How additive manufacturing drives business model change: The perspective of logistics service providers
Highlights
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We structure the variety of LSPs' reactions to the threats and opportunities of AM.
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Our approach combines a taxonomy and cluster analysis of 52 AM activities of LSPs.
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We explore how LSPs adapt their traditional business models to digital AM.
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Propositions and the extensive overview of AM activities foster future research.
Additive manufacturing (AM) is expected to facilitate local manufacturing in shorter, less complex supply chains and, thus, impact the demand for traditional logistics services. With increasing dissemination, AM confronts logistics service providers (LSPs) with the question of how they should adapt their business model to the threats and opportunities that come with the emerging digital technologies. We structure the AM activities of LSPs and develop a deep understanding of their resulting business model dynamics. For this exploratory purpose, this study develops a taxonomy and performs a cluster analysis to present six clusters of how LSPs approach AM today. The six profiles include LSPs that reactively monitor AM or, in contrast, proactively leverage AM for their internal operations and the development of new services for their external customers. Among them, four profiles entail fundamental changes to the traditional business models of LSPs. We find that these LSPs oftentimes continue to rely on their traditional “analog” service strengths to offer integrated service bundles of AM and logistics solutions. They bridge their lack of specific resources by strategic alliances with AM experts. Only a few LSPs have started severing ties to their traditional businesses to develop digitally dominated, platform-based AM services that require different resources. Overall, the comprehensive picture of AM activities enables us to contribute to the knowledge of how LSPs navigate in the digital age and to the nexus of business model dynamics and emerging technologies. We propose a set of propositions and support practitioners in analyzing and designing AM activities.
Introduction
From a supply chain perspective, the essential game-changer of additive manufacturing (AM) technologies lies in the digitalization of the manufacturing process. Parts are manufactured layer-by-layer directly from the digitally available product specification without product-dependent setup and tooling. This significantly reduces production setup time and upfront costs (Holmström
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Step 1: How additive manufacturing drives business model change: The perspective of logistics service
Additive manufacturing (AM) is expected to facilitate local manufacturing in shorter, less complex supply chains and, thus, impact the demand for traditional logistics services. With increasing dissemination, AM confronts logistics service providers (LSPs) with the question of how they should adapt their business model to the threats and opportunities that come with the emerging digital technologies. We structure the AM activities of LSPs and develop a deep understanding of their resulting business model dynamics. For this exploratory purpose, this study develops a taxonomy and performs a cluster analysis to present six clusters of how LSPs approach AM today. The six profiles include LSPs that reactively monitor AM or, in contrast, proactively leverage AM for their internal operations and the development of new services for their external customers. Among them, four profiles entail fundamental changes to the traditional business models of LSPs. We find that these LSPs oftentimes continue to rely on their traditional “analog” service strengths to offer integrated service bundles of AM and logistics solutions. They bridge their lack of specific resources by strategic alliances with AM experts. Only a few LSPs have started severing ties to their traditional businesses to develop digitally dominated, platform-based AM services that require different resources. Overall, the comprehensive picture of AM activities enables us to contribute to the knowledge of how LSPs navigate in the digital age and to the nexus of business model dynamics and emerging technologies. We propose a set of propositions and support practitioners in analyzing and designing AM activities.
Introduction
From a supply chain perspective, the essential game-changer of additive manufacturing (AM) technologies lies in the digitalization of the manufacturing process. Parts are manufactured layer-by-layer directly from the digitally available product specification without product-dependent setup and tooling. This significantly reduces production setup time and upfront costs (Holmström et al., 2010). Several AM machines are even capable of simultaneously producing different parts (Olsen and Tomlin, 2020). With this inherent flexibility compared to traditional, tool-based manufacturing, AM fosters the shift from global, centralized supply chains to decentralized, small-scale manufacturing close to or even at the point of demand (Srai et al., 2016). AM supply chains are expected to become shorter, less complex, and involve fewer actors (Durach et al., 2017). Logistics service providers (LSPs) offer support and management services to ensure smooth operations in supply chains. Consequently, the dissemination of AM directly affects their business (Holmström and Partanen, 2014). Put simply, digital files travel easier than physical products (Verboeket and Krikke, 2019) and, thus, require less and different forms of handling. LSPs have recognized the threats and opportunities that come with AM and have started to tackle them.
A famous example is UPS, which has offered integrated, end-to-end AM and logistics solutions with in-store polymer 3D printing for consumers since 2013 (Berman, 2016). Today, we observe a decline from 62 to 20 listed UPS locations for 3D printing in the US (UPS, 2021). In addition, some LSPs use AM in their operations, most notably for spare parts provision by air carriers, including Air New Zealand, Emirates, and Etihad. By sourcing AM cabin parts (e.g., monitor frames, shrouds, and bumpers) from AM specialists or by building in-house production capacities for AM, these LSPs have started to replace traditional sourcing channels. AM is beneficial for these LSPs because small volumes of lightweight parts can be manufactured on-demand, reducing lead times and high inventory costs (Air New Zealand, 2018; Emirates, 2017; Etihad, 2019). Other LSPs monitor AM and assume a waiting position for now. For example, DHL has lowered its expectations for AM in its 2020 logistics trend radar. DHL predicts AM to complement (and not replace) traditional manufacturing and, thus, have a limited effect on the demand for logistics services (Deutsche Post, 2020).
