Skip to main content
Log in

Assessment of Critical Enablers for Flexible Supply Chain Performance Measurement System Using Fuzzy DEMATEL Approach

  • Original Article
  • Published:
Global Journal of Flexible Systems Management Aims and scope Submit manuscript

Abstract

In the competitive world of today, each organization has a desire to sustain in the marketplace with the implementation of their healthier and flexible supply chain performance measurement (SCPM) system. For the successful implementation, needs to know the significant set of enablers. This study identifies a set of important enablers based on literature review and discussion with field experts of automobile manufacturing industries located in the National Capital Region of India. The vagueness and impreciseness of field expert’s judgements has been reduced using fuzzy decision making trial and evaluation laboratory (fuzzy DEMATEL) approach and analyzed the enablers in order to implement a flexible SCPM system. The findings of this research advocate that enabler ‘higher customer satisfaction’ comes in picture with highest value of ‘Prominence’ (6.4272) and ‘Relation’ (1.0354), therefore seems as a most significant and influencing enabler, while on other side the enabler ‘proper capacity utilization’ is considered as ample influencing enabler, because it has lowest Prominence’ (4.4735) and ‘Relation’ (minus 0.9680) values. This research discussed the categorization into the cause and effect group, degree of interaction and inter-relationship of considered enablers. The outcomes of this study may provide an aid to the managers to implement an effective and flexible SCPM system through which overall profitability of an organization may be improved.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Anderson, E. W., Fornell, C., & Lehmann, D. R. (1994). Customer satisfaction, market share, and profitability: Findings from Sweden. Journal of Marketing, 58(7), 53–66.

    Google Scholar 

  • Anderson, M. F., & Katz, P. B. (1998). Strategic sourcing. International Journal of Logistics Management, 9(1), 1–13.

    Google Scholar 

  • Aviv, Y. (2007). On the benefits of collaborative forecasting partnerships between retailers and manufacturers. Management Science, 53(5), 777–794.

    Google Scholar 

  • Barut, M., Faisst, W., & Kanet, J. J. (2002). Measuring supply chain coupling: an information system perspective. European Journal of Purchasing & Supply Management, 8(3), 161–171.

    Google Scholar 

  • Beamon, B. M. (1999). Measuring supply chain performance. International Journal of Operations and Production Management, 19(3), 275–292.

    Google Scholar 

  • Bhagwat, R., & Sharma, M. K. (2007). Performance measurement of supply chain management: A balanced scorecard approach. Computers & Industrial Engineering, 53(1), 43–62.

    Google Scholar 

  • Bowersox, D., Closs, D., & Cooper, M. (2010). Supply chain logistics management (Int ed.). New York: McGraw-Hill.

    Google Scholar 

  • Brown, S., Squire, B., & Blackmon, K. (2007). The contribution of manufacturing strategy involvement and alignment to world-class manufacturing performance. International Journal of Operations and Production Management, 27(3), 282–302.

    Google Scholar 

  • Chan, F. T. S., Nayak, A., Raj, R., Chong, A. Y. L., & Tiwari, M. (2014). An innovative supply chain performance measurement system incorporating research and development (R&D) and marketing policy. Computers & Industrial Engineering, 69, 64–70.

    Google Scholar 

  • Chang, B., Chang, C. W., & Wu, C. H. (2011). Fuzzy DEMATEL method for developing supplier selection criteria. Expert Systems with Applications, 38(3), 1850–1858.

    Google Scholar 

  • Charan, P., Shankar, R., & Baisya, R. K. (2008). Analysis of interactions among the variables of supply chain performance measurement system implementation. Business Process Management Journal, 14(4), 512–529.

    Google Scholar 

  • Chen, J. K., & Chen, S. (2010). Using a novel conjunctive MCDM approach based on DEMATEL, fuzzy ANP, and TOPSIS as an innovation support system for Taiwanese higher education. Expert Systems with Applications, 37(3), 1981–1990.

    Google Scholar 

  • Chiadamrong, N., & Prasertwattana, K. (2006). A comparative study of supply chain models under the traditional centralized and coordinating policies with incentive schemes. Computers & Industrial Engineering, 50(4), 367.

