Skip to main content

Advertisement

SpringerLink
Log in

How SERU production system improves manufacturing flexibility and firm performance: an empirical study in China

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

Abstract

SERU production system (SERU) demonstrates more manufacturing flexibility in response to volatile market demands. Although many firms have implemented SERU successfully, little research on manufacturing flexibility in SERU exists, especially empirical evidence in the context of Chinese firms. Manufacturing flexibility considers two major dimensions in SERU, namely, product mix flexibility and volume flexibility. We propose a contingency-based framework to explore the source factors of manufacturing flexibility and their impact on firm performance. We examine the moderating effects of both multi-skilled worker turnover and industry type. Using a total of 357 samples from China, we test the hypotheses with structural equation modeling. Our results reveal that both SERU reconfiguration and multi-skilled worker involvement are important sources of product mix flexibility and volume flexibility, and the two kinds of flexibility subsequently improve firm performance. Multi-skilled worker involvement has a stronger impact than SERU reconfiguration on improving manufacturing flexibility. Both multi-skilled worker turnover and industry type are important moderators. We also conduct tests indicating that the mediation effects of manufacturing flexibility are moderated by multi-skilled worker turnover and industry type. Overall, the acquisition of manufacturing flexibility and its impact on performance in SERU are situation dependent.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Notes

  1. http://www.chinanews.com/cj/2016/12-19/8097919.shtml.

  2. We also examined the effect of capital structure as one of the control variables. However, its effect was not significant, and it did not bring significant change to our hypotheses testing. Therefore, we decided to remove it from our model.

References

  • Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120.

    Google Scholar 

  • Berry, W. L., & Cooper, M. C. (1999). Manufacturing flexibility: Methods for measuring the impact of product variety on performance in process industries. Journal of Operations Management, 17(2), 163–178.

    Google Scholar 

  • Braglia, M., & Petroni, A. (2000). Towards a taxonomy of search patterns of manufacturing flexibility in small and medium-sized firms. Omega, 28(2), 195–213.

    Google Scholar 

  • Browne, J., Dubois, D., Rathmill, K., Sethi, S., & Stecke, K. (1984). Classification of flexible manufacturing systems. The FMS Magazine, 2(2), 114–117.

    Google Scholar 

  • Buyukkaramikli, N. C., Bertrand, J. W. M., & Van Ooijen, H. P. G. (2013). Flexible hiring in a make to order system with parallel processing units. Annals of Operations Research, 209(1), 159–178.

    Google Scholar 

  • Chen, I. J., & Paulraj, A. (2004). Towards a theory of supply chain management: The constructs and measurement. Journal of Operations Management, 22(2), 119–150.

    Google Scholar 

  • D’Souza, D. E., & Williams, F. P. (2000). Toward a taxonomy of manufacturing flexibility dimensions. Journal of Operations Management, 18(5), 577–593.

    Google Scholar 

  • Fiegenbaum, A., & Karnani, A. (1991). Output flexibility—A competitive advantage for small firms. Strategic Management Journal, 12(2), 101–114.

    Google Scholar 

  • Fragapane, G., Ivanov, D., Peron, M., Sgarbossa, F., & Strandhagen, J. O. (2020). Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03526-7.

    Article  Google Scholar 

  • Gupta, D. (1993). On measurement and valuation of manufacturing flexibility. International Journal of Production Research, 31(12), 2947–2958.

    Google Scholar 

  • Gupta, Y. P., & Somers, T. M. (1996). Business strategy, manufacturing flexibility, and organizational performance relationships: A path analysis approach. Production and Operations Management, 5(3), 204–233.

    Google Scholar 

  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks, CA: Sage.

    Google Scholar 

  • Hallgren, M., & Olhager, J. (2009). Flexibility configurations: Empirical analysis of volume and product mix flexibility. Omega, 37(4), 746–756.

    Google Scholar 

  • Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York, NY: The Guilford Press.

    Google Scholar 

  • Hutchison, J., & Das, S. R. (2007). Examining a firm’s decisions with a contingency framework for manufacturing flexibility. International Journal of Operations & Production Management, 27(2), 159–180.

    Google Scholar 

  • Jack, E. P., & Raturi, A. (2002). Sources of volume flexibility and their impact on performance. Journal of Operations Management, 20(5), 519–548.

