Spare Parts Supply Chain Research Articles

Revolutionizing the Marine Spare Parts Supply Chain through Additive Manufacturing: A System Dynamics Simulation Case Study

This research investigates the potential of additive manufacturing (AM) to revolutionize the marine spare parts supply chain (SPSC). While AM has been widely explored in other industries, its application in the maritime sector remains limited. This study addresses this gap by comparing traditional and AM-integrated SPSC models using system dynamics simulation. By focusing on high- and low-value spare parts, this research highlights significant cost reductions and improved delivery times through strategic AM integration. Our findings demonstrate the potential of AM to enhance the efficiency and resilience of the maritime supply chain, particularly in remote environments.

High-temperature aging and long-term mechanical properties of polyphenylene sulfide composites for marine use

ABSTRACT Advanced polymeric composites and manufacturing techniques promote developments of marine structures significantly (e.g., spare parts supply chain of shipbuilding and maintenance). In this paper, the GNP/CF-PPS (graphene nanoplatelets short carbon fibre polyphenylene sulfide composite) samples are prepared by mechanically mixing CF-PPS plastic pellets and GNP particles. The mixed compound is 3D-printed by material extrusion additive manufacturing, and the bending mechanics test is carried out to explore how nanoreinforcement affects the ability of printed composites to perform under high temperature conditions. The results show that CF-PPS and GNP/CF-PPS samples have the highest flexural strength at room temperature. As the temperature rises to 260°C, the stiffness increases by 4.8% and 6.7% respectively, while the deflection decreases by 30.4% and 25.6% respectively. The characterisation results of scanning electron microscopy (SEM) show that the cross-sectional pores of CF-PPS samples after adding GNP are relatively reduced, indicating the improvement in the structural stability.

Evaluation of critical success determinants to the implementation of additive manufacturing technology in the spare parts supply chain: a grey causal modelling approach

PurposeAdditive Manufacturing technology (AMT) is swiftly gaining prominence to induce automation and innovation in manufacturing systems. It holds immense potential to change supply chain dynamics by providing the possibility of printing objects on demand. This study thus formulates and analyzes the framework to incorporate AMT to handle the spare parts supply chain management (SPSCM) in capital-intensive industries by identifying and assessing the critical success factors (CSFs).Design/methodology/approachAssessment of the CSFs is performed using the novel Grey Causal Modeling method (GCM) with the objective of making SPSCM resilient and efficient. GCM conducts causal analysis by taking into consideration cause, effects, the objectives, and the situations.FindingsFindings indicate that; Logistics Lead Time (SD4), Time to manufacture (SD3), Management Support (SD11), and Risk Management (SD20) are the most prominent causal factor having a maximum impact when incorporating AMT in SPSCM. The results also reveal that the performance of manufacturing organizations that adopt AMT is substantially influenced by internal and external factors such as Management Support (SD11) and Government Regulations (SD16).Research limitations/implicationsThis research provides valuable information for getting the global spare parts supply chain equipped for the post-COVID age, where digital technologies such as AMT will be fundamental for bolstering supply chain resilience and efficiency.Originality/valueThis research proposes a framework for performance assessment when incorporating AMT in SPSCM. Study also demonstrates methodological application of novel Grey Causal Modelling technique using a real case in a spare parts manufacturing industry in India.

A digital twin-enabled digital spare parts supply chain

An efficient and effective spare parts supply chain management is crucial to ensure highly available production systems. However, achieving such a spare parts supply chain management is challenging due to the intrinsic characteristics of spare parts. Traditional spare parts supply chains (TSPSCs) adopt large spare parts inventories to tackle such intrinsic characteristics, with disadvantages in terms of high inventory costs. Recently, Additive Manufacturing (AM) has shown breakthrough potential, allowing the transition from TSPSCs to digital spare parts supply chains (DSPSCs), where spare parts are no longer stored in large amounts but are stored digitally and produced only when needed. However, despite DSPSCs benefits, a complete transition from TSPSCs to DSPSCs is still far from becoming reality. Indeed, hindering such a transition is a lack of expertise in three main areas, i.e. how to design, operate, and control and monitor a DSPSC. With this position paper, we envision how a digital twin (DT) could overcome these issues, enabling a full transition to a DSPSC. Moreover, this work further concretely suggests how such a DT could be developed, providing the basis for developing both the theoretical and practical knowledge necessary to render DSPSCs a reality.

