Skip to main content
Log in

Interpretive multi-criteria ranking of production systems with ordinal weights and transitive dominance relationships

  • S.I.: SOME
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

The paper deals with the situation of manufacturing of customized engineering products under intense competitive environment. Such customized engineering products can be manufactured by different types of production systems such as job production, batch production, mass production, and flexible manufacturing systems. It then becomes an important decision to choose the best one among these production systems which is also dependent on multiple criteria. The three basic criteria predominantly considered in the context of manufacturing are cost, quality, and flexibility. The paper applies the efficient interpretive ranking process (IRP) as an interpretive multi-criteria ranking method to demonstrate its utility in this context. It considers the design of the decision problem by treating the cross-interaction matrix of ‘Alternatives × Criteria’ as a unit matrix, i.e. each alternative is linked with each criterion. In this type of decision problem, the IRP method is implemented with transitive dominance relationships. Further, it carries out the sensitivity analysis concerning different ordinal weights of various criteria. The paper also makes a methodological contribution in IRP by way of computing dominance index rather than simply considering net dominance (which would be either positive or nil or negative). The dominance index gives a better depiction of the dominance of one alternative over the others.

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

Similar content being viewed by others

References

  • Asadi, N., Fundin, A., & Jackson, M. (2015). The essential constituents of flexible assembly systems: A case study in the heavy vehicle manufacturing industry. Global Journal of Flexible Systems Management,16(3), 235–250.

    Google Scholar 

  • Beach, R., Muhlemann, A. P., Price, D. H., Paterson, A., & Sharp, J. A. (2000). A review of manufacturing flexibility. European Journal of Operational Research,122(1), 41–57.

    Google Scholar 

  • Beskese, A., Kahraman, C., & Irani, Z. (2004). Quantification of flexibility in advanced manufacturing systems using fuzzy concept. International Journal of Production Economics,89(1), 45–56.

    Google Scholar 

  • Buzacott, J. A., & Yao, D. D. (1986). Flexible manufacturing systems: A review of analytical models. Management Science,32(7), 890–905.

    Google Scholar 

  • Cheng, R., Gen, M., & Tsujimura, Y. (1996). A tutorial survey of job-shop scheduling problems using genetic algorithms—I. Representation. Computers & Industrial Engineering,30(4), 983–997.

    Google Scholar 

  • Chiadamrong, N. (1999). An integrated fuzzy multi-criteria decision making method for manufacturing strategies selection. Computers & Industrial Engineering,37(1–2), 433–436.

    Google Scholar 

  • Cooney, R. (2002). Is “lean” a universal production system? Batch production in the automotive industry. International Journal of Operations & Production Management,22(10), 1130–1147.

    Google Scholar 

  • da Silveira, G. J. (2005). Improving trade-offs in manufacturing: Method and illustration. International Journal of Production Economics,95(1), 27–38.

    Google Scholar 

  • Datta, V., Sambasivarao, K. V., Kodali, R., & Deshmukh, S. G. (1992). Multi-attribute decision model using the analytic hierarchy process for the justification of manufacturing systems. International Journal of Production Economics,28(2), 227–234.

    Google Scholar 

  • Duguay, C. R., Landry, S., & Pasin, F. (1997). From mass production to flexible/agile production. International Journal of Operations & Production Management,17(12), 1183–1195.

    Google Scholar 

  • Ferdows, K., & De Meyer, A. (1990). Lasting improvements in manufacturing performance: In search of a new theory. Journal of Operations Management,9(2), 168–184.

    Google Scholar 

  • Gangotra, A., & Shankar, R. (2016). Strategies in managing risks in the adoption of business analytics practices: A case study of a telecom service provider. Journal of Enterprise Information Management,29(3), 374–399.

    Google Scholar 

  • Gupta, Y. P., & Goyal, S. (1989). Flexibility of manufacturing systems: Concepts and measurements. European Journal of Operational Research,43(2), 119–135.

    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.

    Google Scholar 

  • Haleem, A., Sushil, Qadri, M. A., & Kumar, S. (2012). Analysis of critical success factors of world-class manufacturing practices: An application of interpretive structural modelling and interpretive ranking process. Production Planning & Control,23(10–11), 722–734.

    Google Scholar 

  • Hillion, H. P., & Proth, J. M. (1989). Performance evaluation of job-shop systems using timed event-graphs. IEEE Transactions on Automatic Control,34(1), 3–9.

