Abstract
Forecasting demand at the individual stock-keeping-unit (SKU) level often necessitates the use of statistical methods, such as exponential smoothing. In some organizations, however, statistical forecasts will be subject to judgemental adjustments by managers. Although a number of empirical and ‘laboratory’ studies have been performed in this area, no formal OR modelling has been conducted to offer insights into the impact such adjustments may have on supply chain performance and the potential development of mitigation mechanisms. This is because of the associated dynamic complexity and the situation-specific nature of the problem at hand. In conjunction with appropriate stock control rules, demand forecasts help decide how much to order. It is a common practice that replenishment orders may also be subject to judgemental intervention, adding further to the dynamic system complexity and interdependence. The system dynamics (SD) modelling method can help advance knowledge in this area, where mathematical modelling cannot accommodate the associated complexity. This study, which constitutes part of a UK government funded (EPSRC) project, uses SD models to evaluate the effects of forecasting and ordering adjustments for a wide set of scenarios involving: three different inventory policies; seven different (combinations of) points of intervention; and four different (combinations of) types of judgmental intervention (optimistic and pessimistic). The results enable insights to be gained into the performance of the entire supply chain. An agenda for further research concludes the paper.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Akkermans H and Dellaert N (2005). The rediscovery of industrial dynamics: The contribution of system dynamics to supply chain management in a dynamic and fragmented world. Syst Dynam Rev 21: 173–186.
Barlas Y and Ozevin MG (2004). Analysis of stock management gaming experiments and alternative ordering formulations. Syst Res Behav Sci 21: 439–470.
Blattberg RC and Hoch SJ (1990). Database models and managerial intuition: 50% model + 50% manager. Mngt Sci 36: 887–899.
Cachon GP and Lariviere M (1999). Capacity allocation using past sales: When to turn-and-earn. Mngt Sci 45: 685–703.
Chen F, Drezner Z, Ryan JK and Simchi-Levi D (2000a). Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information. Mngt Sci 46: 436–443.
Chen F, Ryan JK and Simchi-Levi D (2000b). The impact of exponential smoothing forecasts on the bullwhip effect. Nav Res Logist 47: 269–286.
Cheung KL and Zhang AX (1999). The impact of inventory information distortion due to customer order cancellations. Nav Res Logist 46: 213–231.
Clark TE and McCracken MW (2001). Tests of equal forecast accuracy and encompassing for nested models. J Econom 105: 85–110.
Clark TE and McCracken MW (2005). Evaluating direct multi-step forecasts. Econom Rev 24: 369–404.
Croson R and Donohue K (2005). Upstream versus downstream information and its impact on the bullwhip effect. Syst Dynam Rev 21: 249–260.
Davis JP, Eisenhardt KM and Bingham C (2007). Developing theory through simulation methods. Acad Mngt Rev 32: 480–499.
Eroglu C (2006). An investigation of accuracy, learning and biases in judgmental adjustments of statistical forecasts. PhD thesis, Ohio State University, USA.
Eroglu C and Croxton KL (2010). Biases in judgmental adjustments of statistical forecasts: The role of individual differences. Int J Forecast 26: 116–133.
Fildes R, Goodwin P, Lawrence M and Nikolopoulos K (2009). Effective forecasting and judgmental adjustments: An empirical evaluation and strategies for improvement in supply-chain planning. Int J Forecast 25: 3–23.
Forrester JW (1958). Industrial dynamics—A major breakthrough for decision makers. Harvard Bus Rev 36: 37–66.
Forrester JW (1961). Industrial Dynamics. Cambridge, MA: MIT Press; currently available from Pegasus Communications: Waltham, MA.
Franses PH (2007). Does experts’ adjustment to model-based forecasts contribute to forecast quality? Econometric Institute Report 2007-37, Erasmus University Rotterdam, The Netherlands.
Franses PH and Legerstee R (2009). Properties of expert adjustments on model-based SKU-level forecasts. Int J Forecast 25: 35–47.
Gavirneni S (2006). Price fluctuations, information sharing and supply chain performance. Eur J Opl Res 174: 1651–1663.
Georgantzas NC (2001). Simulation modelling. In: Warner M (ed). International Encyclopedia of Business and Management, 2nd edn. London, UK: Thomson Learning, pp 5861–5872.
