Eindhoven University of Technology MASTER Forecasting Spare Parts

Eindhoven University of Technology MASTER Forecasting spare parts demand using information about future maintenance sessions Baart, A.L.J. Award date: 2014 Link to publication Disclaimer This document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Student theses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the document as presented in the repository. The required complexity or quality of research of student theses may vary by program, and the required minimum study period may vary in duration. General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain Eindhoven, November 2014 Forecasting spare parts demand using information about future maintenance sessions by Arno Baart Student identity number 0722055 in partial fulfilment of the requirements for the degree of Master of Science in Operations Management and Logistics Supervisors: Dr.ir. J.J. Arts, Eindhoven University of Technology Dr. K.H. van Donselaar, Eindhoven University of Technology Ir. B. Huisman, NedTrain L. Dirkx, NedTrain P.H.E.M. Dassen, NedTrain TUE. School of Industrial Engineering Series Master Theses Operations Management and Logistics Subject headings: Maintenance, Spare parts, Forecasting, Intermittent Demand Abstract In this master thesis project, a new method for forecasting intermittent demand is introduced and subsequently used in a case study which was executed at NedTrain, a Dutch company which performs maintenance of rolling stock. The new method developed in this thesis takes information about maintenance sessions in future into account and has been compared with the following well-known methods from literature: Moving Average, Exponential Smoothing, Croston’s method and the Approximation of Syntetos and Boylan. Moreover, the above mentioned forecasting methods have been compared with the forecasts generated by NedTrain’s software program. The performance of every forecasting method was assessed by calculating the mean error, the mean squared error and the mean absolute deviation. In addition, also some ‘rough’ stock calculations were made. The results show that the forecast accuracy of the forecasts generated with NedTrain’s software program can be improved. However, the differences in forecast accuracy between the different forecasting methods disappear to a large extent when ‘rough’ stock levels are calculated. Keywords Maintenance, Spare parts, Forecasting, Intermittent Demand I II Preface This report is the concluding work of my master Operations Management and Logistics at Eindhoven University of Technology. I am glad that I had the opportunity to conduct my graduation project at NedTrain and especially at a maintenance depot. The reason for this is that being present at a maintenance depot emphasizes the practical relevance of improving things. Moreover, being present at the core business of a company ensures that all relevant processes for your project become much clearer because you can actually see and experience them by yourself. I learned a lot during the past months. Besides executing my project, I visited several meetings and saw how projects are addressed and supervised in practice. I had a great time at NedTrain and therefore I would like to thank some people. First, I would like to thank Bob Huisman, who gave me the opportunity to graduate at NedTrain and especially at a maintenance depot. Furthermore, he solved an issue which was essential for the continuation of my project. Secondly, I would like to thank Luc Dirkx and Pascal Dassen from the maintenance depot of NedTrain in Maastricht. I learned a lot from Luc’s critical view and his way of thinking. I would also like to thank Pascal for sharing his experience and for all necessary facilitating support. Furthermore, I would like to thank Maurice Vuijk, Luc Smeets, Joffrey Kayser, Robert Vialle and Jan van Deun who provided me with the necessary data for this thesis and who were there for answering my questions. In addition, I would like to thank all other people from maintenance depot Maastricht who helped me during my project. Finally, I would like to thank my supervisors from the university. I want to thank Joachim Arts, who was my first supervisor, for his critical view and for listening to my ‘thesis problems’. In addition, he always helped with finding potential solutions with which I could proceed with my project. I would also like to thank Karel van Donselaar, who was my second supervisor. He came up with critical questions with which I was able to improve my work. Arno Baart III Executive Summary Problem definition NedTrain performs maintenance activities of rolling stock. However, in order to execute replacement activities, sufficient stock levels of spare parts need to be available. To determine these stock levels, accurate forecasts of the demand are required. However, forecasting spare parts demand is not easy. Demand for spare parts is often intermittent, which means that the demand pattern contains a lot of periods with zero demand and that demand arrives infrequently. In addition, the demand sizes, when demand occurs, may have a large variance. Intermittent demand items can be very expensive. Inventory for parts with intermittent demand may take up to 60% of the total investment in stock (Johnston et al., 2003). This means that stocking these items in an inappropriate amount leads to unnecessarily high costs. Therefore, small improvements in managing these items may already lead to substantial cost savings (Eaves and Kingsman, 2004; Syntetos, Babai, Dallery, & Teunter, 2009). Approach The objective of this thesis was to improve the forecast accuracy of spare parts demand at NedTrain. Therefore, the forecast accuracy of several demand forecasting methods has been compared by using real intermittent demand data from NedTrain. Demand forecasts generated with NedTrain’s software program XelusParts were compared with demand forecasts made by several well-known methods from literature and a new method which was developed in this thesis. The methods from literature used in this comparative study are Moving Average, Exponential Smoothing, Croston’s method and the Approximation of Syntetos and Boylan. Two variants of the new method were elaborated in this thesis: one with and one without prior knowledge about future maintenance sessions. The variant with prior knowledge knows exactly when every capital good will be maintained beforehand (including the type of maintenance session). However, the one without prior knowledge tries to forecast when every capital good will be maintained in future. Both variants estimate the number of parts of a certain type which will be needed when a certain type of maintenance session will be executed in the next month. These estimates are made for every type of maintenance session by making use of exponential smoothing. Multiplying these number with the (expected) number of maintenance sessions during the next month and adding up all calculated numbers results finally in the total demand forecast for the next period for the specific spare part under consideration. IV Results The results from the comparative study show that the mean error and the mean squared error of both variants of the new method are better than the mean error and the mean squared error of the demand forecasts generated by XelusParts. However, Exponential Smoothing and the Approximation of Syntetos and Boylan actually outperform both variants of the new method. The comparative study even shows that the demand forecasts generated by XelusParts lead to the worst forecast accuracy from all methods compared in this study, which means that all other methods perform better. Potential solutions which might improve the forecasts accuracy of XP are: using unique parameter values for every part, implementing the forecasting methods in XP in the same way as defined in literature and generating unrounded forecasts. Moreover, the results from ‘rough’ stock calculations show that the differences in stock levels are much more modest than the differences in forecast accuracy. However, in order to come up with accurate stock level conclusions, more exact stock calculations (i.e. calculations where the on hand stock is tracked after every occurrence) are needed. These more exact stock calculations are a subject for further research. Furthermore, the performance of NedTrain’s formula for calculating the 푘-factor of the safety stock was tested. The results show that the performance of this formula is poor. The reason for this is that the service level (i.e. fill rate) realized with this formula deviates significantly from pre-determined service levels. Both variants of the new method try to take (expected) fluctuations in the number of maintenance sessions per month into account. However, based on the results of this case study, both variants of the new method were not able to achieve an advantage. However, it is expected that when the number of spare parts needed for executing maintenance sessions is strongly correlated with the number of maintenance sessions executed in that period, that the new method will outperform all other methods. Simply because the new method is able to take (expected) fluctuations in the number of maintenance session during every month into account. Methods such as Moving Average or the Approximation of Syntetos and Boylan are not able to incorporate such information beforehand and only use this information in a reactive way. Furthermore, the second gain of the new method deals with the diversity of types of maintenance sessions for which a particular part is needed.

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