    Google Scholar 

  • Ho, W., Xu, X., & Dey, P. K. (2010). Multi-criteria decision making approaches for supplier evaluation and selection: A literature review. European Journal of Operational Research,202(1), 16–24.

    Google Scholar 

  • Hughes, D. L., Dwivedi, Y. K., & Rana, N. P. (2017). Mapping IS failure factors on PRINCE2® stages: An application of interpretive ranking process (IRP). Production Planning & Control, 28(9), 776–790.

    Google Scholar 

  • Johnzen, C., Dauzere-Peres, S., & Vialletelle, P. (2011). Flexibility measures for qualification management in wafer fabs. Production Planning & Control,22(1), 81–90.

    Google Scholar 

  • Karim, M. A., Smith, A. J. R., Halgamuge, S. K., & Islam, M. M. (2008). A comparative study of manufacturing practices and performance variables. International Journal of Production Economics,112(2), 841–859.

    Google Scholar 

  • Karsak, E. E. (2002). Distance-based Fuzzy MCDM approach for evaluating flexible manufacturing system alternatives. International Journal of Production Research,40(13), 3167–3181.

    Google Scholar 

  • Kaur, H., Singh, S. P., & Glardon, R. (2016). An integer linear program for integrated supplier selection: A sustainable flexible framework. Global Journal of Flexible Systems Management,17(2), 113–134.

    Google Scholar 

  • Kim, Y., & Lee, J. (1993). Manufacturing strategy and production systems: An integrated framework. Journal of Operations Management,11(1), 3–15.

    Google Scholar 

  • Kotha, S. (1996). From mass production to mass customization: The case of the National Industrial Bicycle Company of Japan. European Management Journal,14(5), 442–450.

    Google Scholar 

  • Kulak, O., & Kahraman, C. (2005). Multi-attribute comparison of advanced manufacturing systems using fuzzy vs. crisp axiomatic design approach. International Journal of Production Economics,95(3), 415–424.

    Google Scholar 

  • Luthra, S., Garg, D., & Haleem, A. (2015). Critical success factors of green supply chain management for achieving sustainability in Indian automobile industry. Production Planning & Control,26(5), 339–362.

    Google Scholar 

  • Mangla, S. K., Kumar, P., & Barua, M. K. (2014). A flexible decision framework for building risk mitigation strategies in green supply chain using SAP–LAP and IRP approaches. Global Journal of Flexible Systems Management,15(3), 203–218.

    Google Scholar 

  • Mangla, S. K., Kumar, P., & Barua, M. K. (2015). Flexible decision modeling for evaluating the risks in green supply chain using fuzzy AHP and IRP methodologies. Global Journal of Flexible Systems Management,16(1), 19–35.

    Google Scholar 

  • Mishra, R., Pundir, A. K., & Ganapathy, L. (2014). Manufacturing flexibility research: A review of literature and agenda for future research. Global Journal of Flexible Systems Management,15(2), 101–112.

    Google Scholar 

  • Prakash, C., & Barua, M. K. (2017). Flexible modelling approach for evaluating reverse logistics adoption barriers using fuzzy AHP and IRP framework. International Journal of Operational Research,30(2), 151–171.

    Google Scholar 

  • Raafat, F. (2002). A comprehensive bibliography on justification of advanced manufacturing systems. International Journal of Production Economics,79(3), 197–208.

    Google Scholar 

  • Saaty, T. L. (1980). The analytical hierarchy process: Planning, priority setting, resource allocation., New York: McGraw-Hill.

    Google Scholar 

  • Saaty, T. L. (1990). How to make decision: The analytical decision process. European Journal of Operational Research,48(1), 9–26.

    Google Scholar 

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

    Google Scholar 

  • Shang, J., & Sueyoshi, T. (1995). A unified framework for the selection of a flexible manufacturing system. European Journal of Operational Research,85(2), 297–315.

    Google Scholar 

  • Sharma, O., & Sushil. (2002). Issues in managing manufacturing flexibility: A review. Global Journal of Flexible Systems Management,3(2&3), 11–29.

    Google Scholar 

  • Sharma, V., Dixit, A. R., & Qadri, M. A. (2016). Modeling lean implementation for manufacturing sector. Journal of Modelling in Management,11(2), 405–426.

