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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