On the effect of non-optimal forecasting methods on supply chain downstream demand

IMA Journal of Management Mathematics

Volumn 23, Issue 1, 2012, Pages 81-98

  Ali, Mohammad M ^a   Boylan, John E ^a  

^a BUCKINGHAMSHIRE NEW UNIVERSITY   (United Kingdom)

Author keywords

bullwhip effect; downstream demand inference; forecast information sharing; simple moving average; single exponential smoothing; supply chain management

Indexed keywords

FORECASTING; INFORMATION MANAGEMENT; INFORMATION RETRIEVAL; OPTIMIZATION; SUPPLY CHAIN MANAGEMENT;

BULLWHIP EFFECTS; DOWNSTREAM DEMAND; EXPONENTIAL SMOOTHING; FORECAST INFORMATION; MOVING AVERAGES;

INFORMATION DISSEMINATION;

EID: 84055190082     PISSN: 1471678X     EISSN: 14716798     Source Type: Journal    
DOI: 10.1093/imaman/dpr005     Document Type: Article

References (43)

1. ALI, M. M. & BOYLAN, J. E. (2011) Feasibility principles for downstream demand inference in supply chains. J. Oper. Res. Soc., 62, 474-482.
2. ALWAN, L. C., LIU, J. J. & YAO, D.-Q. (2003) Stochastic characterization of upstream demand processes in a supply chain. IIE Trans., 35, 207-219.
3. BOX, G. E. P., JENKINS, G. M. & REINSEL, G. C. (1994) Time Series Analysis: Forecasting and Control, 3rd edn. New Jersey: Prentice-Hall.
4. BOYLAN, J. E. & JOHNSTON, F. R. (2003) Optimality and robustness of combinations of moving averages. J. Oper. Res. Soc., 54, 109-115.
5. CHANDRA, C. & GRABIS, J. (2005) Application of multi-step forecasting for restraining the bullwhip effect and improving inventory performance under autoregressive demand. Eur. J. Oper. Res., 166, 337-350. (Pubitemid 40219794)
6. CHATFIELD, C. (2003) The Analysis of Time Series: An Introduction, 6th edn. New York: Chapman & Hall.
7. CHEN, F., DREZNER, Z., RYAN, J. K. & SIMCHI-LEVI, D. (2000a) Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information. Manage. Sci., 46, 436-443. (Pubitemid 30596821)
8. CHEN, F., RYAN, J. K. & SIMCHI-LEVI, D. (2000b) The impact of exponential smoothing forecasts on the bullwhip effect. Naval Res. Logistics, 47, 269-286.
9. CHENG, T. C. E. & WU, Y. N. (2005) The impact of information sharing in a two-level supply chain with multiple retailers. J. Oper. Res. Soc., 56, 1159-1165. (Pubitemid 43090711)
10. DALRYMPLE, D. J. (1987) Sales forecasting practices-results from a United States survey. Int. J. Forecasting, 3, 379-391.
11. DANESE, P. (2007) Designing CPFR collaborations: Insights from seven case studies. Int. J. Oper. Prod. Man., 27, 181-204.
12. GAUR, V., GILONI, A. & SESHADRI, S. (2005) Information sharing in a supply chain under ARMA demand. Manage. Sci., 51, 961-969. (Pubitemid 41013685)
13. GILBERT, K. (2005) An ARIMA supply chain model. Manage. Sci., 51, 305-310. (Pubitemid 40360170)
14. GRAVES, S. C. (1999) A single-item inventory model for a nonstationary demand process. Manufact. Service Oper. Manage., 1, 50-61.
15. HAMILTON, J. D. (1994) Time Series Analysis. Princeton, NJ: Princeton University Press.
16. HOLMSTROM, J. (1998) Handling product range complexity: A case study on re-engineering demand forecasting. Bus. Process Manage. J., 43, 241-250.
17. JOHNSTON, F. R., BOYLAN, J. E., MEADOWS, M. & SHALE, E. (1999) Some properties of a simple moving average when applied to forecasting a time series. J. Oper. Res. Soc., 50, 1267-1271. (Pubitemid 32082832)
18. KIM, J. G., CHATFIELD, D., HARRISON, T. P. & HAYYA, J. C. (2006) Quantifying the bullwhip effect in a supply chain with stochastic lead time. Eur. J. Oper. Res., 173, 617-636.
