Decision-support model to select spare parts suitable for additive manufacturing

Cardeal, Gonçalo; Leite, Marco; Ribeiro, Inês

doi:10.1016/j.compind.2022.103798

Cited by 13 publications

(7 citation statements)

References 55 publications

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“…, 2022), bringing the possibility of value creation opportunities between companies through interorganizational cooperation (Luomaranta and Martinsuo, 2022). AM can be used to reduce delivery lead time, optimize inventories and shorten supply chains (SC), as the products/parts can be produced locally, on-demand (Cardeal et al. , 2023; Elhazmiri et al.…”

Section: Literature Reviewmentioning

confidence: 99%

“…AM has the potential of changing the way companies operate and interact with customers, opening new business models, while optimizing production systems (Elhazmiri et al, 2022), bringing the possibility of value creation opportunities between companies through interorganizational cooperation (Luomaranta and Martinsuo, 2022). AM can be used to reduce delivery lead time, optimize inventories and shorten supply chains (SC), as the products/parts can be produced locally, on-demand (Cardeal et al, 2023;Elhazmiri et al, 2022;Fontana et al, 2019), although some barriers to the implementation of AM may bring vulnerabilities to SC, such as the availability of special raw materials, supplier capacity, among others (Naghshineh and Carvalho, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

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The “V” model for decision analysis of additive manufacturing implementation

Lima

Sátyro

Contador

et al. 2023

JMTM

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PurposeThis study aims to broaden the understanding of the additive manufacturing (AM) body of knowledge, presenting a model better suited to the current level of technological development that supports the decision to implement AM in industries, based on the experience of companies in the industry of orthopedic medical implants.Design/methodology/approachBased on the design-science research, the model for the decision to adopt the AM was designed and submitted to experts from the industry of orthopedic implants in Brazil for refinement. For the empirical test of the final model, interviews were used in a company that was considering implementing AM and in another that was not, to evaluate the model.FindingsThe model considers seven dimensions for decision analysis of AM implementation: legal constraints, financial, technological, operational, organizational, supply chain and external factors, being subdivided into 42 criteria that play a relevant role in the implementation decision. The analysis factor of each dimension and criteria are also presented.Originality/valueThe model seeks to be as complete as possible and can be used by various industrial productive sectors, incorporating the analysis of the requirements of health regulatory agencies, suitable for the analysis of the decision to implement AM for the manufacturing of medical implants, not found in other models.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

The “V” model for decision analysis of additive manufacturing implementation

Lima

Sátyro

Contador

et al. 2023

JMTM

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show abstract

“…In order to perform the systematic review present in this article, eight publications were selected, namely: [13,16,17,18,19,20,21,22].…”

Section: Academic Google Capes Theses and Dissertations Catalogmentioning

confidence: 99%

A Systematic Review About Classification Criteria, Applied Methods and Methodologies for Selecting Spare Parts Suitable for Additive Manufacturing

França,

Beal

2023

Blucher Engineering Proceedings

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Additive manufacturing (AM), also known as 3D printing, refers to a family of manufacturing technologies capable of producing products by progressively adding layers of materials. AM emerged in 1981 and is in a continuous process of technological and commercial improvement, demonstrating its relevance for the technological frontier. This fact is evidenced by the growing number of scientific publications aimed at investigating and describing the benefits and opportunities arising from 3D printing for the industrial scenario. A latent opportunity, for example, is the optimization of spare parts management through AM technologies. The aim of this paper is to present a systematic review of classification criteria, methods and methodologies for selecting suitable spare parts for MA.

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“…Implementing AM can also result in reduced manufacturing costs, lower shipping costs, and overall lower operating costs since the distributed fabrication of spare parts uses smaller, more automated equipment and can facilitate mass customization. [23] reveal that there is interest in adopting additive manufacturing in spare parts supply chains in order to shorten supply chains, improve response times, and optimize inventory. In the automotive industry, research on the additive manufacturing production of polymer [24] and metal [25] or composite components [17] has continued to widen.…”

Section: Introductionmentioning

confidence: 99%

Additive manufacturing process selection for automotive industry using Pythagorean fuzzy CRITIC EDAS

et al. 2023

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For many different types of businesses, additive manufacturing has great potential for new product and process development in many different types of businesses including automotive industry. On the other hand, there are a variety of additive manufacturing alternatives available today, each with its own unique characteristics, and selecting the most suitable one has become a necessity for relevant bodies. The evaluation of additive manufacturing alternatives can be viewed as an uncertain multi-criteria decision-making (MCDM) problem due to the potential number of criteria and candidates as well as the inherent subjectivity of various decision-experts engaging in the process. Pythagorean fuzzy sets are an extension of intuitionistic fuzzy sets that are effective in handling ambiguity and uncertainty in decision-making. This study offers an integrated fuzzy MCDM approach based on Pythagorean fuzzy sets for assessing additive manufacturing alternatives for the automotive industry. Objective significance levels of criteria are determined using the Criteria Importance Through Inter-criteria Correlation (CRITIC) technique, and additive manufacturing alternatives are prioritized using the Evaluation based on Distance from Average Solution (EDAS) method. A sensitivity analysis is performed to examine the variations against varying criterion and decision-maker weights. Moreover, a comparative analysis is conducted to validate the acquired findings.

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Decision-support model to select spare parts suitable for additive manufacturing

Cited by 13 publications

References 55 publications

The “V” model for decision analysis of additive manufacturing implementation

The “V” model for decision analysis of additive manufacturing implementation

A Systematic Review About Classification Criteria, Applied Methods and Methodologies for Selecting Spare Parts Suitable for Additive Manufacturing

Additive manufacturing process selection for automotive industry using Pythagorean fuzzy CRITIC EDAS

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