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Additive Manufacturing (AM) is a recent and rapidly developing technology, which has brought about significant disruptions in manufacturing arena. To capture the full spectrum of changes resulting from AM’s introduction into industry, it is not sufficient to look at it just as a disruptive set of technologies but, rather, take a system’s approach....
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Since it affects the performance of whole supply chain significantly, definition of correct inventory control policy in a supply chain is critical. Recent technological development enabled real time visibility of a supply network by horizontal integration of each node in a supply network. By this opportunity, inventory sharing among stocking locati...
Citations
... Handal [49] highlighted the fact that top managers have difficulties making decisions that involve implementing new technologies. The reasons for this hesitation might include a cost-benefit trade-off and the need to investigate the technology at a practical level [81]. AM companies from Africa are making great efforts to overcome challenges, but their poor AM material supply chains cannot be ignored [24]. ...
With the development of new construction technology, increasing attention is being paid to 3D printing due to its construction efficiency as well as its sustainability. Numerous researchers have determined its benefits in cost reduction, resource savings, safety assurance, etc. Although various advantages have been identified, there are limitations and challenges in technology implementation. Especially since it is a new construction method, 3D printing construction projects will have a very different supply chain compared to traditional projects. As part of a research programme investigating the 3D printing construction supply chain in a New Zealand context, this study systematically analysed the research about 3D printing adoption and supply chain challenges in the construction sector. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was adopted as the guideline for literature selection. PRISMA is designed to assist researchers in reporting the review research focus and methodology, and examining the findings from published literature. NVivo was then adopted to code and analyse the selected publications to gather the data necessary for our study. The literature was analysed from the perspectives of the research focus, research methods, and findings. Studies about 3D printing implementation, benefits and barriers, as well as its significance are also analysed. As a result, this research found existing research gaps, including the fragmented situation of management-related research in the 3D printing construction sector, insufficient research in top management for 3D printing construction implementation, and changes to supply chain management practices in 3D printing construction projects. A decision support system demo for supply chain management is drafted in this paper, which requires further study. The research outcome highlighted the existing studies in 3D printing construction implementation and supply chain, and initiated a research topic on supply chain decision making. The result contributes to the theoretical and practical development of 3D printing technology in the construction industry. This review paper also inspires future studies on supply chain frameworks and theoretical models.
... Milad et al. [46] developed a cost model and evaluated the economic feasibility of AM for slow-moving vehicle component SCs by taking the costs of the TM spare part SCs with centralized and outsourced AM spare part SCs. They analyzed and compared the SC costs of 14 different spare parts as well as the overall spare part SC cost that was produced by both TM and AM. ...
... This can be attributed to the fabrication of complex components with the AM process, which needs only raw materials and 3D CAD files. As a result, the number of assemblies was minimized; it cut down the setup and changeover time and enhanced SC just-in-time production [46]. ...
... It also maximizes customization and reduces the costs related to inventory holding, stock-outs, warehousing, packaging, and logistics. We also observed that the research by Mohsen [42] and Milad et al. [46] evaluated the economic feasibility of AM in terms of time-and cost-related factors, respectively. Confirming our findings, their study results illustrated the benefits of implementing AM in reducing timeand cost-related factors in SC systems. ...
Background: The objective of this literature review is to systematically explore the supply chain (SC)-related issues of additive manufacturing (AM)-based production processes. For SC sustainability, efficiency, and performance improvements, the adoptation of disruptive technologies like AM plays a vital role, because the product's SC benefits in terms of reduced total lead time and costs. Methods: To explore the state-of-the-art influences of AM on the SC in this study, 978 papers published in peer-reviewed journals from 2004 to 2023 were retrieved, and 70 of these were identified as the most relevant and then reviewed. Results: As an outcome, the results of this review paper indicated a lack of documented studies in developing countries and, as a result, limited research works, for instance, in fashion industries were observed. In addition, AM best practices in the SC context have been identified and categorized as cost-related, time-related, inventor-related, as well as energy-, waste-, and environment-related factors, SC efficiency factors, and flexibility, marketing, and manufacturing-related factors. Conclusions: By identifying these categories, the study aims to contribute to the efforts of transforming traditional manufacturing into AM-based processes, for which a framework for the AM SC implementation is developed. In summary, the systematic review indicated that further research work is needed on the impacts of the identified AM best practices on SC performance.
... For example [42], shows how to utilize AM technology within the structure of spare parts supply chain while [43] investigated resilience improvement of AM capabilities on the navy spare parts supply chain. Other studies have investigated the impact of AM technologies in various sectors such as aircraft [44][45][46][47], automotive [48][49][50] and defense [51][52][53] industry sectors. Table 2 shows some recent literature on the implementation of AM in supply chain. ...
... Indeed, by adopting a distributed manufacturing approach, an original equipment manufacturer (OEM) can eliminate regional distribution centers by printing and distributing products locally. Thanks to this and to the digitisation of linkages (Holmstr€ om et al., 2016), SC complexity reduces (Ashour Pour et al., 2019) and SC performance improves (e.g. reduction in inventory, lowered transportation costs, environmental impact) (Muir and Haddud, 2018). ...
