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Evaluating flexibility in discrete manufacturing based on performance and efficiency

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  • Seebacher, Gottfried
  • Winkler, Herwig

Abstract

Recent economic developments indicated that greater flexibility in manufacturing is more important than ever and we consider flexibility as a key objective of many manufacturing systems. The ability to provide a certain degree of flexibility of production systems must be prioritized in future manufacturing. The purpose of this paper is to present an applicable approach for the evaluation of flexibility in discrete manufacturing. Therefore, we developed a two-dimensional model to capture the performance and the efficiency of batch production systems. As a result, to evaluate manufacturing flexibility, we calculate a coefficient of variation from the deviations of the production order lead times. In addition, we compute an efficiency coefficient that indicates whether increased flexibility results from the manufacturing performance or from production waste, such as excessive inventory levels and/or excess capacities. The current flexibility model shows how to determine the degree of manufacturing flexibility and how to illustrate different flexibility situations and any improvements in flexibility. Our evaluation approach is based on readily available data and it is applicable to any batch production system with discrete manufacturing processes. Finally, we provide a clearly arranged guideline for practitioners clarifying the evaluation procedure and the computational steps to evaluate the manufacturing flexibility. The practical feasibility and the application of the current approach are demonstrated by an example that shows exactly how to evaluate the manufacturing flexibility and how to measure an improvement of flexibility in business practice.

Suggested Citation

  • Seebacher, Gottfried & Winkler, Herwig, 2014. "Evaluating flexibility in discrete manufacturing based on performance and efficiency," International Journal of Production Economics, Elsevier, vol. 153(C), pages 340-351.
  • Handle: RePEc:eee:proeco:v:153:y:2014:i:c:p:340-351
    DOI: 10.1016/j.ijpe.2014.03.018
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    References listed on IDEAS

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    Cited by:

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    2. Jain, Vineet & Raj, Tilak, 2016. "Modeling and analysis of FMS performance variables by ISM, SEM and GTMA approach," International Journal of Production Economics, Elsevier, vol. 171(P1), pages 84-96.
    3. Sanjai Kumar Shukla & Sushil & Manoj Kumar Sharma, 2019. "Managerial Paradox Toward Flexibility: Emergent Views Using Thematic Analysis of Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 20(4), pages 349-370, December.
    4. Li, Xinyu & Gao, Liang, 2016. "An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem," International Journal of Production Economics, Elsevier, vol. 174(C), pages 93-110.
    5. Enrico Teich & Thorsten Claus, 2017. "Measurement of Load and Capacity Flexibility in Manufacturing," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(4), pages 291-302, December.
    6. Jiaqi Jiang & Yun Feng, 2023. "Optimal hedging in the presence of internal flexibility," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 4557-4571, October.
    7. Wei, Zelong & Song, Xi & Wang, Donghan, 2017. "Manufacturing flexibility, business model design, and firm performance," International Journal of Production Economics, Elsevier, vol. 193(C), pages 87-97.
    8. Seebacher, Gottfried & Winkler, Herwig, 2015. "A capability approach to evaluate supply chain flexibility," International Journal of Production Economics, Elsevier, vol. 167(C), pages 177-186.

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