    Google Scholar 

  • Cho, D. W., Lee, Y. H., Ahn, S. H., & Hwang, M. K. (2012). A framework for measuring the performance of service supply chain management. Computers & Industrial Engineering, 62(3), 801–818.

    Google Scholar 

  • Christopher, M. (1998). Logistics and supply chain management: Strategies for reducing costs and improving services. Financial times (2nd ed.). London: Pitman Publishing.

    Google Scholar 

  • Cohen, J. (1968). Weighted kappa: Nominal scale agreement with provision for scaled disagreement or partial credit. Psychological Bulletin, 70(4), 213–220.

    Google Scholar 

  • Cooper, R. G., & Kleinschmidt, E. J. (1995). Benchmarking the firm’s critical success factors in new project development. Journal of Product Innovation Management, 12(5), 374–391.

    Google Scholar 

  • Cousins, P. D. (2005). The alignment of appropriate firm and supply strategies for competitive advantage. International Journal of Operations & Production Management, 25(5), 403–428.

    Google Scholar 

  • Das, A., Narasimhan, R., & Talluri, S. (2006). Supplier integration: Finding an optimal configuration. Journal of Operations Management, 24(5), 563–582.

    Google Scholar 

  • Daugherty, P. J., Ellinger, A. E., & Rogers, D. S. (1995). Information accessibility: Customer responsiveness and enhanced performance. International Journal of Physical Distribution and Logistics Management, 25(1), 4–17.

    Google Scholar 

  • Dawes, J. (1999). The relationship between subjective and objective company performance measures in market orientation research: Further empirical evidence. Marketing Bulletin, 10, 65–75.

    Google Scholar 

  • Demirbag, M., Koh, S. C. L., Tatoglu, E., & Zaim, S. (2006). TQM and market orientation’s impact on SMEs’ performance. Industrial Management & Data Systems, 106(8), 1206–1228.

    Google Scholar 

  • Dreyer, D. E. (2000). Performance measurement: A practitioner’s perspective. Supply Chain Management Review, 4(4), 30–36.

    Google Scholar 

  • Fawcett, S. E., & Cooper, M. B. (1998). Logistics performance measurement and customer success. Industrial Marketing Management, 27(4), 341–357.

    Google Scholar 

  • Fawcett, S. E., & Myers, M. B. (2001). Product and employee development in advanced manufacturing: Implementation and impact. International Journal of Production Research, 39(1), 65–79.

    Google Scholar 

  • Flynn, B. B., & Flynn, E. J. (2005). Synergies between supply chain management and quality management: Emerging implications. International Journal of Production Research, 43(16), 3421–3436.

    Google Scholar 

  • Frohlich, M. T., & Westbrook, R. (2001). Arcs of integration: an international study of supply chain strategies. Journal of Operations Management, 19(2), 185–200.

    Google Scholar 

  • Fynes, B., de Búrca, S., & Marshall, D. (2004). Environmental uncertainty, supply chain relationship quality and performance. Journal of Purchasing and Supply Management, 10(4/5), 179–190.

    Google Scholar 

  • Gabus, A., & Fontela, E. (1973). Perceptions of the world problematique: Communication procedure, communicating with those bearing collective responsibility. Geneva: Battelle Geneva Research Center. (DEMATEL Report No. 1).

  • Giannakis, M. (2011). Management of service supply chains with a service oriented reference model: The case of management consulting source. Supply Chain Management, 16(5), 346–361.

    Google Scholar 

  • Graham, T. S., Dougherty, P. J., & Dudley, W. N. (1994). The long term strategic impact of purchasing partnerships. International Journal of Purchasing and Materials Management, 30(4), 13–18.

    Google Scholar 

  • Green, K. W, Jr., & Inman, R. A. (2005). Using a just-in-time selling strategy to strengthen supply chain linkages. International Journal of Production Research, 43(16), 3437–3453.

    Google Scholar 

  • Green, K. W., Jr., Inman, R. A., Brown, G., & Willis, T. H. (2005). Market orientation: Relation to structure and performance. Journal of Business & Industrial Marketing, 20(6), 276–284.