Operating minimally intelligent agent-based manufacturing systems across the Average demand Interval – coefficient of variation (ADI-CV) demand state space

ABSTRACT Minimally Intelligent Agent-Based Manufacturing concerns the provision of agents with the minimal intelligence required to autonomously negotiate and broker work across machines and jobs. The minimal intelligence of agents can be updated in real-time and when coupled with technologies, such as Additive Manufacturing (AM), robotics, automated inventory, and Computer Numerical Control (CNC) affords highly flexible, re-configurable, resilient and responsive systems. The concept has become topical as changes to global supply chains are necessitating a shift to responsive and resilient on-demand manufacturing. However, understanding and characterising how these systems operate under various demand profiles is required to support operators operating these future systems. This paper reports a numerical study into the operating behaviour of minimally intelligent agent-based manufacturing systems operating across the Average Demand Interval and Coefficient of Variation (ADI-CV) demand state space. An established state space used in spare part supply chain research. The results show minimally intelligent manufacturing systems are stable across much of the ADI-CV demand space 85%, 63% and 76–84% for constant, triangular and lognormal manufacturing time distribution profiles respectively. The stability is largely independent of the combination of intelligence. Rather, the combination of intelligence impacts job Time-in-System and system response.

A Simulation-Based Bayesian Network Approach to the Joint Decision of the International Transportation Mode and Safety Inventory Policy

Uncertainty in international transportation can have a significant impact on inventory costs and customer service levels in a global supply chain network. A very limited number of studies in the literature have addressed the international transportation mode and safety inventory policy (ITM-SIP) issues simultaneously. This study formulates the problem and proposes a simulation-based Bayesian network (SBN) approach for the joint decisions on ITM-SIP, where Bayesian networks are used to dynamically estimate international replenishment lead times for railway and maritime transportation modes. We implement our approaches in an international automotive spare parts supply chain experiencing fluctuations in its shipping supply market and with dynamic demand. For this joint decision problem, we build an international supply chain network simulation model in which the time-delay uncertainties of key bottleneck nodes in maritime and railway transportation routes are considered. The simulation experiment results show the performance improvement of the SBN approach in substantially reducing total costs and achieving service levels. We also provide insights into the value of decision tactics for responding to different fluctuation modes in the shipping supply market in the global supply chain network.

Case Article—Digitizing Spare Parts Supply Chain via 3D Printing: An Operational Cost Analysis

The case presents a sourcing problem and a manufacturing problem faced by an original equipment manufacturer, seeking recommendations for sourcing a diverse range of parts for high-voltage equipment, as well as making decisions on the manufacturing strategy for a component used in a water monitoring system. The case provides an opportunity to explore the qualitative and quantitative aspects of three-dimensional (3D) printing versus traditional manufacturing, specifically in terms of operational cost. Furthermore, this case facilitates discussions on the potential impact of 3D printing on supply chains. It is suitable for use in graduate and undergraduate courses, as it introduces key concepts such as manufacturing and inventory policies, queueing theory, and life cycle analysis. Ultimately, the case is designed to promote a deeper understanding of the challenges and opportunities that manufacturers face in today’s rapidly evolving technological landscape. Supplemental Material: The Teaching Note and supplemental data are available at https://www.informs.org/Publications/Subscribe/Access-Restricted-Materials .

