Skip to main content
SpringerLink
Log in

Metrics for bullwhip effect analysis

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

Abstract

A bullwhip measurement system based on a two-criterion assessment—‘internal process efficiency’ and ‘customer service level’—is developed in this paper. The framework is designed to assess both individual (single member) and systemic (whole supply chain) performances. Data collection and calculation methods, update and monitoring mechanisms, as well as related procedures for each metric used, are detailed. A comparative analysis with a recent work by Barlas and Gunduz is performed, showing that the adoption of the proposed performance measurement system can help academics and practitioners to better understand, study and avoid the bullwhip effect. Such analysis also provides evidence on the relevance of considering when analysing the bullwhip effect in supply chains, the ‘customer importance’ aspect that is often forgotten in the published literature.

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

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6

Similar content being viewed by others

References

  • Arzu Akyuz G and Erman Erkan T (2010). Supply chain performance measurement: A literature review. International Journal of Production Research 48 (17): 5137–5155.

    Article  Google Scholar 

  • Banomyong R and Supatn N (2011). Developing a supply chain performance tool for SMEs in Thailand. Supply Chain Management 16 (1): 20–31.

    Article  Google Scholar 

  • Barlas Y and Gunduz B (2011). Demand forecasting and sharing strategies to reduce fluctuations and the bullwhip effect in supply chains. Journal of the Operational Research Society 62 (3): 458–473.

    Article  Google Scholar 

  • Bayraktar E, Lenny Koh SC, Gunasekaran A, Sari K and Tatoglu E (2008). The role of forecasting on bullwhip effect for E-SCM applications. International Journal of Production Economics 113 (1): 193–204.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Brun A, Fahmy Salama K and Gerosa M (2009). Selecting performance measurement systems: Matching a supply chain's requirements. European Journal of Industrial Engineering 3 (3): 336–362.

    Article  Google Scholar 

  • Butner K (2010). The smarter supply chain of the future. Strategy and Leadership 38 (1): 22–31.

    Article  Google Scholar 

  • Cachon G (1999). Managing supply chain demand variability with scheduled ordering policies. Management Science 458 (6): 843–856.

    Article  Google Scholar 

  • Cachon GP and Olivares M (2010). Drivers of finished-goods inventory in the US automobile industry. Management Science 56 (1): 202–216.

    Article  Google Scholar 

  • Cachon GP, Randall T and Schmidt GM (2007). In search of the bullwhip effect. Manufacturing and Service Operations Management 9 (4): 457–479.

    Article  Google Scholar 

  • Cagnazzo L, Taticchi P and Brun A (2010). The role of performance measurement systems to support quality improvement initiatives at supply chain level. International Journal of Productivity and Performance Management 59 (2): 163–185.

    Article  Google Scholar 

  • Cai J, Liu X, Xiao Z and Liu J (2009). Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment. Decision Support Systems 46 (2): 512–521.

    Article  Google Scholar 

  • Cannella S and Ciancimino E (2010). On the bullwhip avoidance phase: Supply chain collaboration and order smoothing. International Journal of Production Research 48 (22): 6739–6776.

    Article  Google Scholar 

  • Cannella S, Ciancimino E and Márquez AC (2008). Capacity constrained supply chains: A simulation study. International Journal of Simulation and Process Modelling 4 (2): 139–147.

    Article  Google Scholar 

  • Cannella S, Ciancimino E, Framinan JM and Disney SM (2010). The four supply chain archetypes. Universia Business Review (26): 134–149.

  • Cannella S, Ciancimino E and Framinan JM (2011). Inventory policies and information sharing in multi-echelon supply chains. Production Planning & Control 22 (7): 649–659.

    Article  Google Scholar 

  • Chandler AD (1990). Scale and Scope. Belknap Press: Cambridge, MA.

    Google Scholar 

  • Chen F, Drezner Z, Ryan JK and Simchi-Levi D (2000). Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information. Management Science 46 (3): 436–443.

