Skip to main content
SpringerLink
Log in

Multi-echelon Supply Chain Flexibility Enhancement Through Detecting Bottlenecks

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

Abstract

This study suggests a supply chain design deploying a novel idea from production planning. The idea of capacity bottlenecks is used to improve flexibility in a multi-echelon multi-product supply chain. We suggest an optimization model that focuses on optimal capacity allocations to bottleneck points in order to enhance overall flexibility. The proposed mixed-integer linear programming model minimizes the total cost of facility establishment as well as their utilization and transportation cost. The performance of suggested model is investigated by several test problems with uncertainty in demand, cost, capacity, and product specifications. The results indicate the superiority of the suggested model to the previous flexibility formulation method. According to the numerical results, the proposed model decreases the total supply chain cost by up to 16 % on average. Another advantageous feature of the proposed model is its capability of solving previously insolvable test problems by optimizing flexibility levels.

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

Similar content being viewed by others

References

  • Anderson, D. L., & Lee, H. (1999). Synchronized supply chains: The new frontier. ASCET, 6(1), 56–59.

    Google Scholar 

  • Avittathur, B., & Swamidass, P. (2007). Matching plant flexibility and supplier flexibility: Lessons from small suppliers of US manufacturing plants in India. Journal of Operations Management, 25(3), 717–735.

    Article  Google Scholar 

  • Baffo, I., Confessore, G., & Stecca, G. (2013). A decentralized model for flow shop production with flexible transportation system. Journal of Manufacturing Systems, 32(1), 68–77.

    Article  Google Scholar 

  • Baghalian, A., Rezapour, S., & Farahani, R. Z. (2013). Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case. European Journal of Operational Research, 227(1), 199–215. doi:10.1016/j.ejor.2012.12.017.

    Article  Google Scholar 

  • Ben-Tal, A., & Nemirovski, A. (2000). Robust solutions of Linear Programming problems contaminated with uncertain data. Mathematical Programming, 88, 411–424.

    Article  Google Scholar 

  • Calantone, R., & Dröge, C. (1999). Supply chain flexibility: An empirical study. Journal of Supply Chain Management, 35(3), 16–24.

    Article  Google Scholar 

  • Chase, R. B., Aquilano, N. J., & Jacobs, F. R. (2001). Operations management for competitive advantage. Boston: Irwin McGraw-Hill.

    Google Scholar 

  • Christopher, M. (2000). The agile supply chain: Competing in volatile markets. Industrial Marketing Management, 29(1), 37–44.

    Article  Google Scholar 

  • Corominas, A. (2013). Supply chains: What they are and the new problems they raise. International Journal of Production Research, 51(23–24), 6828–6835.

    Article  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.

    Article  Google Scholar 

  • Dubey, R., Gunasekaran, A., & Childe, S. J. (2015). The design of a responsive sustainable supply chain network under uncertainty. The International Journal of Advanced Manufacturing Technology, 80(1–4), 427–445.

    Article  Google Scholar 

  • Duclos, L. K., Vokurka, R. J., & Lummus, R. R. (2003). A conceptual model of supply chain flexibility. Industrial Management & Data Systems, 103(6), 446–456.

    Article  Google Scholar 

  • Esmaeilikia, M., Fahimnia, B., Sarkis, J., Govindan, K., Kumar, A., & Mo, J. (2014). A tactical supply chain planning model with multiple flexibility options: An empirical evaluation. Annals of Operations Research, Advance Online Publication, 1–26. doi:10.1007/s10479-013-1513-2.

  • Fernandes, R., Gouveia, J. B., & Pinho, C. (2012). Product mix strategy and manufacturing flexibility. Journal of Manufacturing Systems, 31(3), 301–311.

    Article  Google Scholar 

  • Fisher, M. L. (1997). What is the right supply chain for your product? Harvard Business Review, 75, 105–117.

    Google Scholar 

  • Fuller, J. B., O’Conor, J., & Rawlinson, R. (1993). Tailored logistics: The next advantage. Harvard Business Review, 71(3), 87–98.

    Google Scholar 

  • Gilmore, J. H., & Pine, B. J. (1997). The four faces of mass customization. Harvard Business Review, 75(1), 91–101.

    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.