Case—Digitizing Spare Parts Supply Chain via 3D Printing: An Operational Cost Analysis

Case—Digitizing Spare Parts Supply Chain via 3D Printing: An Operational Cost Analysis

A reliability-based methodology for resilient spare parts planning and control

Geo-political conflicts, financial crises, natural disasters, pandemics, human operational errors, and other disruptive events can affect supply chain integrity with unpredictable consequences. In addition, intermittent demand patterns and strict service requirements enhance the complexity of Spare Parts Supply Chains (SPSCs). SPSC resilience has become a significant challenge in today’s increasingly competitive business environment.This study presents a reliability-based methodology with a customer-oriented perspective to support industrial companies in spare parts planning and control. This original approach provides resilience to SPSCs by defining a statistical variable to study supply chain variabilities and externalities, aiding stockout risk control, and suggesting spare parts management plans. The application of the proposed methodology in a case study of a packaging machine company is illustrated.

Numerical Analysis of A Spare Parts Supply Chain With Additive Manufacturing

With recent successes, the use of additive manufacturing (AM) has shown to be a viable alternative for the production of high quality spare parts in remote military or humanitarian missions. However, effective methods for finding inventory policies in such multi-echelon networks with dual sourcing have so far been missing. In a numerical analysis, we confirm that the use of AM in downstream echelons leads to significant cost reduction, as well as reducing inventory levels. A key insight we obtain is that AM reduces the dependency of upstream stock outs, opening the door for efficient heuristics to optimize base stock levels, like the method presented.

Stakeholders’ perceptions on the introduction of additive manufacturing (AM) in the maritime spare parts supply chain

Spare parts supply chain (SPSC) plays a crucial role in keeping ship machinery running. This study, rooted in a thorough literature review encompassing both academic and industry research, as well as established standards, seeks to shed light on stakeholder perspectives regarding the integration of Additive Manufacturing (AM) into the maritime spare parts supply chain (SPSC). The aim is to address fundamental questions critical to shaping the maritime spare parts supply chain with AM. The research employs a questionnaire-based methodology, deemed the most appropriate tool for capturing stakeholder perspectives, motivations, and concerns. Participants are categorized as “end-users” and “suppliers” of spare parts. The end-users in the maritime SPSC encompass individuals both on board vessels and in onshore offices, ranging from ship owners to engineers, surveyors, and staff in various departments. On the upstream side of the SPSC, the study engages with manufacturers and suppliers of spare parts, including original equipment manufacturers and third-party subcontractors, as well as various types of distributors and service providers. The research concludes with a comprehensive understanding of the maritime industry’s engagement with AM, balancing cost-efficiency with the preservation of service quality and operational readiness. It identifies significant barriers, such as quality assurance, the need for skills and knowledge, digital asset management, copyright protection, and equipment availability and costs. This study marks a pioneering exploration of stakeholder perspectives within the maritime SPSC concerning the integration of AM technology. It underscores the industry’s eagerness to embrace digital transformation while highlighting key challenges that must be addressed for a successful transition; this is clearly an added value from a practical perspective. As the maritime sector stands at the precipice of technological evolution, understanding these perspectives is integral to steering towards a more efficient, cost-effective, and sustainable future. Highlights The study delves into the perspectives of diverse stakeholders in the maritime Spare Parts Supply Chain, and how they approach Additive Manufacturing It reveals a comprehensive understanding of the industry’s commitment to cost-efficiency while maintaining service quality and operational readiness The research identifies significant barriers hindering the integration of Additive Manufacturing This study pioneers shedding light on the industry's enthusiasm for digital transformation and highlighting critical challenges that need addressing The findings provide practical insights for the maritime sector as it navigates technological evolution

Rethinking Additive Manufacturing for Spare Parts Supply Chain Management

Overview: Given the ongoing advancements in additive manufacturing (3D printing), many companies are considering using this digital technology for the supply of spare parts. This study identifies the advantages and barriers of additive manufacturing in the context of spare parts supply chain management. The case study examines the perspectives of a manufacturer and a customer of a spare parts supply chain. We present several questions that practitioners can use to (re)consider additive manufacturing for their companies.

