Skip to main content
Log in

A method for analysing operational complexity in supply chains

  • General Paper
  • Published:
Journal of the Operational Research Society

Abstract

This paper proposes a method for analysing the operational complexity in supply chains by using an entropic measure based on information theory. The proposed approach estimates the operational complexity at each stage of the supply chain and analyses the changes between stages. In this paper a stage is identified by the exchange of data and/or material. Through analysis the method identifies the stages where the operational complexity is both generated and propagated (exported, imported, generated or absorbed). Central to the method is the identification of a reference point within the supply chain. This is where the operational complexity is at a local minimum along the data transfer stages. Such a point can be thought of as a ‘sink’ for turbulence generated in the supply chain. Where it exists, it has the merit of stabilising the supply chain by attenuating uncertainty. However, the location of the reference point is also a matter of choice. If the preferred location is other than the current one, this is a trigger for management action. The analysis can help decide appropriate remedial action. More generally, the approach can assist logistics management by highlighting problem areas. An industrial application is presented to demonstrate the applicability of the method.

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.

Figure 1
Figure 2

Similar content being viewed by others

References

  • Alfaro MD and Sepulveda JM (2006). Chaotic behavior in manufacturing systems. International Journal of Production Economics 101 (1): 150–158.

    Article  Google Scholar 

  • Allesina S, Azzi A, Battini D and Regattieri A (2010). Performance measurement in supply chains: New network analysis and entropic indexes. International Journal of Production Research 48 (8): 2297–2321.

    Article  Google Scholar 

  • Blecker T and Abdelkafi N (2006). Complexity and variety in mass customization systems: Analysis and recommendations. Management Decision 44 (7): 908–929.

    Article  Google Scholar 

  • Calinescu A (2008). Transferable lessons from biological and supply chain networks to autonomic computing. In: Sobh T (ed) Advances in Computer and Information Sciences and Engineering. Springer: Germany, pp 51–56.

  • Choi TY, Dooley KJ and Rungtusanatham M (2001). Supply networks and complex adaptive systems: Control versus emergence. Journal of Operations Management 19 (3): 351–366.

    Article  Google Scholar 

  • Christopher M and Lee H (2004). Mitigating supply chain risk through improved confidence. International Journal of Physical Distribution & Logistics Management 34 (5): 388–396.

    Article  Google Scholar 

  • Deshmukh AV, Talavage JJ and Barash MM (1998). Complexity in manufacturing systems, Part 1: Analysis of static complexity. IIE Transactions 30 (7): 645–655.

    Google Scholar 

  • Disney SM and Towill DR (2003a). On the bullwhip and inventory variance produced by an ordering policy. OMEGA: International Journal of Management Science 31 (3): 157–167.

    Article  Google Scholar 

  • Disney SM and Towill DR (2003b). Vendor-managed inventory and bullwhip reduction in a two-level supply chain. International Journal of Operations & Production Management 23 (6): 625–651.

    Article  Google Scholar 

  • Disney SM and Towill DR (2003c). The effect of vendor managed inventory (VMI) dynamics on the bullwhip effect in supply chains. International Journal of Production Economics 85 (2): 199–215.

    Article  Google Scholar 

  • Disney SM and Towill DR (2006). A methodology for benchmarking replenishment induced bullwhip. Supply Chain Management: An International Journal 11 (2): 160–168.

  • Disney SM, Towill DR and van de Velde W (2004). Variance amplification and the golden ratio in production and inventory control. International Journal of Production Economics 90 (3): 295–309.

    Article  Google Scholar 

  • Efstathiou J, Tassano F, Sivadasan S, Shirazi R, Alves J, Frizelle G and Calinescu A (1999). Information complexity as a driver of emergent phenomena in the business community. In: International Workshop on Emergent Synthesis. Kobe University: Japan, pp 1–6.

    Google Scholar 

  • Forrester JW (1961). Industrial Dynamics. Pegasus Communications: Waltham, MA.

    Google Scholar 

  • Frizelle G (1998). The Management of Complexity in Manufacturing. Business Intelligence: London.

    Google Scholar 

  • Frizelle G and Suhov YM (2001). An entropic measurement of queueing behaviour in a class of manufacturing operations. Proceedings of the Royal Society A, Mathematical, Physical and Engineering Sciences 457 (2011): 1579–1601.

    Article  Google Scholar 

  • Frizelle G and Suhov Y (2008). The measurement of complexity in production and other commercial systems. Proceedings of the Royal Society A, Mathematical, Physical and Engineering Sciences 464 (2098): 2649–2668.

    Article  Google Scholar 

  • Frizelle G and Woodcock E (1995). Measuring complexity as an aid to developing operational strategy. International Journal of Operations & Production Management 15 (5): 26–39.

    Article  Google Scholar 

  • Gan HS and Wirth A (2005). Comparing deterministic, robust and online scheduling using entropy. International Journal of Production Research 43 (10): 2113–2134.

