Estimating obsolescence risk from demand data - a case study

In this paper obsolescence of service parts is analyzed in a practical environment. Based on the analysis, we propose a method that can be used to estimate the risk of obsolescence of service parts. The method distinguishes groups of service parts. For these groups, the risk of obsolescence is estimated using the behavior of similar groups of service parts in the past. The method uses demand data as main information source, and can therefore be applied without the use of an expert's opinion. We will give numerical values for the risk of obsolescence obtained with the method, and the e®ects of these values on inventory control will be examined

Maintenance Centered Service Parts Inventory Control

High-tech capital goods enable the production of many services and articles that have become a part of our daily lives. Examples include the refineries that produce the gasoline we put in our cars, the photolithography systems that enable the production of the chips in our cell phones and laptops, the trains and railway infrastructure that facilitate public transport and the aircraft that permit us to travel long distances. To prevent costly production disruptions of such systems when failures occur, it is crucial that service parts are readily available to replace any failed parts. However, service parts represent significant investments and failures are unpredictable, so it is unclear which parts should be stocked and in what quantity. In this thesis, analytical models and solution methods are developed to aid companies in making this decision. Amongst other things, we analyze systems in which multiple parts need replacement after a failure, a situation that is frequently encountered in practice. This affects the ability to complete repairs in a timely fashion. We develop new modeling techniques in order to successfully apply scalable deterministic approaches, such as column generation techniques and sample average approximation methods, to this stochastic problem. This leads to solution techniques that, unlike traditional methods, can ensure that all parts needed to complete maintenance are readily available. The approach is capable of meeting the challenging requirements of a real-life repair shop

Spare parts inventory control for an aircraft component repair shop

We study spare parts inventory control for a repair shop for aircraft components. Defect components that are removed from the aircraft are sent to such a shop for repair. Only after inspection of the component, it becomes clear which specific spare parts are needed to repair it, and in what quantity they are needed. Market requirements on shop performance are reflected in fill rate requirements on the turn around times of the repairs for each component type. The inventory for spare parts is controlled by independent min-max policies. Because parts may be used in the repair of different component types, the resulting optimization problem has a combinatorial nature. Practical instances may consist of 500 component types and 4000 parts, and thus pose a significant computational challenge. We propose a solution algorithm based on column generation. We study the pricing problem, and develop a method that is very efficient in (repeatedly) solving this pricing problem. With this method, it becomes feasible to solve practical instances of the problem in minutes

Risk-based stock decisions for projects

In this report we discuss a model that can be used to determine stocking levels using the data that comes forward from a Shell RCM analysis and the data available in E-SPIR. The model is appropriate to determine stock quantities for parts that are used in redundancy situations, and for parts that are used in different pieces of equipment with different downtime costs. Estimating the annual production loss using the model consists of a number of steps. First, we need to determine which spares are used for the repairs of which failure modes. In the second step, we estimate the average waiting time for spares as a function of the number of spares stocked. In the third step, the annual downtime costs are determined. We combine the downtime costs with the holding costs to determine the optimal number of parts to stock

Integrating Reliability Centered Maintenance and Spare Parts Stock Control

In the classical approach to determine how many spare parts to stock, the spare parts shortage costs or the minimum fill rate are a key factor. A difficulty with this approach lies in the estimation of these shortage costs or the determination of appropriate minimum fill rates. In an attempt to overcome this problem, we propose to use the data gathered in reliability centered maintenance studies to determine shortage costs. We discuss benefits of this approach. At the same time, the approach gives rise to complications, as the RCM study determines downtime costs of the underlying equipment, which have a complex relation with the shortage cost for spare parts in case multiple pieces of equipment have different downtime costs. A further complication is redundancy in the equipment. We develop a framework that enables the modelling of these more complicated systems. We propose an approximative, analytic method based on the model that can be used to determine minimum stock quantities in case of redundancy and multiple systems. In a quantitative study we show that the method performs well. Moreover, we show that including redundancy information in the stocking decision gives significant cost benefits

Finding optimal policies in the (S - 1, S ) lost sales inventory model with multiple demand classes

This paper examines the algorithms proposed in the literature for finding good critical level policies in the (S-1,S) lost sales inventory model with multiple demand classes. Our main result is that we establish guaranteed optimality for two of these algorithms. This result is extended to different resupply assumptions, such as a single server queue. As a corollary, we provide an alternative proof of the optimality of critical level policies among the class of all policies

Simultaneous optimization of speed and buffer times for robust transportation systems

Transport companies often have a published timetable. To maintain timetable reliability despite delays, companies include buffer times during timetable development, and adjust the traveling speed during timetable execution. We develop an approach that can integrate decisions at different time scales (tactical and operational). We model execution of the timetable as a stochastic dynamic program (SDP). An SDP is a natural framework to model random events causing (additional) delay, propagation of delays, and real-time speed adjustments. However, SDPs alone cannot incorporate the buffer allocation, as buffer allocation requires to choose the same action in different states of the SDP. Our objective is finding the buffer allocation that yields the SDP which has minimal long run average costs. We derive several analytical insights into the model. We prove tha

The benefits of combining early aspecific vaccination with later specific vaccination

Timing is of crucial importance for successful vaccination. To avoid a large outbreak, vaccines are administered preferably as quickly as possible. However

Literature review: The vaccine supply chain

Vaccination is one of the most effective ways to prevent and/or control the outbreak of infectious diseases. This medical intervention also brings about many logistical questions. In the past years, the Operations Research/Operations Management community has shown a growing interest in the logistical aspects of vaccination. However, publications on vaccine logistics often focus on one specific logistical aspect. A broader framework is needed so that open research questions can be identified more easily and contributions are not overlooked.In this literature review, we combine the priorities of the World Health Organization for creating a flexible and robust vaccine supply chain with an Operations Research/Operations Management supply chain perspective. We propose a classification for the literature on vaccine logistics to structure this relatively new field, and identify promising research directions. We classify the literature into the following four components: (1) product, (2) production, (3) allocation, and (4) distribution. Within the supply chain classification, we analyze the decision problems for existing outbreaks versus sudden outbreaks and developing countries versus developed countries. We identify unique characteristics of the vaccine supply chain: high uncertainty in both supply and demand; misalignment of objectives and decentralized decision making between supplier, public health organization and end customer; complex political decisions concerning allocation and the crucial

Literature Review - the vaccine supply chain

Vaccination is one of the most effective ways to prevent the outbreak of an infectious disease. This medical intervention also brings about many logistical quest