    Google Scholar 

  • Griffin, A. (1997). PDMA research on new product development practices: Updating trends and benchmarking best practices. Journal of Product Innovation Management, 14(6), 429–458.

    Google Scholar 

  • Gunasekaran, A., & Kobu, B. (2007). Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995–2004) for research and applications. International Journal of Production Research, 45(12), 2819–2840.

    Google Scholar 

  • Gunasekaran, A., & Ngai, E. W. T. (2008). Adoption of e-procurement in Hong Kong: An empirical research. International Journal of Production Economics, 113(1), 159–175.

    Google Scholar 

  • Gunasekaran, A., Patel, C., & McGaughey, R. E. (2004). A Framework for supply chain performance measurement. International Journal of Production Economics, 87(3), 333–347.

    Google Scholar 

  • Gunasekaran, A., Patel, C., & Tirtiroglu, E. (2001). Performance measures and metrics in a supply chain environment. International Journal of Operations & Production Management, 21(1/2), 71–87.

    Google Scholar 

  • Gupta, A. K., & Govindarajan, V. (2000). Knowledge flows within multinational corporations. Strategic Management Journal, 21(4), 473–496.

    Google Scholar 

  • Handfield, R. B., & Nichols, E. L, Jr. (1999). Introduction to supply chain management. Prentice-Hall: Upper Saddle River.

    Google Scholar 

  • Harrtman, R. V., & Bengtsson, L. (2009). Manufacturing competence: A key to success-full supplier integration. International Journal of Manufacturing Technology and Management, 16(3), 283–299.

    Google Scholar 

  • Heizer, J., & Render, B. (2006). Operations management (8th ed.). Pearson Education New Jersey: Upper Saddle River.

    Google Scholar 

  • Hofmann, E., & Locker, A. (2009). Value-based performance measurement in supply chains: A case study from the packaging industry. Production Planning and Control, 20(1), 68–81.

    Google Scholar 

  • Holweg, M., Disney, S., Holmstrom, J., & Smaros, J. (2005). Supply chain collaboration: Making sense of the strategy continuum. European Management Journal, 23(2), 170–181.

    Google Scholar 

  • Hsu, C. Y., Chen, Y. H., & Tzeng, G. H. (2007). FMCDM with Fuzzy DEMATEL Approach for customers’ choice behavior model. International Journal of Fuzzy Systems, 9(4), 236–246.

    Google Scholar 

  • Hu, F., Lim, C. C., & Lu, Z. (2013). Coordination of supply chains with a flexible ordering policy under yield and demand uncertainty. International Journal of Production Economics, 146, 686–693.

    Google Scholar 

  • Hurley, R. F., & Hult, G. T. M. (1998). Innovation, market orientation, and organisational learning: An integration and empirical examination. Journal of Marketing, 62, 42–54.

    Google Scholar 

  • Ittner, C. D., & Larcker, D. F. (1997). Product development cycle time and organizational performance. Journal of Marketing Research, 34(1), 13–23.

    Google Scholar 

  • Jakhar, S. K., & Barua, M. K. (2014). An integrated model of supply chain performance evaluation and decision-making using structural equation modelling and fuzzy AHP. Production Planning and Control, 25(11), 938–957.

    Google Scholar 

  • Johnson, J. L. (1999). Strategic integration in distribution channels: Managing the inter-firm relationship as a strategic asset. Academy of Marketing Science Journal, 27(1), 4–18.

    Google Scholar 

  • Kaufmann, A., & Gupta, M. M. (1991). Introduction to fuzzy arithmetic: theory and application. New York: Van Nostrand Reinhold.

    Google Scholar 

  • Kaynak, H. (2003). The relationship between total quality management practices and their effects on firm performance. Journal of Operations Management, 21(4), 405–435.

    Google Scholar 

  • Ketokivi, M. A., & Schroeder, R. G. (2004). Strategic, structural contingency and institutional explanations in the adoption of manufacturing practices. Journal of Operations Management, 22(1), 63–89.

    Google Scholar 

  • Klein, S., & Roth, V. J. (1993). Satisfaction with international marketing channels. Journal of the Academy of Marketing Science, 21(1), 39–44.

