An optimal approach for the joint problem of level of repair analysis and spare parts stocking

We propose a method that can be used when deciding on how to maintain capital goods, given a product design and the layout of a repair network. Capital goods are physical systems that are used to produce products or services. They are expensive and technically complex and have high downtime costs. Examples are manufacturing equipment, defense systems, and medical devices

An iterative method for the simultaneous optimization of repair decisions and spare parts stocks

In the development process of a capital good, it should be decided how to maintain it once it is in the field. The level of repair analysis (LORA) is used to answer the questions: 1) which components to repair upon failure, and which to discard, 2) at which locations in the repair network to perform the repairs, and 3) at which locations to deploy resources, such as repair equipment. Next, it should be decided what amount of spare parts to store at each location in the network in order to guarantee a certain availability of the product. Usually, the LORA and the spare parts stocking problem are solved sequentially. However, solving the LORA first can lead to high spare parts costs. Therefore, we propose an iterative approach to solve the two problems jointly. We find that the total costs are lowered with 3.2% on average and almost 35% at maximum in our experiments. A cost reduction of a few percent may be worth hundreds of thousands of euros over the life cycle of a capital good

An Advanced Heuristic for Multiple-Option Spare Parts Procurement after End-of-Production

After-sales service is a major profit generator for more and more OEMs in industries with durable products. Successful engagement in after-sales service improves customer loyalty and allows for competitive differentiation through superior service like an extended service period after end of production during which customers are guaranteed to be provided with service parts. In order to fulfill the service guarantee in these cases, an effective and efficient spare parts management has to be implemented, which is challenging due to the high uncertainty concerning spare parts demand over such a long time horizon. The traditional way of spare parts acquisition for the service phase is to set up a huge final lot at the end of regular production of the parent product which is sufficient to fulfill demand up to the end of the service time. This strategy results in extremely high inventory levels over a long period and generates major holding costs and a high level of obsolescence risk. With increasing service time more flexible options for spare parts procurement after end of production gain more and more importance. In our paper we focus on the two most relevant ones, namely extra production and remanufacturing. Managing all three options leads to a complicated stochastic dynamic decision problem. For that problem type, however, a quite simple combined decision rule with order-up-to levels for extra production and remanufacturing turns out to be very effective. We propose a heuristic procedure for parameter determination which accounts for the main stochastic and dynamic interactions between the different order-up-to levels, but still consists of quite simple calculations so that it can be applied to problem instances of arbitrary size. In a numerical study we show that this heuristic performs extremely well under a wide range of conditions so that it can be strongly recommended as a decision support tool for the multi-option spare parts procurement problem.Spare Parts, Inventory Management, Reverse Logistics, Final Order

Strategies for dynamic appointment making by container terminals

We consider a container terminal that has to make appointments with barges dynamically, in real-time, and partly automatic. The challenge for the terminal is to make appointments with only limited knowledge about future arriving barges, and in the view of uncertainty and disturbances, such as uncertain arrival and handling times, as well as cancellations and no-shows. We illustrate this problem using an innovative implementation project which is currently running in the Port of Rotterdam. This project aims to align barge rotations and terminal quay schedules by means of a multi-agent system. In this\ud paper, we take the perspective of a single terminal that will participate in this planning system, and focus on the decision making capabilities of its intelligent agent. We focus on the question how the terminal operator can optimize, on an operational level, the utilization of its quay resources, while making reliable appointments with barges, i.e., with a guaranteed departure time. We explore two approaches: (i) an analytical approach based on the value of having certain intervals within the schedule and (ii) an approach based on sources of exibility that are naturally available to the terminal. We use simulation to get insight in the benefits of these approaches. We conclude that a major increase in utilization degree could be achieved only by deploying the sources of exibility, without harming the waiting time of barges too much

Spare parts inventory pooling: how to share the benefits?

We consider a stock point for expensive, low-usage items that is operated by multiple decision makers. Each faces a Poisson demand process, and the joint stock point is controlled by a continuous-review base stock policy with full backordering. We consider penalty costs for backorders and holding costs for stock on hand. For this model, we derive structural properties of the resulting cost function. We use these to prove not only that it is cost e�ective to share one stock point with all parties involved, but also that collaboration (inventory pooling) can be supported by a stable cost allocation, i.e., the core of the associated cooperative game is non-empty. These results hold under optimized and under exogenously given base stock levels. For the former case, we further identify a stable cost allocation that would be easy to implement in practice and that induces players to reveal their private information truthfully

Analysis of an assemble-to-order system with different review periods

We consider a single item assembled from two components. One of the components has a long leadtime, high holding cost and short review period as compared to the other one. We assume that net stocks are reviewed periodically, customer demand is stochastic and unsatisfied demand is back ordered. We analyze the system under two different policies and show how to determine the policy parameters minimizing average holding and backorder costs. First, we consider a pure base stock policy, where orders for each component are placed such that the inventory position is raised up to a given base stock level. In contrast to this, only the orders for one component follow this logic while the other orders are synchronized in case of a balanced base stock policy. Through mathematical analysis, we come up with the exact long-run average cost function and we show the optimality conditions for both policies. In a numerical study the policies are compared and the results suggest that the balanced base stock policy works better than the pure base stock policy under low service levels and when there is a big difference in the holding costs of the components

Last time buy and repair decisions for spare parts

Original Equipment Manufacturers (OEM’s) of advanced capital goods often offer service contracts for system support to their customers, for which spare parts are needed. Due to technological changes, suppliers of spare parts may stop production at some point in time. As a reaction to that decision, an OEM may place a so-called Last Time Buy (LTB) order to cover demand for spare parts during the remaining service period, which may last for many years. The fact that there might be other alternative sources of supply in the next periods\ud complicates the decision on the LTB. In this paper, we develop a heuristic method to find the near- optimal LTB quantity in presence of an imperfect repair option of the failed parts that can be returned from the field. Comparison of our method to simulation shows high approximation accuracy. Numerical experiments reveal that repair is an excellent option as\ud alternative sourcing, even if it is more expensive than buying a new part, because of postponement of the repair decisions. In addition, we show the impact of other key parameters on costs and LTB quantity