Abstract
Adoption of forecasting techniques, information sharing, and data-driven decision making can potentially improve production planning, supply chain performance and resource efficiency. The impact of forecast quality on supply chain performance and production planning has been highlighted, affecting both upstream and downstream performance. Some of the major challenges for production companies are volatile customer demand information and the related forecasting processes. Due to insufficient practical use of historical demand information, its potentials for the production system are not fully utilized. This paper presents a case study in the automotive sector, analyzing historical customer demand forecasts of 6 months with a visualization tool and discussing identified forecast evolution behaviors. Furthermore, the study presents a clustering approach to expand the single-product forecast evolution analysis to a multiple-product approach. Finally, seven identified clusters of forecast evolution behavior were used to formulate some managerial insights for improvement of forecast accuracy and production system efficiency.
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