Causes and Effects of Poor Demand Forecast Accuracy A Case Study in the Swedish Automotive Industry

This study is a part of a FFI (Fordonsstrategisk Forskning och Innovation) project “Future of\nsharing schedule information in automotive industry supply chains using advanced data\nanalytics”. The study is aimed at describing the current situation in terms of accuracy of\ndemand forecasts sent from OEM companies to their suppliers within the Swedish automotive\nindustry, identifying root causes for inaccuracies in demand forecasts and their effect on the\nsuppliers. This study also aims to provide some guidance to future actions and initiatives for\nimprovement of demand forecast accuracy.\nAn extensive database of delivery schedules was used to identify current patterns in forecasting\naccuracy, utilising FAI (Forecast Accuracy Index) to analyse forecasting performance. The\nstudy employed a case methodology, studying three customers with a single supplier as the\nfocal point as a basis to find root causes and effects of poor forecasting accuracy.\nThe study found that current demand forecast accuracy was poor. Causes for poor performance\nwere found both in sales forecasts, that were used to generate the MPS and subsequently\ncomponent demand, and in the MRP systems of the customers. Inaccuracies in demand\nforecasts were found to mainly be dealt with through buffers of materials and finished\ncomponents at the supplier. Improved forecasting accuracy is expected to allow suppliers to\nlower their inventory levels, resulting in cost savings across the entire supply chain. This study\nproposes evaluation of and changes to current MRP practices, closer integration of\ncomplementary data in the sales forecasting process and employment of machine learning\nalgorithms in forecasting as promising areas for improving the accuracy of demand forecasts.\n