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Introducing additive manufacturing (AM) in a multinational corporation with a global spare parts operation requires tools for a dynamic supplier selection, considering both cost and delivery performance. In the switchover to AM from conventional manufacturing, the objective of this study is to find situations and ways to improve the spare parts service to end customers.

In this explorative study, the authors develop a procedure – in collaboration with the spare parts operations managers of a case company – for dynamic operational decision-making for the selection of spare parts supply from multiple suppliers. The authors' design proposition is based on a field experiment for the procurement and delivery of 36 problematic spare parts.

The practice intervention verified the intended outcomes of increased cost and delivery performance, yielding improved customer service through a switchover to AM according to situational context. The successful operational integration of dynamic additive and static conventional supply was triggered by the generative mechanisms of highly interactive model-based supplier relationships and insignificant transaction costs.

The dynamic decision-making proposal extends the product-specific make-to-order practice to the general-purpose build-to-model that selects the mode of supply and supplier for individual spare parts at an operational level through model-based interactions with AM suppliers. The successful outcome of the experiment prompted the case company to begin the introduction of AM into the company's spare parts supply chain.

Innovative startups have begun a trend using laser sintering (LS) technology patents expiration, namely, by introducing LS additive manufacturing (AM) machines that can overcome utilization barriers, such as the costliness of machines and productivity limitation. The recent rise of this trend has led the authors to investigate this new class of machines in novel settings, including hub configuration. There are various supply chain configurations to supply spare parts in industrial operations. This paper aims to explore the promise of a production configuration that combines the benefits of centralized production with the flexibility of local manufacturing without the huge costs related to it.

This study quantitatively examines the feasibility of different AM-enabled spare parts supply chain configurations. Using cost data extracted from a case study, three scenarios per AM machine technology are modeled and compared.

Results suggest that hub production configuration depending on the utilized AM machines can provide economic efficiency and effectiveness to reduce equipment downtime. While previous studies have suggested the need for AM machines with efficiency for single part production for a distributed supply chain, the findings in this research illustrate the positive relationship between multi-part production capability and the feasibility of a hub manufacturing configuration establishment.

This study explores the promise of a production configuration that combines the benefits of centralized production with the flexibility of local manufacturing without the huge costs related to it. Although the existing body of knowledge contains research on production decentralization, research on various levels of decentralization is lacking. Using a real-world case study, this study aims to compare the feasibility of different levels of decentralization for AM-enabled spare parts supply chains.

The purpose of this research is to highlight the significance of advancing research methodologies in logistics, operations and supply chain management. It seeks to expand the scope of research questions and explore areas previously constrained by traditional methodological approaches, thereby enhancing the exploration of complex, real-world business issues.

This commentary introduces and discusses the special issue on “Advances in Research Methodologies for Logistics and Supply Chain Management,” exploring methodological innovations, diversity and their potential to address complex business and disciplinary challenges. The commentary assesses a broad spectrum of methodologies, ranging from traditional qualitative and quantitative approaches to overlooked methods such as qualitative comparative analysis, netnography, design science, Bayesian networks, machine learning and repertory grid technique. This diverse methodological approach enables a comprehensive examination of emerging and ongoing challenges in the supply chain. In the final summary section, we highlight additional areas of research method innovation not covered in this special issue, offering a broader perspective on future directions for methodological advancements in SCM research.

The findings suggest that integrating less explored methodologies from various disciplines encourages a richer, multi-level analysis of the supply chain management landscape. This integration facilitates a deeper understanding of emerging challenges, such as geopolitical issues, global supply chain disruptions and the integration of new technologies. Additionally, the exploration of ‘white space' in research methodologies indicates significant potential for discovering new insights that bridge practical problems with theoretical contributions.

The value of this methodological diversity extends beyond academic enrichment. It catalyzes the generation of innovative insights crucial for business practitioners, policymakers, consultants and academics. By adopting varied research designs and methodologies, the research note can offer a broader spectrum of analytical perspectives, crucial for uncovering nuanced insights into complex, cross-cultural and relationship-based dynamics in supply chain research.

In retail, product fitting is a critical operational practice. For many products, the operational outcome of the retail supply chain is determined by the customer physically fitting products. Digital product fitting is an emerging operational practice in retail that uses digital models of products and customers to match product supply to customer requirements. This paper aims to explore potential supply chain outcomes of digitalizing the operational practice of product fitting. The purpose is to explore and propose the potential of the practice to improve responsiveness to customer requirements and the utilization of existing variety in mass-produced products.

A maturity model of product fitting is developed to specify three levels of digitalization and potential outcomes for each level. Potential outcomes are developed based on empirical data from a case survey of three technology-developing companies, 13 retail cases and a review of academic literature.

With increasing maturity of digital product fitting, the practice can be used for more purposes. Besides matching product supply to customer demand, the practice can improve material flows, customer relationship management, assortment planning and product development. The practice of digital product fitting is most relevant for products where the final product configuration is difficult to make to order, product and customer attributes are easily measurable and tacit knowledge of customers and products can be formalized using digital modeling.

Potential outcomes are conceptualized and proposed. Further research is needed to observe actual outcomes and understand the mechanisms for both proposed and surprising outcomes in specific contexts.

The maturity model helps companies assess how their operations can benefit from digital product fitting and the efforts required to achieve beneficial outcomes.

This paper is a first attempt to describe the potential outcomes of introducing digital product fitting in retail supply chains.

This paper investigate how fit uncertainty impacts product return costs in online retailing and how digital product fitting, a pre-sales fitting practice, can reduce fit uncertainty.

