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

Abstract

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