After-Market Spare Parts Forecasting at Sandvik Stationary Crushing & Screening

Abstract: Title: After-Market Spare Parts Forecasting at Sandvik Stationary Crushing & Screening. Authors: Arian Marofkhani and Artur Jusopov Supervisors: Professor Gudrun Kiesmüller, Lund University, Faculty of Engineering, Division of Production Management. Macarena Ribalta, Planning & Logistics Manager, Sandvik Stationary Crushing & Screening. Examiner: Professor Johan Marklund, Lund University, Faculty of Engineering, Division of Production Management. Background: Sandvik Stationary Crushing & Screening in Svedala is planning to fully roll out a forecasting system called Voyager and needs guidance in their forecasting process. Sandvik wants to explore how different forecasting methods could help to improve forecast accuracy on a SKU level as well as on a SKU/Stockroom/Customer Cluster level. Purpose: The purpose of the master thesis is to identify and propose quantitative forecasting methods with the aim to improve forecasting accuracy on a SKU and SKU/Stockroom/Customer Cluster level. Methodology: The research approach aims to fulfill the purpose of the study by performing a single case study research. The study adopts an exploratory, explanatory and a descriptive focus to gain deep insight into the research area as well as to understand the current situation of the case company - Sandvik SRP AB. The study incorporates an empirical, data driven approach to collect and filter historical data as well as apply quantitative forecasting methods. Result: The best forecasting method was determined for all ABC-XYZ classes. The best method for each ABC-XYZ class performed better than Moving Average 12. Simple Exponential Smoothing yielded the best forecast accuracy for classes AX, AY, BX, BY, CX and CY on both level 1- and 3 with an exception of class CX on level 1. SBA and Croston’s method yielded the best forecast accuracy for classes AZ, BZ and CZ on both level 1- and 3. The reliability of point forecasts seems to increase with a lower coefficient of variation in time-series. Recommendations: It is recommended that Sandvik classifies their products according to an ABC-XYZ classification, where Simple Exponential Smoothing is the recommended forecasting method for classes AX, AY, BX, BY, CX and CY on both level 1 and 3. It is also recommended that SBA and Croston’s method should be used for classes AZ, BZ and CZ on both level 1 and 3. Further, it is recommended that the forecasting accuracy is monitored with the help of a tracking signal to ensure tolerable forecasting accuracy.