Skip to main content
SpringerLink
Log in

An integrated approach for lean production using simulation and data envelopment analysis

  • Original Research
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

According to the extant literature, improving the leanness of a production system boosts a company’s productivity and competitiveness. However, such an endeavor usually involves managing multiple, potentially conflicting objectives. This study proposes a framework that analyzes lean production methods using simulation and data envelopment analysis (DEA) to accommodate the underlying multi-objective decision-making problem. The proposed framework can help identify the most efficient solution alternative by (i) considering the most common lean production methods for assembly line balancing, such as single minute exchange of dies (SMED) and multi-machine set-up reduction (MMSUR), (ii) creating and simulating various alternative assembly line configuration options via discrete-event simulation modeling, and (iii) formulating and applying DEA to identify the best alternative assembly system configuration for the multi-objective decision making. In this study, we demonstrate the viability and superiority of the proposed framework with an application case on an automotive spare parts production system. The results show that the suggested framework substantially improves the existing system by increasing efficiency while concurrently decreasing work-in-process (WIP).

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
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Agarwal, A., Colak, S., & Eryarsoy, E. (2006). Improvement heuristic for the flow-shop scheduling problem: An adaptive-learning approach. European Journal of Operational Research, 169(3), 801–815.

    Article  Google Scholar 

  • Ahlstrom, P. (2004). Lean service operations: Translating lean production principles to service operations. International Journal of Services Technology and Management, 5(5–6), 545–564.

    Article  Google Scholar 

  • Aldamak, A., & Zolfaghari, S. (2017). Review of efficiency ranking methods in data envelopment analysis. Measurement, 106, 161–172.

    Article  Google Scholar 

  • Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science, 39(10), 1261–1264.

    Article  Google Scholar 

  • Azadeh, A., Yazdanparast, R., Zadeh, S. A., & Zadeh, A. E. (2017). Performance optimization of integrated resilience engineering and lean production principles. Expert Systems with Applications, 84, 155–170.

    Article  Google Scholar 

  • Baril, C., Gascon, V., Miller, J., & Côté, N. (2016). Use of a Discrete-event simulation in a Kaizen event: A case study in healthcare. European Journal of Operational Research, 249(1), 327–339.

    Article  Google Scholar 

  • Barnabè, F., & Giorgino, M. C. (2017). Practicing lean strategy: Hoshin Kanri and X-matrix in a healthcare-centered simulation. The TQM Journal, 29(4), 590–609. https://doi.org/10.1108/TQM-07-2016-0057

    Article  Google Scholar 

  • Becker, C., & Scholl, A. (2006). A survey on problems and methods in generalized assembly line balancing. European Journal of Operational Research, 168(3), 694–715.

    Article  Google Scholar 

  • Biswas, P., & Sarker, B. R. (2008). Optimal batch quantity models for a lean production system with in-cycle rework and scrap. International Journal of Production Research, 46(23), 6585–6610.

    Article  Google Scholar 

  • Boysen, N., Fliedner, M., & Scholl, A. (2008). Assembly line balancing: Which model to use when? International Journal of Production Economics, 111(2), 509–528.

    Article  Google Scholar 

  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.

    Article  Google Scholar 

  • Chauhan, G., & Singh, T. P. (2011). Lean manufacturing through management of labor and machine flexibility: A comprehensive review. Global Journal of Flexible Systems Management, 12(1), 59–80.

    Article  Google Scholar 

  • De Matta, R., Hsu, V. N., & Feng, C. X. J. (2001). Short-term capacity adjustment with offline production for a flexible manufacturing system under abnormal disturbances. Annals of Operations Research, 107(1), 83–100. https://doi.org/10.1023/A:1014942830746

    Article  Google Scholar 

  • Demirbag, M., Tatoglu, E., & Glaister, K. W. (2009). Equity-based entry modes of emerging country multinationals: Lessons from Turkey. Journal of World Business, 44(4), 445–462.

    Article  Google Scholar 

  • Dhafr, N., Ahmad, M., Burgess, B., & Canagassababady, S. (2006). Improvement of quality performance in manufacturing organizations by minimization of production defects. Robotics and Computer-Integrated Manufacturing, 22(5–6), 536–542.