    Article  Google Scholar 

  • Geary S, Disney SM and Towill DR (2006). On bullwhip in supply chains—Historical review, present practice and expected future impact. International Journal of Production Economics 101 (1): 2–18.

    Article  Google Scholar 

  • Hu SJ, Zhu X, Wang H and Koren Y (2008). Product variety and manufacturing complexity in assembly systems and supply chains. CIRP Annals—Manufacturing Technology 57 (1): 45–48.

    Article  Google Scholar 

  • Huaccho Huatuco L, Efstathiou J, Calinescu A, Sivadasan S and Kariuki S (2009). Comparing the impact of different rescheduling strategies on the entropic-related complexity of manufacturing systems. International Journal of Production Research 47 (15): 4305–4325.

    Article  Google Scholar 

  • Hwarng HB, Chong CSP, Xie N and Burgess TF (2005). Modelling a complex supply chain: Understanding the effect of simplified assumptions. International Journal of Production Research 43 (13): 2829–2872.

    Article  Google Scholar 

  • Hwarng HB and Xie N (2008). Understanding supply chain dynamics: A chaos perspective. European Journal of Operational Research 184 (3): 1163–1178.

    Article  Google Scholar 

  • Isik F (2010). An entropy-based approach for measuring complexity in supply chains. International Journal of Production Research 48 (12): 3681–3696.

    Article  Google Scholar 

  • Kim I and Springer M (2008). Measuring endogenous supply chain volatility: Beyond the bullwhip effect. European Journal of Operational Research 189 (1): 172–193.

    Article  Google Scholar 

  • Kumar V (1988). Measurement of loading and operations flexibility in flexible manufacturing systems: An information-theoretic approach. Annals of Operations Research 15 (1–4): 65–80.

    Article  Google Scholar 

  • Larsen ER, Morecroft JDW and Thomsen JS (1999). Complex behaviour in a production-distribution model. European Journal of Operational Research 119 (1): 61–74.

    Article  Google Scholar 

  • Laugesen J and Mosekilde E (2006). Border-collision bifurcations in a dynamic management game. Computers & Operations Research 33 (2): 464–478.

    Article  Google Scholar 

  • Lee HL, Padmanabhan V and Whang SJ (1997). Information distortion in a supply chain: The bullwhip effect. Management Science 43 (4): 546–558.

    Article  Google Scholar 

  • Lee HL and Tang CS (1997). Modelling the costs and benefits of delayed product differentiation. Management Science 43 (1): 40–53.

    Article  Google Scholar 

  • Lin YA, Desouza KC and Roy S (2010). Measuring agility of networked organizational structures via network entropy and mutual information. Applied Mathematics and Computation 216 (10): 2824–2836.

    Article  Google Scholar 

  • Martinez-Olvera C (2008). Entropy as an assessment tool of supply chain information sharing. European Journal of Operational Research 185 (1): 405–417.

    Article  Google Scholar 

  • Monostori L and Csaji BC (2008). Complex adaptive systems (CAS) approach to production systems and organisations. In: Mitsuishi M, Ueda K and Kimura F (eds) Manufacturing Systems and Technologies for the New Frontier. Springer: London, pp 19–24.

  • Olivella J, Corominas A and Pastor R (2010). An entropy-based measurement of working time flexibility. European Journal of Operational Research 200 (1): 253–260.

    Article  Google Scholar 

  • Pathak SD, Day JM, Nair A, Sawaya WJ and Kristal MM (2007). Complexity and adaptivity in supply networks: Building supply network theory using a complex adaptive systems perspective. Decision Sciences 38 (4): 547–580.

    Article  Google Scholar 

  • Pincus SM (1991). Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences USA 88 (6): 2297–2301.

    Article  Google Scholar 

  • Piplani R and Wetjens D (2007). Evaluation of entropy-based dispatching in flexible manufacturing systems. European Journal of Operational Research 176 (1): 317–331.

    Article  Google Scholar 

  • Rivkin JW and Siggelkow N (2007). Patterned interactions in complex systems: Implications for exploration. Management Science 53 (7): 1068–1085.

    Article  Google Scholar 

  • Shannon CE (1948). A mathematical theory of communication. Bell System Technical Journal 27 (July, October): 379–423, 623-656.

    Article  Google Scholar 

  • Shuiabi E, Thomson V and Bhuiyan N (2005). Entropy as a measure of operational flexibility. European Journal of Operational Research 165 (3): 696–707.

    Article  Google Scholar 

  • Sivadasan S, Efstathiou J, Calinescu A and Huaccho Huatuco L (2001a). A discussion of the issues of state definition in the entropy-based measure of operational complexity across supplier-customer systems. Proceedings of the 5th World Multi-Conference on Systemics, Cybernetics and Informatics (SCI 2001), Vol. II, 22–25 July 2001, Orlando, Florida, USA, pp 227–232.

  • Sivadasan S, Efstathiou J, Huaccho Huatuco L and Calinescu A (2001b). Complexity associated with supplier-customer integration. In: Pham DT, Dimov SS and O'Hagan V (eds). Advances in Manufacturing Technology XV: Professional Engineering Publishing: Bury St Edmunds, 31–36.

