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

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/257136
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Type: Examensarbete för masterexamen
Master Thesis
Title: Causes and Effects of Poor Demand Forecast Accuracy A Case Study in the Swedish Automotive Industry
Authors: Martinsson, Teodor
Sjöqvist, Edvin
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.
Keywords: Produktion;Transport;Grundläggande vetenskaper;Hållbar utveckling;Övrig industriell teknik och ekonomi;Production;Transport;Basic Sciences;Sustainable Development;Other industrial engineering and economics
Issue Date: 2019
Publisher: Chalmers tekniska högskola / Institutionen för teknikens ekonomi och organisation
Chalmers University of Technology / Department of Technology Management and Economics
Series/Report no.: Master thesis. E - Department of Technology Management and Economics, Chalmers University of Technology, Göteborg, Sweden : E2019:075
URI: https://hdl.handle.net/20.500.12380/257136
Collection:Examensarbeten för masterexamen // Master Theses



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