A data-driven methodology for the periodic review of spare parts supply chain configurations

Configuring supply chains (SCs) is critical to spare parts retailers’ success, entailing two key aspects: stock deployment into distribution centres (DCs) (i.e. inventory centralisation or decentralisation) and stock supply in each DC (how many spare parts to supply and how often). Given the unpredictability of spare parts demand, stock deployment and supply policies should be regularly reviewed, adapting to fluctuations in customer needs. A viable way to do this is to adopt a multi-criteria ABC criticality classification. However, the multi-criteria ABC criticality classification has often been used to plan stock supply policies in a single DC, but only once to plan spare parts deployment. Nevertheless, the available literature methodology presents major limitations, being not applicable in real companies. Therefore, this paper provides a novel methodology, called SP-LACE, which first reviews the configuration of spare parts SCs based on a multi-criteria criticality classification. Then, allows, for the first time, to evaluate the economic benefits of the reviewed SC configuration. SP-LACE was tested on two case studies and compared with the literature methodology. The results indicate that it provides economic benefits (in terms of total SC cost), overcoming the limitations of the literature methodology and ensuring high service levels.

A planning model for repairable spare part supply chain considering stochastic demand and backorder: an empirical investigation

Today, improving machine availability is vital for industries to compete in the global market. Spare parts play an essential role in the maintenance and repair of equipment, but planning an extensive network in strategic industries with various spare parts can be very challenging due to the existence of different decision factors. The spare parts supply chain deals with inventory management issues, which necessitates considering the related decisions such as determining the stock level and order quantity. Moreover, demand uncertainty and long supply time make decision-making more complex. This paper presents a repair and supply planning model for repairable spare parts while considering a modified formulation of demand uncertainty to minimize costs. The model determines the optimal stock level, lateral transshipment, assignment of spare part orders to suppliers, equipment to repair centers, and the number of intervals over the planning horizon used in demand estimation. This research contributes to the literature by integrating recent decisions, using demand approximation by piecewise linearization, and considering backorder in warehouses evaluated by queuing models. A hybrid approach, including heuristic and genetic algorithms, is used to optimize the model using data from an oil company. The results show that using piecewise linearization and integrated repair and supply planning decisions optimizes costs and improves performance. Also, the availability is affected by the demand estimation, which necessitates precision prediction.

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.

Integrated demand forecasting and planning model for repairable spare part: an empirical investigation

Efficient resource management methods are essential for spare parts used in the maintenance and repair of equipment. Forecasting plays a critical role in planning, especially under demand uncertainty. Existing works regarding spare parts with intermittent demand focus on the mere forecasting model while integrating the planning and forecasting models are not sufficiently investigated. We examine the interaction between two models to optimise planning and forecasting decisions and prevent sub-optimality. This paper presents two mathematical models, including a planning model that determines stock level, spare part order assignment to suppliers, equipment repair assignment, and the number of intervals over the planning horizon. The second model is the forecasting model by Support Vector Machine (SVM). Considering uncertainty, demand estimation is performed by piecewise linearisation considering the optimal number of intervals in the planning model used in forecasting. An interactive procedure is developed to optimise models. We use an empirical investigation from an oil company providing the spare part supply chain data. The analyses show that demand estimation by piecewise method and integrating the decisions optimises the cost, improves the forecasting accuracy, and planning performance. Moreover, we offer several insights to practitioners that shed light on spare part planning and forecasting decisions.