Purpose
This paper investigates how the adoption of additive manufacturing (AM) impacts upstream supply chain (SC) design and considers the influence of drivers and barriers towards the adoption.
Design/methodology/approach
Ten case studies investigating AM adoption by Original Equipment Manufacturers (OEMs) in five industries were conducted. This research is driven by a literature-based framework, and the results are discussed according to the theory of transaction cost economics (TCE).
Findings
The case studies reveal four patterns of AM adoption that affect upstream SC design (due to changes in supply base or types of buyer–supplier relationships): make, buy, make and buy and vertical integration. A make or buy decision is based on the level of experience with the technology, on the AM application (rapid manufacturing, prototyping or tooling) and on the need of control over production. Other barriers playing a role in the decision are the high initial investments and the lack of skills and knowledge.
Originality/value
This paper shows how different decisions regarding AM adoption result in different SC designs, with a specific focus on the upstream SC and changes in the supply base. This research is among the first to provide empirical evidence on the impact of AM adoption on upstream SCs and to identify drivers of the make or buy decision when adopting AM through the theoretical lens of TCE.
... Several studies have discussed ways to configure a spare parts supply chain when adopting AM 2022)). The second stream uses quantitative study (Ashour Pour et al. (2017). Thus a few quantitative papers addressed the integration of additive manufacturing in the supply chain. ...
Spare parts inventory management represents a challenge for aircraft companies. Determining the optimal allocation and consumption of spare parts is problematic due to the intermittent demand. Original equipment manufacturer (OEM) uses different models to evaluate inventory stock level to avoid the non-availability of the desired spare parts when required. With the recent implementation of additive manufacturing (AM) in many sectors, the implications of AM for spare parts inventory management and control models need more attention. This paper aims to evaluate the advantage of AM integration for spare parts optimization in a multi-echelon inventory system. It compares three scenarios for non-moving, slow-moving, and fast-moving spare parts. A scenario-based modeling approach is followed to draw out insights for managers. The first scenario considers the conventional case where there is no integration of AM. The second scenario considers AM integration only in the central maintenance center (CMC). The third scenario assumes AM integration in CMC and regional maintenance centers (RMC). This analysis showed that when AM repair time is inferior to conventional process (CP) repair time, the best scenario for AM manufacturing integration is a decentralized AM location. And when AM repair time equals CP repair time, and AM repair probability is superior to 70%, the decentralized scenario still the optimal integration solution. However, when the AM repair time equals CP repair time, and the AM repair probability is inferior to 70%, the centralized scenario is the optimal integration solution. Moreover, non-moving and slow-moving spare parts are the most suitable categories for optimal AM allocation. Finally, the paper offers guidelines on adopting AM in the aircraft supply chain and the impact on spare part inventory management.
... x AM requires no tooling; therefore, manufacturing is enabled frictionless by solely using CAD data minimising setup and changeover times (Pour et al., 2017) and thus provides the possibility for lead time reduction compared to TM (Westerweel et al., 2020;Meyer et al., 2020a). ...
The vulnerability of supply chains is more evident during crises. Recently, the COVID-19 pandemic, container ship blockades, and marine traffic jams in the Suez Canal have caused severe supply disruptions. Additive manufacturing (AM), often referred to as 3D printing, at demand sites, can solve supply disruptions. This study investigates how AM in different configurations affects supply availability. To this end, we simulate a healthcare supply chain. The simulation considers daily business demand and operational and disruptive supply risks. We measure demand, orders, stock levels, and availability indicators and compare them across supply configurations. The simulation allows identifying the most effective resilience configuration related to the cost-per-availability ratio. Overall, simulations support using AM as a risk-mitigation strategy.
... Collaborative robotics is more adopted in production-related processes, which can improve safety for workers by reducing the risk of injuries, and it usually is helpful in complex task operations (Benotsmane et al., 2019). AM is more related to product-related processes, with the frequent investigation of rapid prototyping and customised product development (Ashour Pour et al., 2019). Indeed, the integrated adoption of enabling technologies could benefit manufacturing companies, such as cost reduction, quality improvement, time reduction and flexibility improvement (Kiel et al., 2017;Kusiak, 2018). ...
Purpose
This paper has two objectives: first, to investigate the state-of-the-art of Industry 4.0 (I4.0) adoption in Italian manufacturing firms and, second, to understand variations in technologies implemented and business functions involved, benefits perceived, and obstacles encountered in I4.0 implementation over a three-year period.
Design/methodology/approach
The approach adopted in this research is descriptive, nesting longitudinal features. The paper presents a descriptive survey of 102 Italian manufacturing companies. The authors also evaluated non-response biases. The longitudinal approach was achieved by comparing the responses of the 40 sub-samples in common with a second similar survey launched three years prior, which aimed to identify patterns of evolution in the adoption of the I4.0 paradigm.