    Article  Google Scholar 

  • Christopher M and Holweg M (2011). ‘Supply chain 2.0’: Managing supply chains in the era of turbulence. International Journal of Physical Distribution and Logistics Management 41 (1): 63–82.

    Article  Google Scholar 

  • Coppini M, Rossignoli C, Rossi T and Strozzi F (2010). Bullwhip effect and inventory oscillations analysis using the beer game model. International Journal of Production Research 48 (13): 3943–3956.

    Article  Google Scholar 

  • Dejonckheere J, Disney SM, Lambrecht MR and Towill DR (2003). Measuring and avoiding the bullwhip effect: A control theoretic approach. European Journal of Operational Research 147 (3): 567–590.

    Article  Google Scholar 

  • Dejonckheere J, Disney SM, Lambrecht MR and Towill DR (2004). The impact of information enrichment on the Bullwhip effect in supply chains: A control engineering perspective. European Journal of Operational Research 153 (3): 727–750.

    Article  Google Scholar 

  • De Kok T, Janssen F, Van Doremalen J, Van Wachem E, Clerkx M and Peeters W (2005). Philips Electronics synchronizes its supply chain to end the bullwhip effect. Interfaces 35 (1): 37–48.

    Article  Google Scholar 

  • Disney SM and Lambrecht MR (2008). On replenishment rules, forecasting, and the bullwhip effect in supply chains. Foundations and Trends in Technology, Information and Operations Management 2 (1): 1–80.

    Article  Google Scholar 

  • Disney SM and Towill DR (2002). A discrete transfer function model to determine the dynamic stability of a vendor managed inventory supply chain. International Journal of Production Research 40 (1): 179–204.

    Article  Google Scholar 

  • Disney SM and Towill DR (2003). On the bullwhip and inventory variance produced by an ordering policy. Omega 31 (3): 157–167.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Disney SM, Naim MM and Potter A (2004). Assessing the impact of e-business on supply chain dynamics. International Journal of Production Economics 89 (2): 109–118.

    Article  Google Scholar 

  • El-Beheiry M, Wong CY and El-Kharbotly A (2004). Empirical quantication of the bullwhip effect. In: Proceedings of the 13th Working Seminar on Production Economics, 16–20 February, Igls/Innsbruck, Austria, pp 3, 259–274.

  • Estampe D, Lamouri S, Paris J-L and Brahim-Djelloul S (2010). A framework for analysing supply chain performance evaluation models. International Journal of Production Economics. doi:10.1016/j.ijpe.2010.11.024.

  • Fioriolli JC and Fogliatto FS (2008). A Model to quantify the Bullwhip Effect in systems with stochastic demand and lead time. IEEE International Conference on Industrial Engineering and Engineering Management, doi: 10.1109/IEEM.2008.4738040.

  • Forrester J (1961). Industrial Dynamics. MIT Press: Cambridge, MA.

    Google Scholar 

  • Forslund H and Jonsson P (2009). Obstacles to supply chain integration of the performance management process in buyer-supplier dyads: The buyers’ perspective. International Journal of Operations and Production Management 29 (1): 77–95.

    Article  Google Scholar 

  • Fransoo JC and Wouters MJF (2000). Measuring the bullwhip effect in the supply chain. Supply Chain Management 5 (2): 78–89.

    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 Special Issue): 2–18.

    Article  Google Scholar 

  • Graves SC and Willems SP (2003). Supply chain design: Safety stock placement and supply chain configuration. In: de Kok AG and Graves SC (eds). Handbooks in OR & MS. Vol. 11 Elsevier: Amsterdam, The Netherlands.

    Google Scholar 

  • Gunasekaran A and 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.

    Article  Google Scholar 

  • Hammond JH (1994). Barilla SpA (A). Harvard Business School Case 694-046.

  • Haughton MA (2009). Distortional bullwhip effects on carriers. Transportation Research Part E: Logistics and Transportation Review 45 (1): 172–185.

