Integrated planning for service tools and spare parts for capital goods

Availability of capital goods is an often-used service measure in after-sales services. The availability depends on how often maintenance is performed, and how long these maintenance actions take. This dissertation focuses on the so-called maintenance delay, i.e., the percentage of time during which the equipment cannot perform its intended function because it is waiting for either user or supplier personnel, parts, or tools associated with corrective maintenance actions. The amount of preventive maintenance, the failure rates of the equipment and the time needed for repair are assumed to be known. Maintenance delay can also be expressed as the percentage of time a machine is waiting for resources related to maintenance. Whenever a machine fails, several resources must be available before the repair action can start: a service engineer to perform the repair action, spare parts when parts within the machine are broken and service tools to support the repair action. In this dissertation, the joint supply of spare parts and service tools is considered; it is assumed that service engineers are always available and thus do not cause extra downtime. Service tools have some special characteristics that make it worthwhile to study them in detail, namely: • Coupling in demands. Whenever a machine fails, a set of service tools is needed. Because of this, the demands of these service tools are correlated. • Coupling in returns. After a repair action has been finished, the service tools are returned to the stock point again. Tools that are demanded together therefore will be returned together. Thus also the return of service tools is correlated. • Tool kits. A tool kit is a case that includes a set of service tools, such that it can be used in one or more repair actions. Tools can be stocked individually as well as in a tool kit. Due to this, substitution of demand is possible. Furthermore, spare parts and service tools have a combined impact on the maintenance delay in two ways: • Coupling in demands. Often a set of spare parts and service tools is needed. All resources need to be available before the repair action can start, and the item with the longest delivery time (regardless whether it is a tool or part) determines the delay for this action. • Service target. Towards the customer, there is only one service target, namely the availability of the machine. Whenever tools and parts are considered separately, a separate target must be set for both. This will lead to suboptimization. The goal of this thesis is to study the integrated planning of spare parts and service tools. However, due to the special characteristics of service tools, we first focus on service tools only. Chapters 2 to 5 concern various aspects of operational management of service tools. In Chapter 6 we consider the joint management of service tools and spare parts. In Chapters 1, 7 and 8, an introduction, the conclusions and a reflection are given, respectively. In Chapter 2, we study why tool kits should or should not be used. This is done by performing an empirical study on the preferences of service engineers, and the aspects that influence this preference. From this study, it follows that service engineers prefer having tool kits over having separate tools. Furthermore, a list of aspects is determined that influences this preference. Although this study led to the conclusion that ASML should continue using tool kits, the rest of this thesis does not take into account the special characteristics of tool kits, like substitution. Including the effect of substitution is future research. In Chapters 3 to 5, the single-location service tool problem with coupling in demands and coupling in returns is studied. In Chapter 3, an approximate evaluation method is developed, which is accurate and reasonably efficient. In Chapter 4, efficient bounds are proven for the average service level of the model. Finally, in Chapter 5, four heuristics are developed and compared to each other on the basis of accuracy (how close is the service level of the proposed solution from the target service level? ) and computation time, and to a lower bound on the basis of costs. The lower bound is found in several steps, using among others lagrangian relaxation and the bounds developed in Chapter 4. The average gap between the lower bound and the solutions found by the heuristics is between 11.9% and 20.3%. One of the heuristics used the evaluation method described in Chapter 3. Although this heuristic is most cost efficient and very accurate, it is very slow and therefore not appropriate to be used for real-life, large problems. Another heuristic takes coupling in demands into account in a simple way by assuming independence. This heuristic is much faster, almost equally accurate, but slightly less cost efficient. Comparing this heuristic to a heuristic used in practice for spare parts, which ignores all coupling, leads to the conclusion that both are approximately as cost efficient (average difference of 2%), but the service tool heuristic is much more accurate (a gap of 0.2% instead of 6%). The service tool heuristic, however, has a higher computation time. In Chapter 6, a multi-location service tools and spare parts problem is studied. Two of the heuristics studied in Chapter 5 are extended to a multi-location setting, and some experiments are done for a small test bed based on data seen at ASML. From this it can be concluded that the heuristic without coupling, the spare parts heuristic, is very inaccurate, and the accuracy depends on the values of the input parameters. Also, it can be seen that there is a lot of variability of the service offered to customers at different warehouses. The heuristic that includes coupling, the service tools heuristic, leads to almost the same costs, but is much more accurate and less variable. Although the computation times are much higher, it is due to this higher accuracy that the service tool heuristic is more appropriate to be used in practice. Furthermore, the impact of integration of the planning of parts and tools is studied. It can be seen that integrating the planning of parts and tools leads to more accurate results, and cost savings up to 15% for the cases in this test bed: a significant saving.