    Google Scholar 

  • Koh, S. C. L., Saad, S. M., & Arunachalam, S. (2006). Competing in the 21st century supply chain through supply chain management and enterprise resource planning integration. International Journal of Physical Distribution and Logistics Management, 36(6), 455–465.

    Google Scholar 

  • Kohli, A. K., & Jaworski, B. J. (1990). Market orientation: The construct, research propositions, and managerial implications. Journal of Marketing, 54(2), 1–18.

    Google Scholar 

  • Kumar, P., & Deshmukh, S. G. (2006). A model for flexible supply chain through flexible manufacturing. Global Journal of Flexible Systems Management, 7(3), 17–24.

    Google Scholar 

  • Kwon, I.-W. G., & Suh, T. (2004). Factors affecting the level of trust and commitment in supply chain relationships. Journal of Supply Chain Management, 40(2), 4–14.

    Google Scholar 

  • Labahn, D. W., Ali, A., & Krapfel, R. (1997). New product development cycle time: The influence of project and process factors in small manufacturing companies. Journal of Business Research, 36(2), 179–188.

    Google Scholar 

  • Lalonde, B. J. (1998). Building a supply chain relationship. Supply Chain Management Review, 2(2), 7–8.

    Google Scholar 

  • Landis, J. R., & Koch, G. G. (1977). A one-way component of variance model for categorical data. Biometrics, 33(4), 671–679.

    Google Scholar 

  • Lawson, B., & Potter, A. (2012). Determinants of knowledge transfer in inter-firm new product development projects. International Journal of Operations & Production Management, 32(10), 1228–1247.

    Google Scholar 

  • Lee, H. I., Kang, H. Y., Hsu, C. F., & Hung, H. C. (2009). A green supplier selection model for high-tech industry. Expert System Application, 36(4), 7917–7927.

    Google Scholar 

  • Li, C. W., & Tzeng, G. H. (2009). Identification of a threshold value for the DEMATEL method using the maximum mean de-entropy algorithm to find critical services provided by a semiconductor intellectual property mall. Expert Systems with Applications, 8(1), 9891–9898.

    Google Scholar 

  • Lin, R. J. (2013). Using fuzzy DEMATEL to evaluate the green supply chain management practices. Journal of Cleaner Production, 40, 32–39.

    Google Scholar 

  • Lin, H. T., & Chang, W. L. (2008). Order selection and pricing methods using flexible quantity and fuzzy approach for buyer evaluation. European Journal of Operational Research, 187(2), 415–428.

    Google Scholar 

  • Lin, C., & Tseng, H. (2006). Identifying the pivotal role of participation strategies and information technology application for supply chain excellence. Industrial Management & Data Systems, 106(5), 739–756.

    Google Scholar 

  • Lin, Y. T., Yang, Y. H., Kang, J. S., & Yu, H. C. (2011). Using DEMATEL method to explore the core competences and causal effect of the IC design service company: An empirical case study. Expert Systems with Applications, 38(5), 6262–6268.

    Google Scholar 

  • Lings, I. (2004). Internal market orientation construct and consequences. Journal of Business Research, 57(4), 405–413.

    Google Scholar 

  • Little, D., Kenworthy, J., Jarvis, P., & Porter, K. (1995). Scheduling across the supply chain. Logistics Information Management, 8(1), 42–48.

    Google Scholar 

  • Lockstrom, M., Schadel, J., Harrison, N., Moser, R., & Malhotra, M. K. (2010). Antecedents to supplier integration in the automotive, industry: A multiple-case study of foreign subsidiaries in China. Journal of Operations Management, 28(3), 240–256.

    Google Scholar 

  • Malhotra, M. K., & Grover, V. (1998). An assessment of survey research in POM: From constructs to theory. Journal of Operations Management, 16(4), 407–425.

    Google Scholar 

  • Mangla, S. K., Kumar, P., & Barua, M. K. (2014). Flexible decision approach for analyzing performance of sustainable supply chains under risks/uncertainty. Global Journal of Flexible Systems Management, 15(2), 113–130.