    Article  Google Scholar 

  • Hausman WH (2003). Supply chain performance metrics. In: Harrison TP, Lee HL and Neale JJ (eds). The Practice of Supply Chain Management: Where Theory and Practice Converge. Kluwer Academic Publishing: Boston, MA, pp. 61–73.

    Google Scholar 

  • Holweg M and Disney SM (2005). The evolving frontiers of the bullwhip problem. In: EUROMA Conference Proceedings, Budapest, Hungary, 19–22 June, pp 707–716.

  • Holweg M, Disney SM, Holmström J and Småros J (2005). Supply chain collaboration: Making sense of the strategy continuum. European Management Journal 23 (2): 170–181.

    Article  Google Scholar 

  • Hosoda T and Disney SM (2006). The governing dynamics of supply chains: The impact of altruistic behaviour. Automatica 42 (8): 1301–1309.

    Article  Google Scholar 

  • Keebler JS and Plank RE (2009). Logistics performance measurement in the supply chain: A benchmark. Benchmarking 16 (6): 785–798.

    Article  Google Scholar 

  • Kleijnen JPC (2005). Supply chain simulation tools and techniques: A survey. International Journal of Simulation and Process Modelling 1 (1/2): 82–89.

    Article  Google Scholar 

  • Kleijnen JPC and Smits MT (2003). Performance metrics in supply chain management. Journal of the Operational Research Society 54 (5): 507–514.

    Article  Google Scholar 

  • Lee HL (2010). Taming the bullwhip. Journal of Supply Chain Management 46 (1): 7.

    Article  Google Scholar 

  • Lee HL and Billington C (1992). Managing supply chain inventory pitfalls and opportunities. Sloan Management Review 33 (3): 65–73.

    Google Scholar 

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

    Article  Google Scholar 

  • Lee HL, Padmanabhan V and Whang S (2006). The bullwhip effect: Reflections. In: Carranza Torres OA and Villegas Moran FA (eds). The Bullwhip Effect in Supply Chains: A Review of Methods, Components and Cases. Palgrave Macmillan: New York, pp 1–14.

    Google Scholar 

  • Lehtinen J and Ahola T (2010). Is performance measurement suitable for an extended enterprise? International Journal of Operations & Production Management 30 (2): 181–204.

    Article  Google Scholar 

  • Magee JF (1958). Production Planning and Inventory Control. McGraw-Hill: New York.

    Google Scholar 

  • Makridakis S, Andersen A, Carbone R, Fildes R, Hibon M, Lewandowski R, Newton J, Parzen R and Winkler R (1982). The accuracy of extrapolation (time series) methods: Results of a forecasting competition. Journal of Forecasting 1 (2): 111–153.

    Article  Google Scholar 

  • Miragliotta G (2006). Layers and mechanisms: A new taxonomy for the bullwhip effect. International Journal of Production Economics 104 (2): 365–381.

    Article  Google Scholar 

  • Mitchell T (1924). Competitive illusion as a cause of business cycles. Quarterly Journal of Economics 38 (4): 631–652.

    Article  Google Scholar 

  • Nudurupati SS, Bititci US, Kumar V and Chan FTS (2011). State of the art literature review on performance measurement. Computers and Industrial Engineering 60 (2): 279–290.

    Article  Google Scholar 

  • Papakiriakopoulos D and Pramatari K (2010). Collaborative performance measurement in supply chain. Industrial Management and Data Systems 110 (9): 1297–1318.

    Article  Google Scholar 

  • Potter A and Lalwani C (2008). Investigating the impact of demand amplification on freight transport. Transportation Research Part E: Logistics and Transportation Review 44 (5): 835–846.

    Article  Google Scholar 

  • Reichhart A, Framinan JM and Holweg M (2008). On the link between inventory and responsiveness in multi-product supply chains. International Journal of Systems Science 39 (7): 677–688.

