Skip to main content
Log in

Optimized skill configuration for the seru production system under an uncertain demand

  • S.I.: Scalable Optimization and Decision Making in OR
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

Owing to its high efficiency and flexibility, the seru production system (SPS), which originated in Japan, has attracted greater attention in management and academic studies. This research focuses on optimizing the configuration for implementing the SPS under an uncertain demand. The study is aimed at formulating a robust production system capable of effectively responding to stochastic demands. The primary issues are determining the amount of skill training required and matching workers with their corresponding skills. A stochastic optimization model is developed to minimize the total expected cost of the system, while considering the costs associated with training, staff shortage, and staff surplus. A heuristic algorithm is developed to solve this problem. Experimental results indicate that, compared to the full-skilled training strategy, appropriate partial skill training (such as the long chain skill training strategy) can yield greater benefits. The total cost and amount of skill training increase with growing differences in the product mix compositions, demand fluctuations, and number of product types. Moreover, the skill level of workers increases with a decrease in training cost and an increase in staff shortage and surplus costs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Alhadi, G., Kacem, I., Laroche, P., & Osman, I. M. (2020). Approximation algorithms for minimizing the maximum lateness and makespan on parallel machines. Annals of Operations Research, 285(1–2), 369–395.

    Article  Google Scholar 

  • Atlason, J., Epelman, M. A., & Henderson, S. G. (2004). Call center staffing with simulation and cutting plane methods. Annals of Operations Research, 127(1–4), 333–358.

    Article  Google Scholar 

  • Bassamboo, A., Randhawa, R. S., & Jan, A. V. M. (2010). Optimal flexibility configurations in newsvendor networks: Going beyond chaining and pairing. Management Science, 56(8), 1285–1303.

    Article  Google Scholar 

  • Bhulai, S., Yuan, T., Heidergott, B. F., & van der Laan, D. A. (2012). Optimal balanced control for call centers. Annals of Operations Research, 201(1), 39–62.

    Article  Google Scholar 

  • Deng, T., & Shen, Z. J. M. (2015). Process flexibility design in unbalanced networks. IEEE Engineering Management Review, 43(1), 62–72.

    Article  Google Scholar 

  • Henao, C. A., Ferrer, J. C., Muoz, J. C., & Vera, J. (2016). Multiskilling with closed chains in a service industry: A robust optimization approach. International Journal of Production Economics, 179, 166–178.

    Article  Google Scholar 

  • Huchzermeier, A., & Loch, C. H. (2001). Project management under risk: Using the real options approach to evaluate flexibility in rd. Management Science, 47(1), 85–101.

    Article  Google Scholar 

  • Jordan, W. C., & Graves, S. C. (1995). Principles on the benefits of manufacturing process flexibility. Management Science, 41(4), 577–594.

    Article  Google Scholar 

  • Kaku, I., Gong, J., Tang, J., & Yin, Y. (2009). Modeling and numerical analysis of line-cell conversion problems. International Journal of Production Research, 47(8), 2055–2078.

    Article  Google Scholar 

  • Listes, O., & Dekker, R. (2005). A scenario aggregation-based approach for determining a robust airline fleet composition for dynamic capacity allocation. Transportation Science, 39(3), 367–382.

    Article  Google Scholar 

  • Liu, C., Li, W., Lian, J., & Yin, Y. (2012). Reconfiguration of assembly systems: From conveyor assembly line to serus. Journal of Manufacturing Systems, 31(3), 312–325.

    Article  Google Scholar 

  • Liu, C., Yang, N., Li, W., Lian, J., Evans, S., & Yin, Y. (2013). Training and assignment of multi-skilled workers for implementing seru production systems. The International Journal of Advanced Manufacturing Technology, 69(5), 937–959.

    Article  Google Scholar 

  • Liu, C., Dang, F., Li, W., Lian, J., Evans, S., & Yin, Y. (2015). Production planning of multi-stage multi-option seru production systems with sustainable measures. Journal of Cleaner Production, 105, 285–299.

    Article  Google Scholar 

  • Long, Y., Lee, L. H., & Chew, E. P. (2012). The sample average approximation method for empty container repositioning with uncertainties. European Journal of Operational Research, 222(1), 65–75.

    Article  Google Scholar 

  • Miyake, D. I. (2006). The shift from belt conveyor line to work-cell based assembly systems to cope with increasing demand variation in japanese industries. Automotive Technology and Management, 6(4), 419–439.

    Google Scholar 

  • Molleman, E. (1998). Variety and the requisite of selforganization. The International Journal of Organizational Analysis, 6(2), 109–131.

    Article  Google Scholar 

  • Mtze, T. (2014). Scheduling with few changes. European Journal of Operational Research, 236(1), 37–50.

    Article  Google Scholar 

  • Quinton, F., Hamaz, I., & Houssin, L. (2020). A mixed integer linear programming modelling for the flexible cyclic jobshop problem. Annals of Operations Research, 285(1–2), 335–352.

