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

Ahlsell, Levin; Jalal, Didar; Khajavi, Siavash H.; Jönsson, Patrik; Holmström, Jan

doi:10.3390/jmmp7010008

Cited by 3 publications

(1 citation statement)

References 45 publications

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“…The outcome of their studies illustrated the benefits of AM in improving SC efficiency in terms of lead time and overall costs. Levin et al [38] modeled both the additive and TM systems to compare their SC efficiency. The results of their study illustrated that AM provides a good improvement in a SC's performance.…”

Section: Time Cost Inventory and Operations Efficiency-related Factorsmentioning

confidence: 99%

Impacts of Adopting Additive Manufacturing Process on Supply Chain: Systematic Literature Review

Woldesilassiea,

Lemu,

Gutema

2024

Logistics

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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.

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Section: Time Cost Inventory and Operations Efficiency-related Factorsmentioning

confidence: 99%

Impacts of Adopting Additive Manufacturing Process on Supply Chain: Systematic Literature Review

Woldesilassiea,

Lemu,

Gutema

2024

Logistics

View full text Add to dashboard Cite

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Design and Manufacturing of Polymer Composite Materials Using Quality Management Methods

Berladir,

Mitalova,

Pavlenko

et al. 2023

JES

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Many factors influence the design and manufacturing of products from polymer composite materials. The expert assessment method was applied in the article for the corresponding analysis. A cause-and-effect diagram was built as a result of a preliminary analysis of the influence of factors on the primary indicator of product quality indicators (e.g., wear resistance). Based on the expert assessment results and quality function deployment analysis, the most critical factors affecting wear resistance were obtained: polymer brand, filler shape and size, technological parameters of mixing, pressing, sintering, and mechanical processing. Their impact was studied to establish quantitative dependencies. A stable value of the wear resistance of the product in the manufacturing process can be ensured by timely adjustment of the mixing, pressing, and sintering modes. As a result of the structural analysis of the process of developing materials with predetermined properties at the enterprise according to the IDEF0 methodology, the importance of assessing the risks associated with the process of multi-criteria optimization of their main quality indicators was confirmed.

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Cybercrime and Cybersecurity Challenges in the Automotive Industry Utilizing Agent-Based Modeling (ABM)

Lewis,

Burrell,

Nobles

et al. 2023

Transformational Interventions for Business, Technology, and Healthcare

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Agent-based modeling (ABM) serves as a powerful and innovative tool to evaluate and address cybersecurity risks in the auto industry. With the increasing integration of advanced technologies in modern vehicles, the potential vulnerabilities and threats to cybersecurity have become a major concern. ABM allows cybersecurity experts to simulate complex interactions among autonomous agents representing various components of the automotive ecosystem, such as vehicles, manufacturers, suppliers, and hackers. ABM can effectively model real-world scenarios and assess how cyber threats propagate and spread throughout the system. It enables the identification of critical points of weakness and potential attack vectors, aiding in the development of proactive strategies to mitigate cybersecurity risks. Through iterative simulations and data-driven analysis, ABM empowers stakeholders in the auto industry to make informed decisions and implement robust security measures that safeguard against cyber-attacks, ensuring the safety and trustworthiness of connected and autonomous vehicles.

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

Cited by 3 publications

References 45 publications

Impacts of Adopting Additive Manufacturing Process on Supply Chain: Systematic Literature Review

Impacts of Adopting Additive Manufacturing Process on Supply Chain: Systematic Literature Review

Design and Manufacturing of Polymer Composite Materials Using Quality Management Methods

Cybercrime and Cybersecurity Challenges in the Automotive Industry Utilizing Agent-Based Modeling (ABM)

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