    Article  Google Scholar 

  • Diaz-Elsayed, N., Jondral, A., Greinacher, S., Dornfeld, D., & Lanza, G. (2013). Assessment of lean and green strategies by simulation of manufacturing systems in discrete production environments. CIRP Annals, 62(1), 475–478.

    Article  Google Scholar 

  • Dillon, A. P., & Shingo, S. (1985). A revolution in manufacturing: The SMED system. CRC Press.

    Google Scholar 

  • Doğan, N. Ö., & Unutulmaz, O. (2016). Lean production in healthcare: A simulation-based value stream mapping in the physical therapy and rehabilitation Department of a Public Hospital. Total Quality Management & Business Excellence, 27(1–2), 64–80.

    Article  Google Scholar 

  • Esmaeilian, B., Behdad, S., & Wang, B. (2016). The evolution and future of manufacturing: A review. Journal of Manufacturing Systems, 39, 79–100.

    Article  Google Scholar 

  • Gijo, E. V., & Antony, J. (2014). Reducing patient waiting time in outpatient department using lean six sigma methodology. Quality and Reliability Engineering International, 30(8), 1481–1491.

    Article  Google Scholar 

  • Greinacher, S., Moser, E., Freier, J., Müller, J., & Lanza, G. (2016). Simulation-based methodology for the application of lean and green strategies depending on external change driver influence. Procedia CIRP, 48, 242–247.

    Article  Google Scholar 

  • Gurumurthy, A., & Kodali, R. (2008). A multi-criteria decision-making model for the justification of lean manufacturing systems. International Journal of Management Science and Engineering Management, 3(2), 100–118.

    Article  Google Scholar 

  • Gustavsson, S. O. (1984). Flexibility and productivity in complex production processes. International Journal of Production Research, 22(5), 801–808. https://doi.org/10.1080/00207548408942500

    Article  Google Scholar 

  • He, J., Chu, F., Zheng, F., & Liu, M. (2020). A green-oriented bi-objective disassembly line balancing problem with stochastic task processing times. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03558-z

    Article  Google Scholar 

  • Helgeson, W. B., & Birnie, D. P. (1961). Assembly line balancing using the ranked positional weight technique. Journal of Industrial Engineering, 12(6), 394–398.

    Google Scholar 

  • Hines, P., & Rich, N. (1997). The seven value stream mapping tools. International Journal of Operations & Production Management.

  • Hopp, W., & Spearman, M. (2000). Factory physics. McGraw-Hill/Irwin.

    Google Scholar 

  • Jordon, K., Dossou, P. E., & Junior, J. C. (2019). Using lean manufacturing and machine learning for improving medicines procurement and dispatching in a hospital. Procedia Manufacturing, 38, 1034–1041.

    Article  Google Scholar 

  • Kilic, H. S., & Durmusoglu, M. B. (2013). A mathematical model and a heuristic approach for periodic material delivery in lean production environment. The International Journal of Advanced Manufacturing Technology, 69(5), 977–992. https://doi.org/10.1007/s00170-013-5082-y

    Article  Google Scholar 

  • Kull, T. J., Yan, T., Liu, Z., & Wacker, J. G. (2014). The moderation of lean manufacturing effectiveness by dimensions of national culture: Testing practice-culture congruence hypotheses. International Journal of Production Economics, 153, 1–12.

    Article  Google Scholar 

  • Li, Y., Diabat, A., & Lu, C. C. (2020). Leagile supplier selection in Chinese textile industries: A DEMATEL approach. Annals of Operations Research, 287(1), 303–322. https://doi.org/10.1007/s10479-019-03453-2

    Article  Google Scholar 

  • Mao, Z., Huang, D., Fang, K., Wang, C., & Lu, D. (2019). Milk-run routing problem with progress-lane in the collection of automobile parts. Annals of Operations Research. https://doi.org/10.1007/s10479-019-03218-x

    Article  Google Scholar 

  • Markarian, J. (2004). Six sigma: Quality processing through statistical analysis. Plastics, Additives and Compounding, 6(4), 28–31. https://doi.org/10.1016/S1464-391X(04)00236-3

    Article  Google Scholar 

  • Meyers, F. E., & Stewart, J. R. (2002). Motion and time study for lean manufacturing. Pearson College Division.