Multi-Period Spare Parts Supply Chain Network Optimization under (T, s, S) Inventory Control Policy with Improved Dynamic Particle Swarm Optimization

Spare parts are the critical operation asset for ensuring a production line keeps going, which significantly improves the performance of manufacturing enterprises. This article pays attention to the joint optimization of spare part management and spare part supply chain network optimization in multiple supply periods. An extended (T, s, S) inventory control strategy is utilized to manage spare parts in customer nodes which can determine supply time, consumption and demand. In this spare part supply chain, the supply environment is different in different periods, so the mathematical model and solution method should be able to respond to and detect the environment change quickly. Hence, a dynamic nonlinear programming model is developed for optimizing inventory control decisions and spare part supply decisions so as to minimize the total cost. Furthermore, an improved self-adaptive dynamic migrating particle swarm optimization algorithm is proposed to solve the optimization problem. In this algorithm, a novel environment change detection and response strategy is applied to deal with the dynamic period in the spare part supply chain network. The results obtained show that the improved algorithm improves the computation time by eight percent and has better computational efficiency compared with the traditional algorithm.

Switchover to industrial additive manufacturing: dynamic decision-making for problematic spare parts

PurposeIntroducing additive manufacturing (AM) in a multinational corporation with a global spare parts operation requires tools for a dynamic supplier selection, considering both cost and delivery performance. In the switchover to AM from conventional manufacturing, the objective of this study is to find situations and ways to improve the spare parts service to end customers.Design/methodology/approachIn this explorative study, the authors develop a procedure – in collaboration with the spare parts operations managers of a case company – for dynamic operational decision-making for the selection of spare parts supply from multiple suppliers. The authors' design proposition is based on a field experiment for the procurement and delivery of 36 problematic spare parts.FindingsThe practice intervention verified the intended outcomes of increased cost and delivery performance, yielding improved customer service through a switchover to AM according to situational context. The successful operational integration of dynamic additive and static conventional supply was triggered by the generative mechanisms of highly interactive model-based supplier relationships and insignificant transaction costs.Originality/valueThe dynamic decision-making proposal extends the product-specific make-to-order practice to the general-purpose build-to-model that selects the mode of supply and supplier for individual spare parts at an operational level through model-based interactions with AM suppliers. The successful outcome of the experiment prompted the case company to begin the introduction of AM into the company's spare parts supply chain.

A BALANCED PERFORMANCE MEASUREMENT SYSTEM FOR AUTOMOTIVE SPARE PARTS SUPPLY CHAIN

This paper aims to provide a performance measurement system for the automotive spare parts supply chain. We focus on an independent distributor belonging to an independent channel. The framework encompasses different performance aspects for a high supply chain visibility, as well as the required categories, subcategories and key performance indicators for the automotive spare parts supply chain performance measurement. The framework is the first contribution that advances the performance measurement in the automotive aftermarket and allows assessing the performance of the automotive spare parts supply chain as a whole.

Improving agility and resilience of automotive spares supply chain: The additive manufacturing enabled truck model

The global supply chain disruption by the COVID-19 pandemic is difficult, if not impossible, to estimate as over 94% of the top 1000 fortune companies were badly affected. The need for building resilient supply chains to mitigate the effect of such disruptions is rising rapidly than ever before across the global business spectrum. Building resilience in the automotive spare parts (ASPs) supply chain is critically important as any disruption to automotive spares supply chain will affect the operations of the logistics sector, the backbone of global supply chains. This research work contributes to improving the resilience of the automotive spare parts supply chain by proposing a Viable Supply Chain (VSC) framework design that incorporates Additive Manufacturing (AM) enabled trucks in the automotive spares supply chain network. Based on the proposed model, conceptual case models are developed and tested with proposed AM enabled truck manufacturing closer to end customer. A heuristic approach called shortest time heuristic is also proposed to solve the routing and scheduling of an AM enabled truck to deliver customers’ orders of the spare parts through an online platform. Importantly, the study demonstrate how additive manufacturing can help the ASPs industry to switch from the existing practice of make-to-stock to a more efficient inventory management and cost saving make-to-order model while also achieving resilience and sustainability in by providing a source of spares support for discontinued models of vehicles.