Moving to additive manufacturing for spare parts supply

This study seeks to investigate when and how a transition to additive manufacturing (AM) becomes profitable for the low-volume spare parts business. As a starting point, we conducted a case study at an OEM of radar systems which foresaw various opportunities that become available with the transition to AM. In particular, it is the case company that can perceive the prospects of shortened lead times and the promise of tool-less manufacturing. However, scepticism regarding whether a transition will pay off amid high AM piece prices and uncertain AM technology advancements remains. We employed stochastic dynamic programming to assess the situation encountered at the company. Therefore, we regarded particularities such as a decreasing AM piece price over the course of the service horizon and determined if (and when) AM should be prepared or tooling be discarded. It turned out that an immediate investment in AM technology is the most effective strategy and leads to more than 12% cost savings. Numerical experiments further substantiate the results of the case study and indicate that long lead times, high inventories, and severe backorder costs in the classical situation are all arguments in favor of an early investment in AM technology; this occurs despite an (initially) higher AM piece price and additional setup costs. Moreover, we observed that postponing the investment in AM is often not advisable. Instead, conventional manufacturing and AM are recommended to be used in parallel before making a complete transition to AM

Improving effectiveness of spare parts supply by additive manufacturing as dual sourcing option

The low-volume spare parts business is often identified as a potential beneficiary of additive manufacturing (AM) technologies. Currently, high AM unit costs or low AM part reliabilities deem the application of AM economical inferior to conventional manufacturing (CM) methods in most cases. In this paper, we investigate the potential to overcome these deficiencies by combining AM and CM methods. For that purpose, we develop an approach that is tailored toward the unique characteristics of dual sourcing with two production methods. Opposed to the traditional dual sourcing literature, we consider the different failure behavior of parts produced by AM and CM methods. Using numerical experiments and a case study in the aviation industry, we explore under which conditions dual sourcing with AM performs best. Single sourcing with AM methods typically leads to higher purchasing and maintenance costs while single sourcing with CM methods increases backorder and holding costs. Savings of more than 30% compared to the best single sourcing option are possible even if the reliability or unit costs of a part sourced with AM are three times worse than for a CM part. In conclusion, dual sourcing methods may play an important role to exploit the benefits of AM methods while avoiding its drawbacks in the low-volume spare parts business

Part Selection for Freeform Injection Molding:Framework for Development of a Unique Methodology

The purpose of this study is to provide an overview of a methodology, which will enable industrial end-users to identify potential components to be manufactured by Freeform Injection Molding (FIM). The difference between the technical and economic criteria needed for part selection for Additive Manufacturing (AM) and FIM will be discussed, which will lead us towards proposing a new methodology for part selection for FIM. Our proposed approach starts by identifying the most similar components (from end-user part libraries) to some reference parts, which can be produced by FIM. Identification will be followed by cluster analysis based on important factors for FIM part selection. As there are some interdependency between the factors involved in the clusters, some decision rules using Fuzzy Interference System (FIS) will be applied to rank the parts within each cluster using user-defined technical and economic criteria. Once the first set of potential FIMable parts have been identified, Design of Experiment (DOE) will be conducted to investigate which factors are most important and how they interact with each other to generate the desirable quality of the FIM parts. The DOE results will be validated in order to finetune the ranges of the parameters, which gives the best results. Finally, a predictive model will be developed based on the optimum feasible range of FIM parameters. This will help the end-users to analytically find the new FIMable parts without repeating the algorithm for the new parts

Maintenance service logistics

Capital goods, such as manufacturing equipment, trains, and Industrial printers, are used in the primary processes of their users. Their availability is of key importance. To achieve high availability, maintenance is required throughout their long life cycles. Many different resources such as spare parts, service engineers and tools, are necessary to perform maintenance. In some cases, e.g. for trains, also maintenance facilities are required. Maintenance service logistics encompasses all processes that ensure that the resources required for maintenance are at the right place at the right time. In a broader sense, it also includes maintenance planning and design-for-maintenance. We first discuss capital goods and the requirements that their users have, which leads us to basic maintenance principles and the structure of typical service supply chains. Next, various relevant decisions and supporting theories and models are discussed. Finally, we discuss the latest developments within maintenance service logistics