Series ESD-WP-2006-11 ADVANCES IN SUPPLY CHAIN MANAGEMENT : POTENTIAL TO IMPROVE FORECASTING ACCURACY Shoumen

Working Paper Series Abstract Forecasting is a necessity almost in any operation. However, the tools of forecasting are still primitive in view of the great strides made by research and the increasing abundance of data made possible by automatic identification technologies, such as, RFID. The relationship of various parameters that may change and impact decisions are so abundant that any credible attempt to drive meaningful associations are in demand to deliver the value from acquired data. This paper proposes some modifications to adapt an advanced forecasting technique (GARCH) with the aim to develop it as a decision support tool applicable to a wide variety of operations including supply chain management. We have made an attempt to coalesce a few different ideas toward a " solutions " approach aimed to model volatility and in the process, perhaps, better manage risk. It is possible that industry, governments, corporations, businesses, security organizations, consulting firms and academics with deep knowledge in one or more fields, may spend the next few decades striving to synthesize one or more models of effective modus operandi to combine these ideas with other emerging concepts, tools, technologies and standards to collectively better understand, analyze and respond to uncertainty. However, the inclination to reject deep rooted ideas based on inconclusive results from pilot projects are a detrimental trend and begs to ask the question whether one can aspire to build an elephant using mouse as a model. Forecasting is an ancient activity and has become more sophisticated in recent years. For a long time steady steps in a time series data set, such as simple trends or cycles (such as seasonals) were observed and extended into the future. However, now a mixture of time series, econometrics and economic theory models can be employed to produce several forecasts which can then be interpreted jointly or combined in sensible fashions to give a superior value. The variable being forecast is a random variable. Originally attention was largely directed towards the mean of this variable; later to the variance, and now to the whole marginal distribution. Pre-testing of the data to find its essential features has become important and that has produced modern techniques such as cointegration. The horizon over which the forecast is attempted is also important, and longer-run forecasts are now being considered as well as forecasts of "breaks" in the series. The question of evaluation of forecasts has also been …