Additive manufacturing of digital spare parts offers promising new possibilities for companies to drastically shorten lead times and to omit storage costs. However, the concept of digital spare parts has not yet gained much footing in the manufacturing industry. This study aims to identify grounds for its selective rejection. Conducted from a corporate perspective, outlining a holistic supply chain network structure to visualize different digital spare part distribution scenarios, this survey study evaluates technical and economic additive manufacturing capabilities. Results are analyzed and discussed further by applying the Mann-Whitney test to examine the influence of the company size and the presence of 3D-printed end-use components within supply networks on gathered data. Machines’ limited build chamber volumes and the necessity of post-processing are considered as the main technical challenges of current additive manufacturing processes. Furthermore, it can be concluded that company sizes have a significant effect on perceived technological limitations. Overall, the results lead to the conclusion that the readiness level of the digital spare parts concept demands for further development.
PurposeThe study aims to investigate the impact of additive manufacturing (AM) on the performance of a spare parts supply chain with a particular focus on underlying spare part demand patterns.Design/methodology/approachThis work evaluates various AM-enabled supply chain configurations through Monte Carlo simulation. Historical demand simulation and intermittent demand forecasting are used in conjunction with a mixed integer linear program to determine optimal network nodal inventory policies. By varying demand characteristics and AM capacity this work assesses how to best employ AM capability within the network.FindingsThis research assesses the preferred AM-enabled supply chain configuration for varying levels of intermittent demand patterns and AM production capacity. The research shows that variation in demand patterns alone directly affects the preferred network configuration. The relationship between the demand volume and relative AM production capacity affects the regions of superior network configuration performance.Research limitations/implicationsThis research makes several simplifying assumptions regarding AM technical capabilities. AM production time is assumed to be deterministic and does not consider build failure probability, build chamber capacity, part size, part complexity and post-processing requirements.Originality/valueThis research is the first study to link realistic spare part demand characterization to AM supply chain design using quantitative modeling.
3D printing or additive manufacturing (AM) (in the industrial context) is an innovative, as opposed to subtractive, technology, bringing new opportunities and benefits to the spare part supply chain (SPSC). The aim of this work is to capture the views of the stakeholders at the end of the chain, extruding factors that will benefit the end-user and the factors that are likely to be an obstacle, by employing the questionnaire method. Company objectives regarding spares (cost reductions, improvement of services, space reduction) have been prioritized differently by the stakeholders. The most important barriers according to the participants are the quality assurance of the spare parts made by the new technology followed by the know-how and skills of staff. Other views such as suitable parts are suggested. The practical value of this work, in addition to assessing the readiness of the industry, is that it provides guidance for the successful implementation of AM in the maritime industry.
This chapter aims to investigate the potential economic and environmental sustainability outcomes of additive manufacturing (AM) for spare parts logistics. System dynamic simulation was conducted to analyze the sustainability of producing a spare part used in a railways subsystem using a particular additive manufacturing (AM) technology (i.e., selective laser sintering [SLS]) compared to producing it using injection molding. The results of the simulation showed that using SLS for the chosen part is superior to the conventional one in terms of total variable costs as well as for carbon footprint. Compared to the conventional supply chain, for the AM supply chain, the costs of the supplier reduces by 46%, that of the railways company reduces by 71%, while the overall supply chain costs reduce by 61.9%. The carbon emissions in the AM supply chain marginally reduces by 2.89% compared to the conventional supply chain.
Spare part management is one of the most important work for enterprises, especially for manufacturing enterprises; however, the spare part management problems trouble enterprise operators a lot. In this article, implementation methods of lean spare parts management are illuminated. Spare parts purchase process is declared to reduce the purchasing cost and inventory value. We had established a suitable lean spare parts inventory management model for consumable parts, wear parts, insurance parts and accident parts. In addition, methods of lean spare parts management had been created base on optimized supply chain, ERP and integrating repeated material inventory. We used SAP-iPro system and self-developed system to manage spare parts, so that warehouse management process, spare parts purchase process and maintenance process are standardized. According to theory analysis and practice, the remarkable economic benefit is created for enterprise by the means of optimizing spare parts distribution, standardizing and scientific spare parts management.
The reliability and economical operation of marine power plants depend upon the design quality, capability, and skill of the technical staff that operate the plant. The normal breakdowns of marine machinery and the frequency of planned maintenance are based mainly on the subjective
judgement of the operators who ensure that such breakdowns are minimized. To achieve such objectives, an optimum maintenance goal must be adopted using various types of computer software for expert systems with high processing speed, which have been developed to aid fault or failure diagnosis.
Such systems are at different stages of application. In this paper, a typical expert system for condition monitoring, budgeting, and spare part management to enhance the optimum management of a marine power plant is presented. The use of this technique with known models will substantially
reduce downtime and fully utilize the technical crew onboard and ashore. From the author's experience in the management of ferries, there is an optimum amount of maintenance effort for any given condition.
Evaluating the Readiness Level of Additively Manufactured Digital Spare Parts: An Industrial Perspective