{"title":"快速成型制造服务局的选择:贝叶斯网络综合框架","authors":"","doi":"10.1016/j.ijpe.2024.109348","DOIUrl":null,"url":null,"abstract":"<div><p>Additive manufacturing service bureaus (AMSBs) are crucial for enabling manufacturing organizations to leverage the benefits of additive manufacturing (AM) technology, such as on-demand manufacturing, production speed, etc., all while eliminating the expense of maintaining inventories. Consequently, many organizations favor AMSBs for expertise, cost efficiency, and access to diverse equipment, materials, and post-processing, reducing the necessity for substantial in-house investments. While researchers have explored evolving business models and the types of AM services offered by AMSBs to some extent, there is a noticeable research gap in selecting the most compatible AMSB for specific customer requirements, which this research would like to address. Initially, this research identifies various types of services offered by AMSBs, classifying them into eight groups: generative, evaluative, explorative, facilitative, constructive, decisive, selective, and assistive. Then, a knowledge-based expert system is introduced to select a suitable type of AM service. Further, 101 AMSB selection criteria are identified and grouped into criteria and sub-criteria by incorporating insights from literature and experts. Then, 26 pertinent criteria were shortlisted through Delphi. Neutrosophic best-worst method is then utilized to quantify criteria weights. Finally, a Bayesian network is used to calculate the selection probability of each AMSB, identifying the AMSB with the highest probability as the most compatible. The robustness of this framework is validated through sensitivity analysis. The practical effectiveness of the framework was demonstrated through a case study involving Ferro Oil-Tech India Private Limited. 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引用次数: 0
摘要
增材制造服务局(AMSB)对于制造企业充分利用增材制造(AM)技术的优势(如按需制造、生产速度等)至关重要,同时还能省去维持库存的费用。因此,许多企业青睐 AMSB,因为它们能提供专业技术、成本效益,并能获得各种设备、材料和后处理,从而减少大量内部投资的必要性。虽然研究人员在一定程度上探索了不断发展的商业模式和 AMSB 所提供的 AM 服务类型,但在选择最符合特定客户要求的 AMSB 方面还存在明显的研究空白,本研究希望解决这一问题。首先,本研究确定了 AMSB 提供的各种服务类型,并将其分为八类:生成性、评估性、探索性、促进性、建设性、决定性、选择性和辅助性。然后,引入基于知识的专家系统来选择合适的 AM 服务类型。此外,通过吸收文献和专家的见解,确定了 101 个 AMSB 选择标准,并将其归类为标准和次级标准。然后,通过德尔菲法筛选出 26 个相关标准。然后,利用中性最优-最差法量化标准权重。最后,利用贝叶斯网络计算每个 AMSB 的选择概率,将概率最高的 AMSB 确定为最匹配的 AMSB。通过敏感性分析,验证了该框架的稳健性。通过涉及 Ferro Oil-Tech India Private Limited 的案例研究,证明了该框架的实际有效性。对结果的分析提供了宝贵的管理见解,并提出了提高组织业务竞争力的方法。
Additive manufacturing service bureau selection: A Bayesian network integrated framework
Additive manufacturing service bureaus (AMSBs) are crucial for enabling manufacturing organizations to leverage the benefits of additive manufacturing (AM) technology, such as on-demand manufacturing, production speed, etc., all while eliminating the expense of maintaining inventories. Consequently, many organizations favor AMSBs for expertise, cost efficiency, and access to diverse equipment, materials, and post-processing, reducing the necessity for substantial in-house investments. While researchers have explored evolving business models and the types of AM services offered by AMSBs to some extent, there is a noticeable research gap in selecting the most compatible AMSB for specific customer requirements, which this research would like to address. Initially, this research identifies various types of services offered by AMSBs, classifying them into eight groups: generative, evaluative, explorative, facilitative, constructive, decisive, selective, and assistive. Then, a knowledge-based expert system is introduced to select a suitable type of AM service. Further, 101 AMSB selection criteria are identified and grouped into criteria and sub-criteria by incorporating insights from literature and experts. Then, 26 pertinent criteria were shortlisted through Delphi. Neutrosophic best-worst method is then utilized to quantify criteria weights. Finally, a Bayesian network is used to calculate the selection probability of each AMSB, identifying the AMSB with the highest probability as the most compatible. The robustness of this framework is validated through sensitivity analysis. The practical effectiveness of the framework was demonstrated through a case study involving Ferro Oil-Tech India Private Limited. The analysis of the results provided valuable managerial insights and suggested ways to enhance the business competitiveness of the organization.
期刊介绍:
The International Journal of Production Economics focuses on the interface between engineering and management. It covers all aspects of manufacturing and process industries, as well as production in general. The journal is interdisciplinary, considering activities throughout the product life cycle and material flow cycle. It aims to disseminate knowledge for improving industrial practice and strengthening the theoretical base for decision making. The journal serves as a forum for exchanging ideas and presenting new developments in theory and application, combining academic standards with practical value for industrial applications.