    Google Scholar 

  • Meza, D., & Jeong, K. Y. (2013). Measuring efficiency of lean six sigma project implementation using data envelopment analysis at NASA. Journal of Industrial Engineering and Management (JIEM), 6(2), 401–422.

    Google Scholar 

  • Monden, Y. (1984). A simulation analysis of the Japanese just-in-time technique (with Kanbans) for a multiline, multistage production system’: A comment. Decision Sciences, 15(3), 445–447.

    Article  Google Scholar 

  • Monkman, S. K., Morrice, D. J., & Bard, J. F. (2008). A Production scheduling heuristic for an electronics manufacturer with sequence-dependent setup costs. European Journal of Operational Research, 187(3), 1100–1114. https://doi.org/10.1016/j.ejor.2006.06.063

    Article  Google Scholar 

  • Naylor, J. B., Naim, M. M., & Berry, D. (1999). Leagility: Integrating the lean and agile manufacturing paradigms in the total supply chain. International Journal of Production Economics, 62(1–2), 107–118.

    Article  Google Scholar 

  • Negahban, A., & Smith, J. S. (2014). Simulation for manufacturing system design and operation: Literature review and analysis. Journal of Manufacturing Systems, 33(2), 241–261.

    Article  Google Scholar 

  • Nemati, M., Kazemi Matin, R., & Toloo, M. (2020). A two-stage DEA model with partial impacts between inputs and outputs: Application in refinery industries. Annals of Operations Research, 295, 285–312.

    Article  Google Scholar 

  • Nicholas, J. (2015). Lean production for competitive advantage: A comprehensive guide to lean methodologies and management practices. CRC Press.

    Google Scholar 

  • Ohlmann, J. W., Fry, M. J., & Thomas, B. W. (2008). Route design for lean production systems. Transportation Science, 42(3), 352–370.

    Article  Google Scholar 

  • Ohno, T. (1988). Toyota production system: Beyond large-scale production. CRC Press.

    Google Scholar 

  • Porter, M. E. (1989). How competitive forces shape strategy. Readings in strategic management (pp. 133–143). Palgrave.

    Chapter  Google Scholar 

  • Rahani, A. R., & Al-Ashraf, M. (2012). Production flow analysis through value stream mapping: A lean manufacturing process case study. Procedia Engineering, 41, 1727–1734.

    Article  Google Scholar 

  • Rekiek, B., Dolgui, A., Delchambre, A., & Bratcu, A. (2002). State of art of optimization methods for assembly line design. Annual Reviews in Control, 26(2), 163–174.

    Article  Google Scholar 

  • Robinson, S. (2004). Simulation: The practice of model development and use. Wiley Chichester.

    Google Scholar 

  • Scholl, A., Boysen, N., & Fliedner, M. (2009). Optimally solving the alternative subgraphs assembly line balancing problem. Annals of Operations Research, 172(1), 243. https://doi.org/10.1007/s10479-009-0578-4

    Article  Google Scholar 

  • Singh, B. J., & Khanduja, D. (2010). SMED: For quick changeovers in foundry SMEs. International Journal of Productivity and Performance Management.

  • Soroush, H., Sajjadi, S. M., & Arabzad, S. M. (2014). Efficiency analysis and optimisation of a multi-product assembly line using simulation. International Journal of Productivity and Quality Management, 13(1), 89–104.

    Article  Google Scholar 

  • Suarez, F. F., Cusumano, M. A., & Fine, C. H. (1995). An empirical study of flexibility in manufacturing. MIT Sloan Management Review, 37(1), 25.

    Google Scholar 

  • Sullivan, W. G., McDonald, T. N., & Van Aken, E. M. (2002). Equipment replacement decisions and lean manufacturing. Robotics and Computer-Integrated Manufacturing, 18(3–4), 255–265.

