UNIK-FCST: knowledge-assisted adjustment of statistical forecasts
Expert Systems with Applications: An International Journal
Interaction of judgemental and statistical forecasting methods: issues &
Management Science
Judgmental adjustment in time series forecasting using neural networks
Decision Support Systems
An experiment in linguistic synthesis with a fuzzy logic controller
International Journal of Human-Computer Studies - Special issue: 1969-1999, the 30th anniversary
Characteristics and organizational constraints of collaborative planning
Cognition, Technology and Work
Fuzzy decision support system for demand forecasting with a learning mechanism
Fuzzy Sets and Systems
Computers and Industrial Engineering
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Mathematical forecasting approaches can lead to reliable demand forecast in some environments by extrapolating regular patterns in time-series. However, unpredictable events that do not appear in historical data can reduce the usefulness of mathematical forecasts for demand planning purposes. Since forecasters have partial knowledge of the context and of future events, grouping and structuring the fragmented implicit knowledge, in order to be easily and fully integrated in final demand forecasts is the objective of this work. This paper presents a judgemental collaborative approach for demand forecasting in which the mathematical forecasts, considered as the basis, are adjusted by the structured and combined knowledge from different forecasters. The approach is based on the identification and classification of four types of particular events. Factors corresponding to these events are evaluated through a fuzzy inference system to ensure the coherence of the results. To validate the approach, two case studies were developed with forecasters from a plastic bag manufacturer and a distributor belonging to the food retailing industry. The results show that by structuring and combining the judgements of different forecasters to identify and assess future events, companies can experience a high improvement in demand forecast accuracy.