    Google Scholar 

  • Jain, A., Jain, P. K., Chan, F. T. S., & Singh, S. (2013). A review on manufacturing flexibility. International Journal of Production Research, 51(19), 5946–5970.

    Google Scholar 

  • Keil, M., Tan, B. C. Y., Wei, K. K., Saarinen, T., Tuunainen, V., & Wassenaar, A. (2000). A Cross-Cultural Study on Escalation of Commitment Behavior in Software Projects. MIS Quarterly, 24(2), 299–325.

    Google Scholar 

  • Koste, L. L., Malhotra, M. K., & Sharma, S. (2004). Measuring dimensions of manufacturing flexibility. Journal of Operations Management, 22(2), 171–196.

    Google Scholar 

  • Kumar, V. (1988). Measurement of loading and operations flexibility in flexible manufacturing systems: an information theoretic approach. Annals of Operations Research, 15(1), 65–80.

    Google Scholar 

  • Liu, C. G., Stecke, K. E., Lian, J., & Yin, Y. (2014). An implementation framework for seru production. International Transactions in Operational Research, 21(1), 1–19.

    Google Scholar 

  • Matta, R. D., Hsu, V. N., & Feng, C. X. (2001). Short-term capacity adjustment with offline production for a flexible manufacturing system under abnormal disturbances. Annals of Operations Research, 107(1–4), 83–100.

    Google Scholar 

  • Mendes, L., & Machado, J. (2015). Employees’ skills, manufacturing flexibility and performance: A structural equation modelling applied to the automotive industry. International Journal of Production Research, 53(13), 4087–4101.

    Google Scholar 

  • Mishra, R., Pundir, A. K., & Ganapathy, L. (2016). Conceptualizing sources, key concerns and critical factors for manufacturing flexibility adoption: An exploratory study in Indian manufacturing firms. Journal of Manufacturing Technology Management, 27(3), 379–407.

    Google Scholar 

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

    Google Scholar 

  • Narasimhan, R., & Das, A. (1999). An empirical investigation of the contribution of strategic sourcing to manufacturing flexibilities and performance. Decision Sciences, 30(3), 683–718.

    Google Scholar 

  • Narasimhan, R., Talluri, S., & Das, A. (2004). Exploring flexibility and execution competencies of manufacturing firms. Journal of Operations Management, 22(1), 91–106.

    Google Scholar 

  • Nunnally, J. C. (1978). Psycometric methods. New York: McGraw-Hill.

    Google Scholar 

  • Parthasarthy, R., & Sethi, S. P. (1993). Relating strategy and structure to flexible automation: A test of fit and performance implications. Strategic Management Journal, 14(7), 529–549.

    Google Scholar 

  • Patel, P. C., Terjesen, S., & Li, D. (2012). Enhancing effects of manufacturing flexibility through operational absorptive capacity and operational ambidexterity. Journal of Operations Management, 30(3), 201–220.

    Google Scholar 

  • Peng, D. X., & Lai, F. (2012). Using partial least squares in operations management research: A practical guideline and summary of past research. Journal of Operations Management, 30(6), 467–480.

    Google Scholar 

  • Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.

    Google Scholar 

  • Rogers, P. P., Divesh, O., & Richard, E. W. (2011). Conceptualising complementarities in manufacturing flexibility: A comprehensive view. International Journal of Production Research, 49(12), 3767–3793.

    Google Scholar 

  • Sakazume, Y. (2006). Conditions for successful implementation of assembly cells. Industrial Engineering and Management Systems, 5, 142–148.

    Google Scholar 

  • Salvador, F., Rungtusanatham, M., Forza, C., & Trentin, A. (2007). Mix flexibility and volume flexibility in a build-to-order environment: Synergies and trade-offs. International Journal of Operations & Production Management, 27(11), 1173–1191.

    Google Scholar 

  • Sawhney, R. (2013). Implementing labor flexibility: A missing link between acquired labor flexibility and plant performance. Journal of Operations Management, 31(1–2), 98–108.

    Google Scholar 

  • Sethi, A. K., & Sethi, S. P. (1990). Flexibility manufacturing: a survey. International Journal of Flexible Manufacturing Systems, 2(4), 289–328.

    Google Scholar 

  • Shah, R., & Ward, P. T. (2003). Lean manufacturing: Context, practice bundles, and performance. Journal of Operations Management, 21(2), 129–149.

