Additive manufacturing service bureau selection: A Bayesian network integrated framework

Abstract

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.