Findings
Survey findings demonstrate that Italian manufacturing companies still have limited awareness of I4.0 technologies, and the adoption of I4.0 technologies differs per technology. Company size and information system coverage level are the two factors that impact the company's technology adoption level. The comparative study shows that knowledge and adoption increase in a three-year interval with an unbalanced involvement of business functions regarding the I4.0 transformation. Indeed, companies are still seeking I4.0 solutions to reduce costs and lead times primarily, and the benefits perceived by companies are shown to be related to the number of I4.0 technologies in use. Finally, when companies put the I4.0 technologies into practice, competence is constantly considered the most significant barrier.
Research limitations/implications
This paper aims at conducting a thorough investigation into the development of I4.0 adoption in manufacturing companies. The main limitation of this study concerns the limited number of subjects involved in the longitudinal study (40) and the focus on a limited geographical area (Italy). In addition, more I4.0 technologies could also be incorporated into the survey protocol to gain further insight into I4.0 development.
Originality/value
The authors provide one of the first attempts to assess the variations of I4.0 implementation concerning technology adoption, business function involvement, and the alteration of benefits and obstacles. Several studies presented in the literature highlight the lack of longitudinal studies investigating the development of the I4.0 paradigm in a specific manufacturing context: this paper is the attempt at filling this gap.
... The current literature on AM is mainly focused on the sustainability features of 3D printing machines (Niaki and Nonino 2017a, b;Sharma and Dixit 2019), their production costs and technical aspects Yang and Li 2018). According to Ashour Pour et al. (2017) less than 10% of the AM literature has investigated the effects of AM on the supply chain costs and performance. Social sustainability aspect of AM has remained underdeveloped and limited knowledge regarding that has led to a considerable gap in literature Naghshineh et al. 2020) because it has complicated nature and is difficult to be quantified ). ...
... Despite the popularity of RP, the diffusion and utilization of this technology have been slower than its evolution and adoption (Ashour Pour et al. 2017;Zheng et al. 2019;Tavassoli et al. 2020). Some studies have indicated the main barriers to AM adoption are the shortage of trained workforce to utilize RP and inadequate knowledge regarding the possible effects of AM systems on supply chains (Thomas-Seale et al. 2018;Ituarte et al. 2019;Alabi et al. 2019;Yang et al. 2020). ...
The present study aimed to assess the effect of implementing Rapid Prototyping (RP) in the product development phase on the sustainability of a conventional supply chain. The sustainability indicators of RP utilization were identified through a critical literature review and consulting two experienced RP practitioners to determine the key variables regarding the potential impact of RP on the supply chain components, with an emphasis on sustainability pillars. A generic system dynamics modeling was provided to simulate the RP-adapted supply chain and measure its sustainability performance. The simulation results indicated that RP utilization in the design phase could decrease the number of the assembly parts and material consumption in conventional manufacturing, while indirectly affecting the reduction of waste generation, logistics, CO2 emissions, processes, and the total costs which are related to environmental and economic aspects of the sustainable supply chain. Findings indicated that significant increase in operational skills and knowledge as the main indicators of the social dimension could remarkably reduce the failure rates and increase the quality of the products. This indicator plays a pivotal role in operational success and could be enhanced through training programs. Social sustainability indirectly affects environmental and economic sustainability. This was the first model-based research to examine the potential effects of RP on the sustainability of a conventional manufacturing. The proposed generic model encompassed the variables that could be applicable in every scenario to help decision-makers change values or add more variables within specific industry settings and choose the applicable ones, which in turn, accelerating the RP adoption in supply chains and providing insights for operational decisions regarding product design stage.
... Estimating and modeling AM costs is difficult and complex; many researchers study the production of a few individual parts and of the studies that examine multi-part assemblies, few consider supply chain impacts such as transportation and inventory costsexceptions include Pour et al. (2019), Thomas (2016), Holmstrom et al. (2010) and/or financial benefits from decreased risks of disruptions (Thomas, 2016;Thomas and Gilbert, 2014). Many of these studies focus on comparing AM costs with traditional manufacturing (TM) (Thomas, 2016;Atzeni and Salmi, 2012;Holmstrom et al., 2010;Ruffo et al., 2006a;Allen, 2006;Hopkinson and Dickens, 2003). ...
Purpose - 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/Approach - This 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. Findings - This 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/implications - This 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/value - This research is the first study to link realistic spare part demand characterization to AM supply chain design using quantitative modeling.
... Estimating and modeling AM costs is difficult and complex; many researchers study the production of a few individual parts and of the studies that examine multi-part assemblies, few consider supply chain impacts such as transportation and inventory costsexceptions include Pour et al. (2019), Thomas (2016), Holmstrom et al. (2010) and/or financial benefits from decreased risks of disruptions (Thomas, 2016;Thomas and Gilbert, 2014). Many of these studies focus on comparing AM costs with traditional manufacturing (TM) (Thomas, 2016;Atzeni and Salmi, 2012;Holmstrom et al., 2010;Ruffo et al., 2006a;Allen, 2006;Hopkinson and Dickens, 2003). ...
Purpose
The 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/approach
This 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.
Findings
This 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/implications
This 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/value
This research is the first study to link realistic spare part demand characterization to AM supply chain design using quantitative modeling.