  • Mavi, R. K., Kazemi, S., Najafabadi, A. F., & Mousaabadi, H. B. (2013). Identification and assessment of logistical factors to evaluate a green supplier using the fuzzy logic DEMATEL method. Polish journal of environmental studies, 22(2), 445–455.

    Google Scholar 

  • Mishra, A. A., & Shah, R. (2009). In union lies strength: Collaborative competence in new product development and its performance effects. Journal of Operations Management, 27(4), 324–338.

    Google Scholar 

  • Morgan, J., & Monczka, R. M. (1996). Supplier integration: A new level of supply chain management. Purchasing, 120, 110–113.

    Google Scholar 

  • Narasimhan, R., & Jayaram, J. (1998). Causal linkages in supply chain management: an exploratory study of North American manufacturing firms. Decision Sciences, 29(3), 579–605.

    Google Scholar 

  • Neely, A., Gregory, M. J., & Platts, K. (1995). Performance measurement system design, a literature review and research agenda. International Journal of Operations and Production Management, 15(4), 80–116.

    Google Scholar 

  • Nyaga, G., Whipple, J., & Lynch, D. (2010). Examining supply chain relationships: Do buyer and supplier perspectives on collaborative relationships differ? Journal of Operations Management, 28(2), 101–114.

    Google Scholar 

  • Oke, A., Prajogo, D. I., & Jayaram, J. (2013). Strengthening the innovation chain: The role of internal innovation climate and strategic relationships with supply chain partners. Journal of Supply Chain Management, 49(4), 43–58.

    Google Scholar 

  • Opricovic, S., & Tzeng, G. H. (2003). Defuzzification within a multi-criteria decision model. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 11(5), 635–652.

    Google Scholar 

  • Paulraj, A., Lado, A. A., & Chen, J. J. (2008). Inter-organizational communication as a relational competency: Antecedents and performance outcomes in collaborative buyer-supplier relationships. Journal of Operations Management, 26(1), 45–64.

    Google Scholar 

  • Petersen, K., Handfield, R., & Ragatz, G. (2005). Supplier integration into new product development: coordinating product, process, and supply chain design. Journal of Operations Management, 23(3–4), 371–388.

    Google Scholar 

  • Qureshi, M. N., Kumar, D., & Kumar, P. (2008). An integrated model to identify and classify the key criteria and their role in the assessment of 3PL services providers. Asia Pacific Journal of Marketing and Logistics, 20(2), 227–249.

    Google Scholar 

  • Rabelo, L., Eskandari, H., Shaalan, T., & Helal, M. (2007). Value chain analysis using hybrid simulation and AHP. International Journal of Production Economics, 105, 536–547.

    Google Scholar 

  • Ragatz, G. L., Handfield, R. B., & Scannell, T. V. (1997). Success factors for integrating suppliers into new product development. Journal of Product Innovation Management, 14(3), 190–202.

    Google Scholar 

  • Ravi, V., Shankar, R., & Tiwari, M. K. (2005). Analyzing alternatives in reverse logistics for end-of-life computers: ANP and balanced scorecard approach. Computers & Industrial Engineering, 48(2), 327–356.

    Google Scholar 

  • Saad, M., & Patel, B. (2006). An investigation of supply chain performance measurement in Indian automotive sector. Benchmarking, 13(1/2), 36–53.

    Google Scholar 

  • Shieh, J. I., Wu, H. H., & Huang, K. K. (2010). A DEMATEL method in identifying key success factors of hospital service quality. Knowledge-Based Systems, 23(3), 277–282.

    Google Scholar 

  • Sidola, A., Kumar, P., & Kumar, D. (2012). System dynamics investigation of information technology in small and medium enterprise supply chain. Journal of Advances in Management Research, 9(2), 199–207.

    Google Scholar 

  • Simatupang, T. M., & Sridharan, R. (2002). The collaborative supply chain. International Journal of Logistics Management, 13(1), 15–30.

    Google Scholar 

  • Slack, N., Chambers, S., Harland, C., Harrison, A., & Johnston, R. (1995). Operations management. London: Pitman Publishing.

