A Collaborative Demand Forecasting Process with Event-Based Fuzzy Judgements

Abstract

a b s t r a c tMathematical 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.

Semiconductor Yield Forecasting Using Quadratic-Programming-Based Fuzzy Collaborative Intelligence Approach

“…The initial values of the concepts and the connection weights of the fuzzy cognitive map are dependent on the subjective belief of the expert and can be modified after collaboration. Cheikhrouhou et al [23] thought that collaboration is necessary because of the unexpected events that may occur in the future demand.…”

A Fuzzy Collaborative Forecasting Approach for Forecasting the Productivity of a Factory

“…Similarly, Chen [6] defined the concept of partial consensus as the intersection of the views. Cheikhrouhou et al [28] thought that collaboration is necessary because of the unexpected events that may occur in the future demand. Chen and Wang [29] proposed an agent-based fuzzy collaborative intelligence approach, in which software agents rather than domain experts are used to improve the efficiency of collaboration.…”

Extended distribution‐free newsvendor models with demand updates using experts’ judgment

“…The method involves evaluating the demand impact of contextual information using expert judgment in an objective manner and integrating into a mathematical forecast. Cheikhrouhou et al (2011) extend the work to a situation where a group of forecasters have fragmented contextual information and partial domain knowledge. The approach involves identification and classification of four different types of potential future events and assessment of their impacts using a fuzzy inference engine that ensures the coherence of the results and limits the biases in decision making.…”