Abstract
Demand forecasts are crucial to drive supply chains and enterprise resource planning systems. Improved accuracy in forecasts directly affects all levels of the supply chain, reducing stock costs and increasing customer satisfaction. Usually, this problem is faced by testing various time series methods with a different level of complexity to find out which one is the most accurate. From our point of view, the problem should be re-addressed. In this sense, the effort should be focused on incorporating more efficient sources of information that are frequently overlooked. This paper explores different sources of information (apart from past observations) that might enhance the capability of a company to produce accurate forecasts. Such sources are: (i) Judgmental forecasting at SKU level and (ii) Information sharing. Additionally, new models are proposed to integrate such information well. Data collected from a manufacturer of household cleaning products and a major UK grocery retailer are used to illustrate the procedure.
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Notes
- 1.
SDP algorithms are avaible in the MATLAB toolbox: CAPTAIN http://www.es.lancs.ac.uk/cres/captain/
References
Blattberg RC, Hoch SJ (1990) Database models and managerial intuition: 50% model + 50% manager. Manage Sci 36:887–899
Cannella S, Ciancimino E (2009) On the bullwhip avoidance phase: supply chain collaboration and order smoothing. Int J Prod Res 1:1–38
Fildes R, Goodwin P, Lawrence M, 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
Franses PH, Legerstee R (2009) Properties of expert adjustments on model-based SKU-level forecasts. Int J Forecast 25:35–47
Geary S, Disney SM, Towill DR (2006) On bullwhip in supply chains-historical review, present practice and expected future impact. Int J Prod Econ 101:2–18
Jakeman AJ, Young PC (1984) Recursive filtering and the inversion of ill-posed causal problems. Utilitas Mathematica 35:351–376
Lee HL, Padmanabhan V, Whang S (1997) The bullwhip effect in supply chains. Sloan Manage Rev 38:93–102
Mello J (2009) The impact of sales forecast game playing on supply chains. Foresight Int J Appl Forecasting 13:13–22
Young PC, McKenna P, Bruun J (2001) Identification of non-linear stochastic systems by state dependent parameter estimation. Int J Control 74:1837–1857
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© 2012 Springer-Verlag London Limited
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Trapero, J.R., Pedregal, D.J. (2012). Supply Chain Demand Forecasting: Towards an Integrated Approach. In: Sethi, S., Bogataj, M., Ros-McDonnell, L. (eds) Industrial Engineering: Innovative Networks. Springer, London. https://doi.org/10.1007/978-1-4471-2321-7_31
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DOI: https://doi.org/10.1007/978-1-4471-2321-7_31
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