    Google Scholar 

  • Skarlo, T. (1999). ‘The flexible landscape’: A model for explaining operational mix and volume flexibility. Production Planning & Control,10(8), 735–744.

    Google Scholar 

  • Slack, N. (1983). Flexibility as a manufacturing objective. International Journal of Operations & Production Management,3(3), 4–13.

    Google Scholar 

  • Son, Y. K., & Park, C. S. (1987). Economic measure of productivity, quality and flexibility in advanced manufacturing systems. Journal of Manufacturing Systems,6(3), 193–207.

    Google Scholar 

  • Suarez, F. F., Cusumano, M. A., & Fine, C. H. (1995). An empirical study of flexibility in manufacturing. Sloan Management Review,37(1), 25–32.

    Google Scholar 

  • Sushil. (2005). Interpretive matrix: A tool to aid interpretation of management and social research. Global Journal of Flexible Systems Management,6(2), 27–30.

    Google Scholar 

  • Sushil. (2009). Interpretive ranking process. Global Journal of Flexible Systems Management,10(4), 1–10.

    Google Scholar 

  • Sushil. (2017a). Efficient interpretive ranking process incorporating implicit and transitive dominance relationships. Annals of Operations Research. https://doi.org/10.1007/s10479-017-2608-y.

    Article  Google Scholar 

  • Sushil. (2017b). Multi-criteria valuation of flexibility initiatives using integrated TISM–IRP with a big data framework. Production Planning & Control,28(11–12), 999–1010.

    Google Scholar 

  • Sushil. (2017c). Modified ISM/TISM process with simultaneous transitivity checks for reducing direct pair comparisons. Global Journal of Flexible Systems Management,18(4), 331–351.

    Google Scholar 

  • Sushil. (2018a). Valuation of flexibility initiatives: A conceptual framework. In Sushil, T. P. Singh, & A. J. Kulkarni (Eds.), Flexibility in resource management, flexible systems management. Singapore: Springer.

    Google Scholar 

  • Sushil. (2018b). Valuation of flexibility initiatives along the value chain. In J. Connell, R. Agarwal, Sushil, & S. Dhir (Eds.), Global value chains, flexibility and sustainability, flexible systems management. Singapore: Springer.

    Google Scholar 

  • Tayal, A., Gunasekaran, A., Singh, S. P., Dubey, R., & Papadopoulos, T. (2017). Formulating and solving sustainable stochastic dynamic facility layout problem: A key to sustainable operations. Annals of Operations Research,253(1), 621–655.

    Google Scholar 

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

    Google Scholar 

  • Upton, D. M. (1995). What makes factories flexible? Harvard Business Review,73(4), 74–81.

    Google Scholar 

  • Ware, N. R., Singh, S. P., & Banwet, D. K. (2014). Modeling flexible supplier selection framework. Global Journal of Flexible Systems Management,15(3), 261–274.

    Google Scholar 

  • Weng, F. T., & Her, M. G. (2002). Study of the batch production of micro parts using the EDM process. The International Journal of Advanced Manufacturing Technology,19(4), 266–270.

    Google Scholar 

  • Youndt, M. A., Snell, S. A., Dean, J. W., & Lepak, D. P. (1996). Human resource management, manufacturing strategy, and firm performance. Academy of Management Journal,39(4), 836–866.

    Google Scholar 

  • Zipkin, P. H. (1986). Models for design and control of stochastic, multi-item batch production systems. Operations Research,34(1), 91–104.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sushil.

Appendix: Dominating interaction matrices and successive comparison digraphs for various criteria (Ci)

Appendix: Dominating interaction matrices and successive comparison digraphs for various criteria (Ci)

See Figs. 3, 4 and 5 and Table 11.

Fig. 3
figure 3

Dominating interaction matrix and successive comparison digraph for criterion C1

Fig. 4
figure 4

Dominating interaction matrix and successive comparison digraph for criterion C2

Fig. 5
figure 5

Dominating interaction matrix and successive comparison digraph for criterion C3

Table 11 Interpretive logic—knowledge base—ranking of alternatives w.r.t. criteria

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sushil Interpretive multi-criteria ranking of production systems with ordinal weights and transitive dominance relationships. Ann Oper Res 290, 677–695 (2020). https://doi.org/10.1007/s10479-018-2946-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-018-2946-4

Keywords

Navigation