    Article  Google Scholar 

  • Riddalls CE and Bennett S (2001). The optimal control of batched production and its effect on demand amplification. International Journal of Production Economics 72 (2): 159–168.

    Article  Google Scholar 

  • Riddalls CE, Bennett S and Tipi NS (2000). Modelling the dynamics of supply chains. International Journal of Systems Science 31 (8): 969–976.

    Article  Google Scholar 

  • Sajadi SM, Seyed Esfahani MM and Sörensen K (2011). Production control in a failure-prone manufacturing network using discrete event simulation and automated response surface methodology. International Journal of Advanced Manufacturing Technology 53 (1–4): 35–46.

    Article  Google Scholar 

  • Schmenner RW (2001). Looking ahead by looking back: Swift, even flow in the history of manufacturing. Production and Operations Management 10 (1): 87–96.

    Article  Google Scholar 

  • Sharma S (2010). Policies concerning decisions related to quality level. International Journal of Production Economics 125 (1): 146–152.

    Article  Google Scholar 

  • Shepherd C and Günter H (2006). Measuring supply chain performance: Current research and future directions. International Journal of Productivity and Performance Management 55 (3–4): 242–258.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Strozzi F, Bosch J and Zaldívar JM (2007). Beer game order policy optimization under changing customer demand. Decision Support Systems 42 (4): 2153–2163.

    Article  Google Scholar 

  • Syntetos AA, Babai MZ, Dallery Y and Teunter R (2009a). Periodic control of intermittent demand items: Theory and empirical analysis. Journal of the Operational Research Society 60 (5): 611–618.

    Article  Google Scholar 

  • Syntetos AA, Boylan JE and Disney SM (2009b). Forecasting for inventory planning: A 50-year review. Journal of the Operational Research Society 60 (5): 149–160.

    Article  Google Scholar 

  • Syntetos AA, Georgantzas NC, Boylan JE and Dangerfield BC (2011). Judgement and supply chain dynamics. Journal of the Operational Research Society 62 (6): 1138–1158.

    Article  Google Scholar 

  • Teunter RH and Duncan L (2009). Forecasting intermittent demand: A comparative study. Journal of the Operational Research Society 60 (3): 321–329.

    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 

  • Warburton RDH (2004). An analytical investigation of the Bullwhip Effect. Production and Operations Management 13 (2): 150–160.

    Article  Google Scholar 

  • Wong WP and Wong KY (2008). A review on benchmarking of supply chain performance measures. Benchmarking 15 (1): 25–51.

    Article  Google Scholar 

  • Wouters M (2009). A developmental approach to performance measures—Results from a longitudinal case study. European Management Journal 27 (1): 64–78.

    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 

  • Zhou L, Disney S and Towill DR (2010). A pragmatic approach to the design of bullwhip controllers. International Journal of Production Economics 128 (2): 556–568.

    Article  Google Scholar 

Download references

Acknowledgements

We wish to thank the anonymous referees for insightful comments on earlier versions of the paper. We also would like to express our gratitude to Dr Elena Ciancimino for her contribution to the development of the proposed framework. This research was funded by a grant from the Portuguese Foundation for Science and Technology, grant SFRH/BPD/68576/2010.

Author information

Authors and Affiliations

  1. Technical University of Lisbon, Porto Salvo, Portugal

    S Cannella, A P Barbosa-Póvoa & S Relvas

  2. University of Seville, Seville, Spain

    J M Framinan

Authors
  1. S Cannella
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A P Barbosa-Póvoa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J M Framinan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S Relvas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S Cannella.

Additional information

Correction

In a previous version of this article the cited article by Barlas and Gunduz was wrongly dated to 2001. This reference has been corrected to “Barlas and Gunduz (2011)”.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Cannella, S., Barbosa-Póvoa, A., Framinan, J. et al. Metrics for bullwhip effect analysis. J Oper Res Soc 64, 1–16 (2013). https://doi.org/10.1057/jors.2011.139

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/jors.2011.139

Keywords

Search

Navigation