Georgantzas NC (2003). Tourism dynamics: Cyprus’ hotel value chain and profitability. Syst Dynam Rev 19: 175–212.
Georgantzas NC (2009). Scenario-driven planning with system dynamics, In: Meyers B (ed). Encyclopedia of Complexity and System Science. Springer, NY, Entry # 573, p. 23.
Kleijnen JPC and Smits MT (2003). Performance metrics in supply chain management. J Opl Res Soc 54: 507–514.
Kolassa S, Shutz W, Boylan JE, and Syntetos AA (2008). Judgemental changes to forecasts: Higher inventories, unchanged out-of-stocks. 28th International Symposium on Forecasting, Nice, France.
Lane DC and Schwaninger M (2008). Theory building with system dynamics: Topic and research contributions. Syst Res Behav Sci 25(4): 439–445.
Lee HL, Padmanabhan V and Whang S (1997a). Information distortion in a supply chain: The Bullwhip effect. Mngt Sci 43: 546–558.
Lee HL, Padmanabhan V and Whang S (1997b). The Bullwhip Effect in supply chains. Sloan Mngt Rev 38: 93–102.
Lee HL, So KC and Tang CS (2000). The value of information sharing in a two-level supply chain. Mngt Sci 46: 626–643.
Otto A and Kotzab H (2003). Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain. Eur J Opl Res 144: 306–320.
Paik S-K and Bagchi PK (2007). Understanding the causes of the bullwhip effect in a supply chain. Int J Retail Distrib Mngt 35: 308–324.
Potter A and Disney SM (2006). Bullwhip and batching: An exploration. Int J Prod Econ 104: 408–418.
Pujawan IN (2004). The effect of lot sizing rules on order variability. Eur J Opl Res 159: 617–635.
Reiner G and Fichtinger J (2009). Demand forecasting for supply processes in consideration of pricing and market information. Int J Prod Econ 118: 55–62.
Richmond B (2009). Think® Software (version 9.1.3). iSee Systems™: Lebanon, NH.
Saeed K (2009). Can trend forecasting improve stability in supply chains? A response to Forrester’s challenge in Appendix L of Industrial Dynamics. Syst Dynam Rev 25: 63–78.
Sterman JD (1994). Learning in and about complex systems. Syst Dynam Rev 10: 291–330.
Sterman JD (2000). System Dynamics: Systems Thinking and Modelling for a Complex World. Boston, MA: Irwin McGraw-Hill.
Syntetos AA and Boylan JE (2008). Demand forecasting adjustments for service-level achievement. IMA J Mngt Math 19: 175–192.
Syntetos AA, Boylan JE and Disney SM (2009b). Forecasting for inventory planning: A 50-year review. J Opl Res Soc 60(S1): 149–160.
Syntetos AA, Nikolopoulos K, Boylan JE, Fildes R and Goodwin P (2009a). The effects of integrating management judgement into intermittent demand forecasts. Int J Prod Econ 118: 72–81.
Syntetos AA, Nikolopoulos K and Boylan JE (2010). Judging the judges through accuracy-implication metrics: The case of inventory forecasting. Int J Forecast 26: 134–143.
Wong CY, El-Beheiry MM, Johansen J and Hvolby HH (2007). The implications of information sharing on bullwhip effects in a toy supply chain. Int J Risk Assess Mngt 7: 4–18.
Yasarcan H and Barlas Y (2005). A generalized stock control formulation for stock management problems involving composite delays and secondary stocks. Syst Dynam Rev 21: 33–68.
Zhang X (2004). Evolution of ARMA demand in supply chains. Manuf Serv Opns Mngt 6: 195–198.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Copyright information
© 2018 The Author(s)
About this chapter
Cite this chapter
Syntetos, A.A., Georgantzas, N.C., Boylan, J.E., Dangerfield, B. (2018). Judgement and Supply Chain Dynamics. In: Kunc, M. (eds) System Dynamics. OR Essentials. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-349-95257-1_8
Download citation
DOI: https://doi.org/10.1057/978-1-349-95257-1_8
Published:
Publisher Name: Palgrave Macmillan, London
Print ISBN: 978-1-349-95256-4
Online ISBN: 978-1-349-95257-1
eBook Packages: Business and ManagementBusiness and Management (R0)