Additive Manufacturing of Slow-Moving Automotive Spare Parts: A Supply Chain Cost Assessment

This study develops a cost model for the additive manufacturing (AM)-produced spare parts supply chain in the automotive industry. Moreover, we evaluate the economic feasibility of AM for slow-moving automotive spare parts by comparing the costs of the traditional manufacturing (TM) spare parts supply chain (SPSC) with centralized, outsourced AM SPSC. Data from a multiple case study of an OEM in the automotive industry regarding SPSC is utilized. The supply chain costs of 14 individual spare parts were analyzed, and the total SPSC cost for the AM and TM, were compared. Three of the fourteen parts showed potential for cost-savings, if they were produced with AM instead of TM. In this context, AM polymer parts showed greater potential than metal to replace TM as the more economical option of manufacturing from a total supply chain cost perspective. This study shows that the AM competitiveness to TM, from a financial perspective, increases for spare parts with low demand, high minimum order quantity, and high TM production price. The SPSC cost model included: cost of production, transport, warehousing, and service costs. This study contributes to the emerging field of part identification for AM and the existing literature regarding cost modeling in SPSCs

Optimise initial spare parts inventories: an analysis and improvement of an electronic decision tool.

Control of spare parts is very difficult as demands can be very low (once in a few years is no exception), while the consequences of a stockout can be severe. While in the past many companies choose to have very large spares inventories, one now observe trends in areas with good transportation connections to keep spare parts at the suppliers. Hence it is very important to make a good selection of which spare parts to stock at the start-up of new plants. To this end Shell Global Solutions has developed an electronic decision tool, called E-SPIR. In this report we analyse the decision rules used in it. We consider stockout penalties and advise to use criticality classifications instead. Furthermore, we investigate minimum stock levels, demand distributions and order quantities.

An overview of inventory systems with several demand classes

In this chapter we discuss inventory systems where several demand classes may be distinguished. In particular, we focus on single-location inventory systems and we analyse the use of a so-called critical level policy. With this policy some inventory is reserved for high-priority demand. A number of practical examples where several demand classes naturally arise are presented, and the implications and modelling of the critical level policy in distribution systems are discussed. Finally, an overview of the literature on inventory systems with several demand classes is given

A framework to assess the sustainability of additive manufacturing for spare parts

Additive manufacturing (AM) is a promising technology for the optimization of the spare parts supply chain. A complete evaluation of whether it is advantageous to switch to this technology for spare parts management should include a comprehensive assessment of its sustainability in addition to its techno-economic viability. General analyses of the economic, environmental, and social impacts of AM have been conducted, but assessments of the sustainability effects of AM in the spare parts field is limited to specific industries. Thus, based on the literature, we designed a framework that can support a life cycle evaluation of the emerging application of AM technology. It represents a methodological approach that covers all the stages of the spare parts life cycle and the three dimensions of sustainability. It has been designed to support both researchers and practitioners who are considering AM for the manufacturing of spare parts. Copyright (C) 2022 The Authors

Optimise initial spare parts inventories: an analysis and improvement of an electronic decision tool.

Control of spare parts is very difficult as demands can be very low (once in a few years is no exception), while the consequences of a stockout can be severe. While in the past many companies choose to have very large spares inventories, one now observe trends in areas with good transportation connections to keep spare parts at the suppliers. Hence it is very important to make a good selection of which spare parts to stock at the start-up of new plants. To this end Shell Global Solutions has developed an electronic decision tool, called E-SPIR. In this report we analyse the decision rules used in it. We consider stockout penalties and advise to use criticality classifications instead. Furthermore, we investigate minimum stock levels, demand distributions and order quantities

Analysis and Control for Heavy Equipment Spare Parts Inventory in the Nickel Mining Industry

In the mining industry, analyzing, and controlling the spare parts inventory is important activities and need special attention from the company. The high production cost of the mining industry, including spare parts and shortages costs affects the financial and operational aspects of the company. Spare parts inventory management policy is critical to increase the service level and minimize spare parts cost. In this mining case study, the out of stock incidents in the heavy equipment spare parts were often occurring and significantly affecting the performance of maintenance and operations. The purpose of this study is to determine minimum stock (safety stock) and maximum stock to reduce overstocks and reduce the number of stocks out in spare parts inventory and as well as inventory cost. The method used to solve the problem is economic order quantity (EOQ) and continuous review control (S, s) to determine the minimum and maximum stocks. Determining the combination of S and s has a major impact on the success of inventory control. Substitution parts are subdivided into alphabetical values to determine their significance. As a result of calculating inventory value using EOQ and S, s will get the highest inventory value to reduce inventory costs