    Article  Google Scholar 

  • Hall, D. T., & Parker, V. A. (1993). The role of workplace flexibility in managing diversity. Organizational Dynamics, 22(1), 5–18.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Hasuike, T., & Ishii, H. (2009). On flexible product-mix decision problems under randomness and fuzziness. Omega, 37(4), 770–787.

    Article  Google Scholar 

  • Jordan, J. A., & Michel, F. J. (2000). Next generation manufacturing: Methods and techniques. New York: Wiley.

    Google Scholar 

  • Kaur, H., Singh, S. P., & Glardon, R. (2015). An integer linear program for integrated supplier selection: A sustainable flexible framework. Global Journal of Flexible Systems Management. doi:10.1007/s40171-015-0105-1.

  • Khatwani, G., Singh, S. P., Trivedi, A., & Chauhan, A. (2015). Fuzzy-TISM: A fuzzy extension of TISM for group decision making. Global Journal of Flexible Systems Management, 16(1), 97–112.

    Article  Google Scholar 

  • Koste, L. L., & Malhotra, M. K. (1999). A theoretical framework for analyzing the dimensions of manufacturing flexibility. Journal of Operations Management, 18(1), 75–93.

    Article  Google Scholar 

  • Kume, K., & Fujiwara, T. (2015). Production flexibility of real options in daily supply chain. Global Journal of Flexible Systems Management. doi:10.1007/s40171-015-0103-3.

  • Lai, G., Debo, L. G., & Sycara, K. (2009). Sharing inventory risk in supply chain: The implication of financial constraint. Omega, 37(4), 811–825.

    Article  Google Scholar 

  • Lau, R. (1996). Strategic flexibility: A new reality for world-class manufacturing. SAM Advanced Management Journal, 61, 11–15.

    Google Scholar 

  • Lee, H. L. (2002). Aligning supply chain strategies with product uncertainties. California Management Review, 44(3), 105–119.

    Article  Google Scholar 

  • Lee, S. M., & Hong, S. (2002). An enterprise-wide knowledge management system infrastructure. Industrial Management & Data Systems, 102(1), 17–25.

    Article  Google Scholar 

  • Lee, H. L., Padmanabhan, V., & Whang, S. (2004). Information distortion in a supply chain: The bullwhip effect. Management Science, 50(12_supplement), 1875–1886. doi:10.1287/mnsc.1040.0266.

  • Lummus, R. R., Duclos, L. K., & Vokurka, R. J. (2003). Supply chain flexibility: Building a new model. Global Journal of Flexible Systems Management, 4(4), 1.

    Google Scholar 

  • Martínez Sánchez, A., & Pérez, M. P. (2005). Supply chain flexibility and firm performance: A conceptual model and empirical study in the automotive industry. International Journal of Operations & Production Management, 25(7), 681–700.

    Article  Google Scholar 

  • Mason-Jones, R., Naylor, B., & Towill, D. R. (2000). Lean, agile or leagile? Matching your supply chain to the marketplace. International Journal of Production Research, 38(17), 4061–4070.

    Article  Google Scholar 

  • Miles, R. E. (1989). Adapting to technology and competition—a new industrial-relations system for the 21st-century. California Management Review, 31(2), 9–28.

    Article  Google Scholar 

  • Petridis, K. (2015). Optimal design of multi-echelon supply chain networks under normally distributed demand. Annals of Operations Research, 227(1), 63–91.

    Article  Google Scholar 

  • Prater, E., Biehl, M., & Smith, M. A. (2001). International supply chain agility-Tradeoffs between flexibility and uncertainty. International Journal of Operations & Production Management, 21(5/6), 823–839.

    Article  Google Scholar 

  • Radjou, N. (2000). Deconstruction of the supply chain. Supply Chain Management Review, 4(5), 30–38.

    Google Scholar 

  • Rajabalipour Cheshmehgaz, H., Desa, M. I., & Wibowo, A. (2013). A flexible three-level logistic network design considering cost and time criteria with a multi-objective evolutionary algorithm. Journal of Intelligent Manufacturing, 24(2), 277–293.

    Article  Google Scholar 

  • Reddy, S. B., & Reddy, R. (2002). Competitive agility and the challenge of legacy information systems. Industrial Management & Data Systems, 102(1), 5–16.