    Google Scholar 

  • Shaw, J. (2011). Turnover rates and organizational performance: Review, critique, and research agenda. Organizational Psychology Review, 1(3), 187–213.

    Google Scholar 

  • Sousa, R., & Voss, C. A. (2008). Contingency research in operations management practices. Journal of Operations Management, 26(6), 697–713.

    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, 3(1), 106–119.

    Google Scholar 

  • Suarez, F. F., Cusumano, M. A., & Fine, C. H. (1996). An empirical study of manufacturing flexibility in printed circuit board assembly. Operations Research, 44(1), 223–240.

    Google Scholar 

  • Swamidass, P. M., & Newell, W. T. (1987). Manufacturing strategy, environmental uncertainty and performance: A path analytic model. Management Science, 33(4), 509–524.

    Google Scholar 

  • Swink, M., Narasimhan, R., & Kim, S. W. (2005). Manufacturing practices and strategy integration: Effects on cost efficiency, flexibility, and market-based performance. Decision Sciences, 36(3), 427–457.

    Google Scholar 

  • Upton, D. M. (1994). The management of manufacturing flexibility. California Management Review, 36(2), 72–89.

    Google Scholar 

  • Upton, D. M. (1995). What really makes factories flexible? Harvard Business Review, 73(4), 74–84.

    Google Scholar 

  • Vokurka, R. J., & O’Leary-Kelly, S. W. (2000). A review of empirical research on manufacturing flexibility. Journal of Operations Management, 18(4), 485–501.

    Google Scholar 

  • Ward, P. T., Duray, R., Leong, G. K., & Sum, C. (1995). Business environment, operations strategy, and performance: An empirical study of Singapore manufacturers. Journal of Operations Management, 13(2), 99–115.

    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.

    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.

    Google Scholar 

  • Yu, K., Cadeaux, J., & Luo, B. N. (2015). Operational flexibility: Review and meta-analysis. International Journal of Production Economics, 169, 190–202.

    Google Scholar 

  • Yu, Y., & Tang, J. F. (2018). Seru production. Beijing: Science Press. (in Chinese).

    Google Scholar 

  • Yu, Y., & Tang, J. F. (2019). Review of seru production. Frontiers of Engineering Management, 6(2), 183–192.

    Google Scholar 

  • Zhang, X., Liu, C. G., Li, W. J., Evans, S., & Yin, Y. (2017). Effects of key enabling technologies for seru production on sustainable performance. Omega, 66, 290–307.

    Google Scholar 

  • Zhang, Q., Vonderembse, M. A., & Cao, M. (2006). Achieving flexible manufacturing competence: The roles of advanced manufacturing technology and operations improvement practices. International Journal of Operations & Production Management, 26(6), 580–599.

    Google Scholar 

  • Zhang, Q., Vonderembse, M. A., & Lim, J. S. (2003). Manufacturing flexibility: Defining and analyzing relationships among competence, capability, and customer satisfaction. Journal of Operations Management, 21(2), 173–191.

    Google Scholar 

Download references

Acknowledgement

The authors acknowledge financial support from the National Natural Science Foundation of China (71420107028, 71673040).

Author information

Authors and Affiliations

  1. School of Management Science and Engineering, Dongbei University of Finance and Economics, Dalian, People’s Republic of China

    Chang Liu, Zhen Li & Jiafu Tang

  2. Discipline of Information Technology, Mathematics and Statistics, Murdoch University, Murdoch, WA, 6150, Australia

    Xuequn Wang

  3. Department of Transportation & Logistics Management, National Chiao Tung University, No. 1001, Daxue Road, East District, Hsinchu City, 30010, Taiwan

    Ming-Jong Yao

Authors
  1. Chang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhen Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiafu Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xuequn Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ming-Jong Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Appendices

Appendix A

See Table 12.

Table 12 Constructs, measurement and supporting literature
Full size table

Appendix B

See Table 13.

Table 13 Measurement assessment
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, C., Li, Z., Tang, J. et al. How SERU production system improves manufacturing flexibility and firm performance: an empirical study in China. Ann Oper Res 316, 529–554 (2022). https://doi.org/10.1007/s10479-020-03850-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-020-03850-y

Keywords

Search

Navigation