    Google Scholar 

  • Sushil. (2012). Multiple perspectives of flexible systems management. Global Journal of Flexible Systems Management, 13(1), 1–2.

    Google Scholar 

  • Thakkar, J., Kanda, A., & Deshmukh, S. G. (2009). Supply chain performance measurement framework for small and medium scale enterprises. Benchmarking, 16(5), 702–723.

    Google Scholar 

  • Thome, A. M. T., Scavarda, L. F., Pires, S. R. I., Ceryno, P., & Klingebiel, K. (2014). A multi-tier study on supply chain flexibility in the automotive industry. International Journal of Production Economics, 158, 91–105.

    Google Scholar 

  • Tompkins, J., & Ang, D. (1999). What are your greatest challenges related to supply chain performance measurement? IIE Solutions, 31(6), 66.

    Google Scholar 

  • Turner, J. R. (1993). Integrated supply chain management: What’s wrong with this picture? Industrial Engineering, 25(12), 52–55.

    Google Scholar 

  • Tyagi, M., Kumar, P., & Kumar, D. (2014). Selecting alternatives for improvement in IT enabled supply chain performance. International Journal of Procurement Management, 7(2), 168–182.

    Google Scholar 

  • Tzeng, G. H., Chiang, C. H., & Li, C. W. (2007). Evaluating intertwined effects in e-learning programs: a novel hybrid MCDM model based on factor analysis and DEMATEL. Expert Systems with Applications, 32(4), 1028–1044.

    Google Scholar 

  • Ulaga, W., & Chacour, S. (2001). Measuring customer-perceived value in business markets. Industrial Marketing Management, 30(6), 525–540.

    Google Scholar 

  • Van der Vorst, J. G. A. J., & Beulens, A. J. M. (2002). Identifying sources of uncertainty to generate supply chain redesign strategies. International Journal of Physical Distribution & Logistics Management, 32(6), 409–430.

    Google Scholar 

  • Vanderhaeghe, A., & de Treville, S. (2003). How to fail at flexibility. Supply Chain Forum, 4(1), 64–67.

    Google Scholar 

  • Wadhwa, S., Madaan, J., & Avneet, S. (2007). Need for flexibility and innovation in healthcare management systems. Global Journal of Flexible Systems Management, 8(1–2), 45–54.

    Google Scholar 

  • Waggoner, D., Neely, A., & Kennerley, M. (1999). The forces that shape organizational performance measurement systems: An interdisciplinary review. International Journal of Production Economics, 60–61, 53–60.

    Google Scholar 

  • Womack, J. P., & Jones, D. T. (2005). Lean solutions: How companies and customers can create wealth together. New York: Simon & Schuster.

    Google Scholar 

  • Wu, W. W., & Lee, Y. T. (2007). Developing global managers’ competencies using the fuzzy DEMATEL method. Expert Systems with Applications, 32(2), 499–507.

    Google Scholar 

  • Yang, Y. P., Shieh, H. M., Leu, J. D., & Tzeng, G. H. (2008). A novel hybrid MCDM model combined with DEMATEL and ANP with applications. International Journal Operational Research, 5(3), 160–168.

    Google Scholar 

  • Yeh, C. H., & Deng, H. (2004). A practical approach to fuzzy utilities comparison in fuzzy multi-criteria analysis. International Journal of Approximate Reasoning, 35(2), 179–194.

    Google Scholar 

  • Zadeh, L. A. (1965). Fuzzy Set. Information and Control, 8(3), 338–353.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohit Tyagi.

Appendix

Appendix

See Tables 9, 10, 11, 12, 13.

Table 9 The linguistic scale direct-relation matrix by expert 1
Table 10 Defuzzified direct-relation-matrix by expert 1
Table 11 Average direct-relation matrix
Table 12 Normalized direct-relation matrix
Table 13 Total inter-relation matrix

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tyagi, M., Kumar, P. & Kumar, D. Assessment of Critical Enablers for Flexible Supply Chain Performance Measurement System Using Fuzzy DEMATEL Approach. Glob J Flex Syst Manag 16, 115–132 (2015). https://doi.org/10.1007/s40171-014-0085-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40171-014-0085-6

Keywords

Navigation