    Article  Google Scholar 

  • Ricker, F., & Kalakota, R. (1999). Order fulfillment: The hidden key to e-commerce success. Supply Chain Management Review, 11(3), 60–70.

    Google Scholar 

  • Sabri, E. H., & Beamon, B. M. (2000). A multi-objective approach to simultaneous strategic and operational planning in supply chain design. Omega, 28(5), 581–598.

    Article  Google Scholar 

  • Sahu, A. K., Sahu, S. K., Datta, S., & Mahapatra, S. S. (2015). Supply chain flexibility assessment and decision-making: A fuzzy intelligent approach. International Journal of Business Excellence, 8(6), 675–699.

    Article  Google Scholar 

  • Santoso, T., Ahmed, S., Goetschalckx, M., & Shapiro, A. (2005). A stochastic programming approach for supply chain network design under uncertainty. European Journal of Operational Research, 167(1), 96–115.

    Article  Google Scholar 

  • Schütz, P., Tomasgard, A., & Ahmed, S. (2009). Supply chain design under uncertainty using sample average approximation and dual decomposition. European Journal of Operational Research, 199(2), 409–419.

    Article  Google Scholar 

  • Seebacher, G., & Winkler, H. (2015). A capability approach to evaluate supply chain flexibility. International Journal of Production Economics, 167, 177–186.

    Article  Google Scholar 

  • Simchi-Levi, D. (2000). Designing and managing the supply chain. New York: Irwin McGraw-hill.

    Google Scholar 

  • Singh, R. K., & Acharya, P. (2013). Supply chain flexibility: A frame work of research dimensions. Global Journal of Flexible Systems Management, 14(3), 157–166.

    Article  Google Scholar 

  • Slack, N. (1987). The flexibility of manufacturing systems. International Journal of Operations & Production Management, 7(4), 35–45.

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  • Swafford, P. M., Ghosh, S., & Murthy, N. (2006). The antecedents of supply chain agility of a firm: Scale development and model testing. Journal of Operations Management, 24(2), 170–188.

    Article  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.

    Article  Google Scholar 

  • Tat Leung, Y., & Sheen, G. (1993). Resolving deadlocks in flexible manufacturing cells. Journal of Manufacturing Systems, 12(4), 291–304.

    Article  Google Scholar 

  • Thatte, A. A. (2007). Competitive advantage of a firm through supply chain responsiveness and SCM practices. PhD Dissertation, The University of Toledo.

  • Tiwari, A. K., Tiwari, A., & Samuel, C. (2015). Supply chain flexibility: A comprehensive review. Management Research Review, 38(7), 767–792.

    Article  Google Scholar 

  • Tiwari, A. K., Tiwari, A., Samuel, C., & Bhardwaj, P. (2013). Procurement flexibility as a tool for supplier selection in disastrous environments. Global Journal of Flexible Systems Management, 14(4), 211–223.

    Article  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.

    Article  Google Scholar 

  • Voudouris, V. T., & Consulting, A. (1996). Mathematical programming techniques to debottleneck the supply chain of fine chemical industries. Computers & Chemical Engineering, 20, S1269–S1274.

    Article  Google Scholar 

  • Ward, P. T., & Duray, R. (2000). Manufacturing strategy in context: Environment, competitive strategy and manufacturing strategy. Journal of Operations Management, 18(2), 123–138.

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Yu, H., Zeng, A. Z., & Zhao, L. (2009). Single or dual sourcing: decision-making in the presence of supply chain disruption risks. Omega, 37(4), 788–800.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, College of Engineering, University of Tehran, North Kargar, PO Box: 4563-11155, Tehran, Iran

    Iman Kazemian

  2. Department of Computer Science, University of Auckland, Private Bag 92019, Auckland, New Zealand

    Samin Aref

  3. Department of Computing and Information Technology, Unitec Institute of Technology, Private Bag 92025, Auckland, New Zealand

    Samin Aref

Authors
  1. Iman Kazemian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Samin Aref
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Iman Kazemian.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kazemian, I., Aref, S. Multi-echelon Supply Chain Flexibility Enhancement Through Detecting Bottlenecks. Glob J Flex Syst Manag 17, 357–372 (2016). https://doi.org/10.1007/s40171-016-0130-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40171-016-0130-8

Keywords

Search

Navigation