    Google Scholar 

  • Sivadasan S, Efstathiou J, Calinescu A and Huaccho Huatuco L (2002a). Policies for managing operational complexity in the supply chain. In: Frizelle G and Richards H (eds) Tackling Industrial Complexity: The Ideas that Make a Difference. University of Cambridge, pp 549–555.

    Google Scholar 

  • Sivadasan S, Efstathiou J, Frizelle G, Shirazi R and Calinescu A (2002b). An information-theoretic methodology for measuring the operational complexity of supplier-customer systems. International Journal of Operations & Production Management 22 (1): 80–102.

    Article  Google Scholar 

  • Sivadasan S, Efstathiou J, Calinescu A and Huaccho Huatuco L (2006). Advances on measuring the operational complexity of supplier-customer systems. European Journal of Operational Research 171 (1): 208–226.

    Article  Google Scholar 

  • Sivadasan S, Smart J, Huaccho Huatuco L and Calinescu A (2010). Operational complexity and supplier-customer integration: Case study insights and complexity rebound. Journal of the Operational Research Society 61 (12): 1709–1718.

    Article  Google Scholar 

  • Slack N, Chambers S and Johnston R (2010). Operations Management. 6th edn. Financial Times Prentice-Hall: Harlow, UK.

    Google Scholar 

  • Smart J, Calinescu A and Huatuco LH (2012). Extending the information-theoretic measures of the dynamic complexity of manufacturing systems. International Journal of Production Research, doi:10.1080/00207543.2011.638677.

  • Sterman JD (1989). Modeling managerial behavior: Misperceptions of feedback in a dynamic decision making experiment. Management Science 35 (3): 321–339.

    Article  Google Scholar 

  • Surana A, Kumara S, Greaves M and Raghavan UN (2005). Supply-chain networks: A complex adaptive systems perspective. International Journal of Production Research 43 (20): 4235–4265.

    Article  Google Scholar 

  • Towill DR, Zhou L and Disney SM (2007). Reducing the bullwhip effect: Looking through the appropriate lens. International Journal of Production Economics 108 (1–2): 444–453.

    Article  Google Scholar 

  • Voss C, Tsikriktsis N and Frohlich M (2002). Case research in operations management. International Journal of Operations & Production Management 22 (2): 195–219.

    Article  Google Scholar 

  • Wahab MIM and Stoyan SJ (2008). A dynamic approach to measure machine and routing flexibilities of manufacturing systems. International Journal of Production Economics 113 (2): 895–913.

    Article  Google Scholar 

  • Wang H, Efstathiou J and Yang JB (2005). Entropy-based complexity measures for dynamic decision processes. Dynamics of Continuous Discrete and Impulsive Systems—Series B—Applications & Algorithms 12 (5–6): 829–848.

    Google Scholar 

  • Wilding RC (1998). Chaos theory: Implications for supply chain management. International Journal of Logistics Management 1 (9): 43–56.

    Article  Google Scholar 

  • Wu Y and Zhang DZ (2007). Demand fluctuation and chaotic behaviour by interaction between customers and suppliers. International Journal of Production Economics 107 (1): 250–259.

    Article  Google Scholar 

  • Wu Y, Frizelle G, Ayral L, Marsein J, Van de Merwe E and Zhou D (2002). A simulation study on supply chain complexity in manufacturing industry. In: Frizelle G and Richards H (eds). Tacking Industrial Complexity: The Ideas that Make a Difference. University of Cambridge, pp 239–248, ISBN: 1-902546-24-5.

    Google Scholar 

  • Wu Y, Frizelle G and Efstathiou J (2007). A study on the cost of operational complexity in customer-supplier systems. International Journal of Production Economics 106 (1): 217–219.

    Article  Google Scholar 

  • Yang B, Burns ND and Backhouse CJ (2004). Management of uncertainty through postponement. International Journal of Production Research 42 (6): 1049–1064.

    Article  Google Scholar 

  • Zhang T and Efstathiou J (2006). The complexity of mass customization systems under different inventory strategies. International Journal of Computer Integrated Manufacturing 19 (5): 423–433.

    Article  Google Scholar 

  • Zhou L and Disney SM (2006). Bullwhip and inventory variance in a closed loop supply chain. OR Spectrum 28 (1): 127–149.

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to express their sincere thanks to the industrial collaborators for providing the access of the case studies and the financial support for the project. The authors also gratefully acknowledge the support of EPSRC for grants GR/M52458 and GR/M57842. The authors thank the anonymous reviewers for their comments and suggestions for the improvement of the paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Y R Wu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wu, Y., Huatuco, L., Frizelle, G. et al. A method for analysing operational complexity in supply chains. J Oper Res Soc 64, 654–667 (2013). https://doi.org/10.1057/jors.2012.63

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/jors.2012.63

Keywords

Navigation