    Article  Google Scholar 

  • Rockafellar, R. T., & Wets, J. B. (1991). Scenarios and policy aggregation in optimization under uncertainty. Mathematics of Operations Research, 16(1), 119–147.

    Article  Google Scholar 

  • Roth, A., Singhal, J., Singhal, K., & Tang, C. S. (2016). Knowledge creation and dissemination in operations and supply chain management. Production and Operations Management, 25(9), 1473–1488.

    Article  Google Scholar 

  • Schulze, T., Grothey, A., & McKinnon, K. (2017). A stabilised scenario decomposition algorithm applied to stochastic unit commitment problems. European Journal of Operational Research, 261(1), 247–259.

    Article  Google Scholar 

  • Shinohara, T. (1995). Shocking news of the removal of conveyor systems: Single-worker seru production system. Nikkei Mech: Tech. rep.

    Google Scholar 

  • Slomp, J., & Molleman, E. (2002). Cross-training policies and team performance. International Journal of Production Research, 40(5), 1193–1219.

    Article  Google Scholar 

  • Stecke, K. E., Yin, Y., Kaku, I., & Murase, Y. (2012). Seru: The organizational extension of jit for a super-talent factory. International Journal of Strategic Decision Sciences (IJSDS), 3(1), 106–119.

    Article  Google Scholar 

  • Tomlin, B. (2006). On the value of mitigation and contingency strategies for managing supply chain disruption risks. Management Science, 52(5), 639–657.

    Article  Google Scholar 

  • Wallace, R. B., & Whitt, W. (2005). A staffing algorithm for call centers with skill-based routing. Manufacturing & Service Operations Management, 7(4), 276–294.

    Article  Google Scholar 

  • Wang, Y., & Tang, J. (2018). Cost and service-level-based model for a seru production system formation problem with uncertain demand. Journal of Systems Science and Systems Engineering, 27(4), 519–537. https://doi.org/10.1007/s11518-018-5379-3. identifier: 5379.

    Article  Google Scholar 

  • Yin, Y., Kaku, I., & Stecke, K. E. (2008). The evolution of seru production systems throughout canon. Operations Management Education Review, 2, 27–40.

    Google Scholar 

  • Yin, Y., Stecke, K. E., Swink, M., & Kaku, I. (2017). Lessons from seru production on manufacturing competitively in a high cost environment. Journal of Operations Management, 49–51, 67–76.

    Article  Google Scholar 

  • Yin, Y., Stecke, K. E., Li, D. (2018). The evolution of production systems from industry 2.0 through industry 4.0. International Journal of Production Research, 56(1–2), 848–861.

  • Ying, K. C., Ying, K. C., & Tsai, Y. J. (2017). Minimising total cost for training and assigning multiskilled workers in seru production systems. International Journal of Production Research, 55(10), 2978–2989.

    Article  Google Scholar 

  • Yu, Y., Gong, J., Tang, J., Yin, Y., & Kaku, I. (2012). How to carry out assembly line-cell conversion? A discussion based on factor analysis of system performance improvements. International Journal of Production Research, 50(18), 5259–5280.

    Article  Google Scholar 

  • Yu, Y., Tang, J., Sun, W., Yin, Y., & Kaku, I. (2013). Reducing worker(s) by converting assembly line into a pure cell system. International Journal of Production Economics, 145(2), 799–806.

    Article  Google Scholar 

  • Yu, Y., Tang, J., Gong, J., Yin, Y., & Kaku, I. (2014). Mathematical analysis and solutions for multi-objective line-cell conversion problem. European Journal of Operational Research, 236(2), 774–786.

    Article  Google Scholar 

  • Yu, Y., Sun, W., Tang, J., Kaku, I., Wang, J., & Wang, J. (2017). Line-seru conversion towards reducing worker(s) without increasing makespan: Models, exact and meta-heuristic solutions. International Journal of Production Research, 55(10), 2990–3007.

    Article  Google Scholar 

  • Yu, Y., Sun, W., Tang, J., & Wang, J. (2017). Line-hybrid seru system conversion: Models, complexities, properties, solutions and insights. Computers & Industrial Engineering, 103, 282–299.

    Article  Google Scholar 

  • Ylmaz, F. (2020). Operational strategies for seru production system: A bi-objective optimisation model and solution methods. International Journal of Production Research, 58(11), 3195–3219.

    Article  Google Scholar 

Download references

Acknowledgements

The paper is financially supported by National Natural Science Foundation of China (Project 71420107028, Project 71901119).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jiafu Tang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Y., Tang, J. Optimized skill configuration for the seru production system under an uncertain demand. Ann Oper Res 316, 445–465 (2022). https://doi.org/10.1007/s10479-020-03805-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-020-03805-3

Keywords

Navigation