    Article  Google Scholar 

  • Susilawati, A., Tan, J., Bell, D., & Sarwar, M. (2015). Fuzzy logic based method to measure degree of lean activity in manufacturing industry. Journal of Manufacturing Systems, 34, 1–11.

    Article  Google Scholar 

  • Trovinger, S. C., & Bohn, R. E. (2005). Setup time reduction for electronics assembly: Combining simple (SMED) and IT-based methods. Production and Operations Management, 14(2), 205–217.

    Article  Google Scholar 

  • Van Goubergen, D. (2008). Setup reduction for lean cells and multi-machine situations. Lean business systems and beyond (pp. 295–303). Springer.

    Chapter  Google Scholar 

  • Villarreal, B., Garza-Reyes, J. A., Kumar, V., & Lim, M. K. (2017). Improving road transport operations through lean thinking: A case study. International Journal of Logistics Research and Applications, 20(2), 163–180.

    Article  Google Scholar 

  • Vinodh, S., & Balaji, S. R. (2011). Fuzzy logic based leanness assessment and its decision support system. International Journal of Production Research, 49(13), 4027–4041.

    Article  Google Scholar 

  • Vinodh, S., & Chintha, S. K. (2011). Leanness assessment using multi-grade fuzzy approach. International Journal of Production Research, 49(2), 431–445.

    Article  Google Scholar 

  • Womack, J. P., & Jones, D. T. (1997). Lean thinking—Banish waste and create wealth in your corporation. Journal of the Operational Research Society, 48(11), 1148–1148.

    Article  Google Scholar 

  • Wu, Z., Xu, J., & Xu, Z. (2016). A multiple attribute group decision making framework for the evaluation of lean practices at logistics distribution centers. Annals of Operations Research, 247(2), 735–757. https://doi.org/10.1007/s10479-015-1788-6

    Article  Google Scholar 

  • Yang, T., Kuo, Y., Su, C. T., & Hou, C. L. (2015). Lean production system design for fishing net manufacturing using lean principles and simulation optimization. Journal of Manufacturing Systems, 34, 66–73.

    Article  Google Scholar 

  • Zaim, S., Bayyurt, N., Turkyilmaz, A., Solakoglu, N., & Zaim, H. (2008). Measuring and evaluating efficiency of hospitals through total quality management. Journal of Transnational Management, 12(4), 77–97.

    Article  Google Scholar 

  • Zarrin, M., & Azadeh, A. (2017). Simulation optimization of lean production strategy by considering resilience engineering in a production system with maintenance policies. SIMULATION, 93(1), 49–68.

    Article  Google Scholar 

  • Zhou, Z., Wang, M., Ding, H., Ma, C., & Liu, W. (2013). Further study of production possibility set and performance evaluation model in supply chain DEA. Annals of Operations Research, 206(1), 585–592.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Engineering, Industrial Engineering Department, İstanbul University-Cerrahpasa, Avcilar Campus, Istanbul, Turkey

    Sinem Buyuksaatci Kiris

  2. Sabanci Business School, Sabanci University, Sabanci University Tuzla Campus, Istanbul, Turkey

    Enes Eryarsoy

  3. College of Engineering, Department of Industrial Engineering, Istanbul Sabahattin Zaim University, Istanbul Sabahattin Zaim University Campus Halkali, Istanbul, Turkey

    Selim Zaim

  4. Department of Management Science and Information Systems (MSIS), Spears School of Business, Oklahoma State University, Tulsa, OK, 74106, USA

    Dursun Delen

  5. School of Business, Ibn Haldun University, Istanbul, Turkey

    Dursun Delen

Authors
  1. Sinem Buyuksaatci Kiris
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Enes Eryarsoy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Selim Zaim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dursun Delen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dursun Delen.

Additional information

Publisher's Note

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

Appendix

Appendix

See Table

Table 8 Generated multi-activity diagram
Full size table

8.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kiris, S.B., Eryarsoy, E., Zaim, S. et al. An integrated approach for lean production using simulation and data envelopment analysis. Ann Oper Res 320, 863–886 (2023). https://doi.org/10.1007/s10479-021-04265-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-021-04265-z

Keywords

Search

Navigation