. "Digital transformation in manufacturing industries: Effects of firm size, product innovation, and production type," Technological Forecasting and Social Change, Elsevier, vol. 207(C).
    2. Lorenzo L. González Romeo & Juan Bory Reyes & Jorge A. Rojas Ramírez, 2024. "Systemic and Cyber-Physical Methodology for the Implementation of Industry 4.0 in Mexican Automotive Manufacturing Companies," SN Operations Research Forum, Springer, vol. 5(3), pages 1-26, September.
    3. Hochkamp, Florian & Rabe, Markus, 2022. "Outlier detection in data mining: Exclusion of errors or loss of information?," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Jahn, Carlos & Blecker, Thorsten & Ringle, Christian M. (ed.), Changing Tides: The New Role of Resilience and Sustainability in Logistics and Supply Chain Management – Innovative Approaches for the Shift to a New , volume 33, pages 91-117, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    4. Wissuwa, Florian & Durach, Christian F. & Choi, Thomas Y., 2022. "Selecting resilient suppliers: Supplier complexity and buyer disruption," International Journal of Production Economics, Elsevier, vol. 253(C).
    5. Maria Richert & Marek Dudek, 2023. "Risk Mapping: Ranking and Analysis of Selected, Key Risk in Supply Chains," JRFM, MDPI, vol. 16(2), pages 1-30, January.

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