The examples demonstrate a wide variety in LSPs' reactions to AM. Some LSPs may be in the process of creating new AM business models (e.g., UPS). In addition, LSPs may revise their internal operations and substitute traditional suppliers (e.g., Air New Zealand, Emirates, and Etihad) or may not expect AM to have a fundamental impact on their business yet (e.g., DHL). So far, the operations and supply chain management (OSCM) literature lacks an understanding of how and why traditional supply chain actors like LSPs respond differently to AM (Öberg et al., 2018; Savolainen and Collan, 2020). Gaining such an understanding is fundamental for exploring the impact of AM on the existing business model of LSPs and the resulting interplay of AM activities and traditional logistics services. Moreover, it is an essential prerequisite for investigating the performance and organizational implications of LSPs' reactions to AM in future research. This study aims to fill this gap by considering a broad sample of LSPs and providing a structured overview of their responses to AM, both externally with new or adjusted business models and internally by adapting their operations. As our interest rests on capturing the current state of LSPs’ AM activities, we focus on a process-based perspective. We address our objective in three research questions:
RQ1
How do LSPs respond to AM, that is, which specific AM activities of LSPs can we observe as a reaction to AM?
RQ2
What are the underlying reasons for LSPs to pursue these specific AM activities?
RQ3
How are the AM activities interwoven with the traditional business models of LSPs?
To investigate these exploratory research questions, we concentrate on LSPs that have already initiated their AM activities. We draw on the nexus of business model dynamics and emerging technologies as our theoretical lens and make use of arguments from the resource-based view (RBV), particularly related to dynamic capabilities. As suggested by Golicic and Davis (2012), we follow a mixed-methods approach that begins with a qualitative taxonomy development to structure AM activities of LSPs. Subsequently, we classify the AM activities of a selected sample of 47 LSPs based on the developed taxonomy. This classification uses data collected from publicly available sources and semi-structured interviews. We propose a six-cluster solution demonstrating distinct profiles for AM activities of LSPs via quantitative cluster analysis. We find one profile of LSPs that reactively follows information about AM (Monitors), two profiles that proactively leverage AM for their internal operations (Explorers and Co-Industrializers), and three profiles that proactively develop new services (Traditionalists, Complementors, and Intermediaries). On this basis, we explore the underlying reasons for these AM activities and demonstrate how AM entails fundamental changes in the traditional business models of four of the profiles.
The primary contribution of this study is a comprehensive picture and a profound understanding of the state of AM activities of LSPs, which we compile from public data sources and spotlight interviews. This structured overview of AM activities enriches the young stream of OSCM literature on AM business models. Furthermore, based on the analysis of the six derived profiles of LSPs, we contribute to building theory in two areas, summarized in a set of research propositions: First, we identify the specific reasons why LSPs respond to AM and, thus, build knowledge on why the service-based logistics industry adapts to AM. This relates to the literature dealing with LSPs' approach toward innovation and digital transformation (e.g., Busse and Wallenburg, 2011; Cichosz et al., 2020; Mathauer and Hofmann, 2019). The case of AM is of particular interest as it represents potentially disruptive emerging digital technologies. Thus, investigating LSPs’ response to AM gives us valuable insights into how LSPs try to stay competitive in the era of digital supply chains (Goldsby and Zinn, 2016; Stank et al., 2019). Second, this study contributes to emerging technologies and business model dynamics in general (e.g., Baden-Fuller and Haefliger, 2013; Chesbrough, 2007) and in the AM context (Rong et al., 2018). Empirical investigations of business model dynamics are still scarce (Cavalcante, 2013). We show that although several LSPs have begun AM activities, their AM business models are not yet fully established. Furthermore, their AM business models heavily rely on their traditional services at the currently emerging stage of AM. LSPs enter strategic alliances with AM experts, compensating their skill and asset deficits, to cooperatively offer novel combinations of logistics and AM. Only a few LSPs decouple their AM activities from their traditional logistics services. Finally, we compile managerial implications. Among others, we provide insights for managers of LSPs in the design of new AM activities and the classification of their existing ones.
The remainder of this paper is structured as follows. Section 2 introduces the theoretical background for investigating the reaction of LSPs to AM. Section 3 details our combined approach of a taxonomy development and cluster analysis. In Section 4, we propose and analyze the six-cluster solution of AM activities of LSPs which contributes to answering RQ1. Section 5 discusses our findings in the broader context of LSPs’ reasons for pursuing AM activities (RQ2) and business model dynamics (RQ3), summarized in a set of propositions. Section 6 presents our conclusions and suggests paths for future research.
