PhD Defence Sha Zhu
In her dissertation Sha Zhu presented three contributions to the problem of obsolescence of large spare parts inventories for capital goods. First, a new method based on extreme-value theory is developed to aid companies in forecasting the spare parts demand distribution. Next, the inventory control problem for on-condition maintenance and shutdown maintenance is analyzed. A new approach for joint forecasting and inventory control based on probabilistic information on the maintenance plan is proposed. It was found that the value of this plan is significant in preparing the repair shop by catching the irregularity and lumpiness of spare parts demand. Finally, a model is established to model the spare parts ordering problem against the background of shutdown maintenance project planning. Decision makers need strategies which consider the interdependence of maintenance activities. This new stochastic programming approach is able to give much better advice than traditional methods and hence meets the requirement of real-life shutdown projects. Sha Zhu has defended her dissertation on Thursday, 16 September at 10:30h. Her supervisors were Prof. Rommert Dekker (ESE) and Dr Willem van Jaarsveld (TU Eindhoven). The members of the Doctoral Committee were Prof. Robert Boute (KU Leuven), Prof. Chuqiao Zhou (RSM), Prof. Zumbul Atan (TU Eindhoven), Prof. Aris Syntetos (Cardiff University), Prof. Sena Eruguz (VU Amsterdam), and Prof. Morteza Pourakbar (RSM).
About Sha Zhu
Sha Zhu (1987) obtained her Bachelor’s degree in Transportation Planning and Management from Jilin University in 2010. In 2013 she received her Master’s degree in Logistic Engineering in Shanghai Jiao Tong University.
Sha Zhu joined the Erasmus Research Institute of Management (ERIM) in October 2013 as a PhD student under the supervision of prof. dr. ir. Rommert Dekker and dr. Willem van Jaarsveld. She worked on spare parts demand forecasting and inventory management problems. Her work has been published in European Journal of Operational Research, Reliability Engineering & System Safety and International Journal of Production Economics. She has presented her research at various national and international conferences, including IFORS, EURO and ISIR International Symposium on Inventories. Her research interests include operations research, demand forecasting and inventory management.
During her PhD project she assisted in various courses, primarily Advanced Inventory Supply Chain Management and Production Planning and Scheduling.
Thesis Abstract
Capital goods are expensive machines or products that are used by manufacturers to produce their end-products. Examples include computers, production equipment, aircrafts and lithography machines that are used by semi-conductor manufacturers. Availability of spare parts is essential to facilitate their maintenance both to correct failures as well as to prevent these. Large spare parts inventories however, tie up significant capital and face the risk of obsolescence. Hence smart decisions are needed on inventories: when to stock and in which quantity. These decisions should be based on good forecasts.
In this dissertation we present three contributions to this problem. First, a new method based on extreme-value theory is developed to aid companies in forecasting the spare parts demand distribution. Next, we analyze the inventory control problem for on-condition maintenance and shutdown maintenance. We propose a new approach for joint forecasting and inventory control based on probabilistic information on the maintenance plan. We found the value of this plan to be significant in preparing the repair shop by catching the irregularity and lumpiness of spare parts demand. Finally, we model the spare parts ordering problem against the background of shutdown maintenance project planning. Decision makers need strategies which consider the interdependence of maintenance activities. Our new stochastic programming approach is able to give much better advice than traditional methods and hence meets the requirement of real-life shutdown projects.