The paper analyzes the current performance of a retailer's e-commerce and return operations by estimating costs generated by product returns, including product handling costs, tied-up capital, inventory holding costs, transportation costs, and order-picking costs. The estimated costs were built on 2,229 return transactions from a Scandinavian fashion footwear retailer. A digital product fitting technology was tested with the retailer’s products and resulted in estimations on how such technology could affect product returns.

The cost of a return is approximately 17% of the prime cost. The major cost elements are product handling costs and transportation costs, which together amount to 72% of the total costs. If well calibrated, the fitting technology can cut fit-related return costs by up to 80%. The findings show how customers reacted to the fitting technology: it was unable to verify fit every time, but it serves as a useful and effective support tool for customers when placing orders.

Virtual fit verification using digital product fitting is key to retailers to reduce fit-related returns. Digital product fitting using three-dimensional scanning is more appropriate for some products, but it is unsuitable for products that are difficult to measure and scan.

The paper contributes an empirical estimate of retail supply chain costs associated with fit uncertainty, as well as theoretical understanding of the role of pre-sales fit verification in avoiding product returns.

The purpose of this paper is to study the adoption and motivation to adopt global spare parts practices in autonomous units servicing the products of an original equipment manufacturer.

The methodological approach is case study investigating the reasons for different levels of use and the perceptions regarding the benefits of a centralized supply chain management in four representative service units.

Autonomous spare part units often source locally because local suppliers are easy to work with in terms of purchasing processes and have no requirements for systematic planning and control of spare parts purchases and inventory management. However, increasing the share of centrally sourced and managed spare parts in the supply chain brings advantages in terms of lower total cost and higher availability. From the perspective of individual subunits engaged in providing product support services, this advantage of relying on a centrally managed spare parts supply chain of an original equipment manufacturer is not self-evident. Autonomous units frequently choose to continue sourcing spare parts from alternative sources, undermining the economies of scale attainable through the original equipment manufacturer’s supply chain. Higher levels of use are facilitated by back-office purchasing management at the unit level. The positive perceptions of centralized supply management in general – including the relationship between the supply unit and the service unit – further facilitate adoption, while local requirements and practices inhibit it.

The study is a single case study and presents proposals requiring further study of the reasons for the observed differences in use of centralized supply chain management.

Centralized spare parts management service requires investment in back-office resources at the service unit level.

The research increases the practical relevance of existing research through an empirical investigation on the autonomous units’ motivations for and perceived benefits of centralized spare parts supply.

This works provides a thorough understanding of the challenges and opportunities associated with Additive Manufacturing (AM) adoption in the medical sector. Through this analysis, we aim to better understand when to adopt AM, how to do so, and how such adoption might change in the future.

This research first conducted a systematic literature review (SLR) to identify AM challenges and opportunities in the medical sector, which were then validated through a Delphi study. The 18 Delphi study participants were also asked to suggest countermeasures for the challenges and help identify future AM adoption scenarios. Finally, these findings were analyzed according to the ecosystem pie model to design an ecosystem model for AM in the medical sector.

Among the 13 challenges and 13 opportunities identified, the lack of a skilled workforce and the responsiveness achievable via AM were by far the most relevant challenge and opportunity. Moreover, the participants identified countermeasures for 10 challenges, as well as three future AM adoption scenarios. Finally, leveraging these findings, an ecosystem model was developed.

This work contributes to the limited understanding of the AM challenges and opportunities in the medical sector. It helps medical practitioners to better understand the challenges and opportunities associated with AM and AM manufacturers to better identify where to focus their R&D efforts and how this would impact future AM adoption levels. Furthermore, this work extends current theory supporting the design of an ecosystem model for AM in the medical sector following the ecosystem pie model.

Studies the problem of producing a product range with high customer service in the implosive industries. In the implosive industries a wide range of end products are manufactured from a limited number of standard materials. The problem of producing a product range with high customer service is studied within the framework of product range, capacity and customer acceptable lead time. In the existing just‐in‐time literature, strong emphasis is put on set‐up and through‐put time reduction for achieving high product availability with low cost. However, the decision regarding the product range and its implications on production control is not adequately considered. It is proposed that the relevant time scope to consider demand and capacity is the product range cycle. Three cases from the food and paper industry are studied. The cases indicate constraints for achieving quick response capability.

The challenge faced by supplier companies in the grocery supply chain for implementing collaborative planning, forecasting and replenishment (CPFR) is how to get the retailer to forecast, especially when it has not been necessary before. In this paper a solution that would allow collaboration on a wide scale is presented. The forecasting approach is called “rank and share” and uses input from the retailer’s existing planning process – the category management process. The benefit of using category management as the basis is that the retailer can scale up collaboration with a large number of suppliers without increasing planning resources. For the supplier the benefit is point of sales forecasts at the time of the assortment decision. To support this collaborative forecasting process there is a need for more robust replenishment solutions, new measures to illustrate benefits, and for a distributed planning architecture and software. Potential solutions for these are also discussed in the paper.

Current e‐grocery models are based on the consumer making his or her purchase over the Internet, and the e‐grocer delivering the purchase to the household. However, there are numerous opportunities for innovative new services. Analyzes the opportunities offered by bar code and radio frequency identification (RFID) technology to develop a new type of e‐grocery related service, namely vendor‐managed inventory (VMI) in the household. In assessing the opportunity for extending the value offering of an e‐grocery business to VMI in the household a number of key operational issues are addressed, i.e. data capture, solution robustness and cost saving potential in the supply chain. The analysis indicates that the development of new value offerings such as VMI is critical if e‐grocery businesses are ever to gain a competitive advantage over traditional retail formats.