19. KLASSEN, R. D. & FLORES, B. E. (2001) Forecasting practices of Canadian firms: Survey results and comparisons. Int. J. Prod. Econ., 70, 163-174. (Pubitemid 32264329)
20. LEE, H. L., PADMANABHAN, V. & WHANG, S. (1997) Information distortion in supply chains: The bullwhip effect. Manage. Sci., 43, 546-559.
21. LEE, H. L., SO, K. C. & TANG, C. S. (2000) The value of information sharing in a two-level supply chain. Manage. Sci., 46, 626-643.
22. LUONG, H. T. & PHIEN, N. H. (2007) Measure of bullwhip effect in supply chains: The case of high order autoregressive demand process. Eur. J. Oper. Res., 183, 197-209. (Pubitemid 46803122)
23. MADY, M. T. (2000) Sales forecasting practices of Egyptian public enterprises: Survey evidence. Int. J. Forecasting, 16, 359-368.
24. MCCULLEN, P. & TOWILL, D. R. (2001) Practical ways of reducing bullwhip. Inst. Oper. Manage. Control, 26, 24-30.
25. MENTZER, J. T. & COX JR, J. E. (1984) Familiarity, application, and performance of sales forecasting techniques. J. Forecasting, 3, 27-36.
26. MENTZER, J. T. & KAHN, K. B. (1995) Forecasting technique familiarity, satisfaction, usage, and application. J. Forecasting, 14, 465-476.
27. MONTGOMERY, C. & JOHNSON, L. A. (1976) Forecasting and Time Series Analysis. New York: McGraw Hill.
28. RAGHUNATHAN, S. (2001) Information sharing in a supply chain: A note on its value when demand is nonstationary. Manage. Sci., 47, 605-610. (Pubitemid 34192786)
29. RAGHUNATHAN, S. (2003) Impact of demand correlation on the value of and incentives for information sharing in a supply chain. Eur. J. Oper. Res., 146, 634-649.
30. ROBERTS, S. A. (1982) A general class of Holt-Winters type forecasting models. Manage. Sci., 28, 808.
31. SANDERS, N. R. (1992) Corporate forecasting practices in the manufacturing industry. Prod. Inventory Manage. J., 33, 54-57.
32. SANDERS, N. R. (1997) The status of forecasting in manufacturing firms. Prod. Inventory Manage. J., 38, 32-36.
33. SANDERS, N. R. & MANRODT, K. B. (1994) Forecasting practices in US corporations: Survey results. Interfaces, 24, 92-100.
34. SANDERS, N. R. & MANRODT, K. B. (2003) Forecasting software in practice: Use, satisfaction, and performance. Interfaces, 33, 90-93. (Pubitemid 37395843)
35. SAP AG (2004) R/3 System Release 3.0F Online Documentation. Walldorf, Germany: SAP AG.
36. SILVER, E. A., PYKE, D. F. & PETERSON, R. (1998) Inventory Management and Production Planning and Scheduling, 3rd edn. New York: John Wiley.
37. SKJOETT-LARSEN, T., THERNOE, C. & ANDRESEN, C. (2003) Supply chain collaboration: Theoretical perspectives and empirical evidence. Int. J. Phys. Distrib. Logist. Manage., 33, 531-549.
38. SPARKES, J. R. & MCHUGH, A. K. (1984) Awareness and use of forecasting techniques in British industry. J. Forecasting, 3, 37-42.
39. STAMATOPOULOS, I., TUENTER, R. H. & FILDES, R. A. (2006) The impact of forecasting on the bullwhip effect. Working Paper 2006/016. Lancaster University Management School.
40. SYNTETOS, A. A., BOYLAN, J. E. & DISNEY, S. M. (2009) Forecasting for inventory planning: A 50-year review. J. Oper. Res. Soc., 60, S149-S160.
41. WONG, C. Y., EL-BEHEIRY, M. M,. JOHANSEN, J. & HVOLBY, H.-H. (2007) The implications of information sharing on bullwhip effects in a toy supply chain. Int. J. Risk Assess. Manage., 7, 4-18. (Pubitemid 44846526)
42. YU, Z., YAN, H. & CHENG, T. C. E. (2002) Modelling the benefits of information sharing-based partnerships in a two-level supply chain. J. Oper. Res. Soc., 53, 436-446. (Pubitemid 34410653)
43. ZHANG, X. (2004) The impact of forecasting methods on the bullwhip effect. Int. J. Prod. Econ., 88, 15-27.