The Digital Transformation of Logistics. Группа авторов

4.1 Conventional supply chain.

Figure 4.2 Additive manufacturing supply chain.

Distributed manufacturing can occur close to customers, disregarding that they might be situated in locations that are hard to reach or where transportation is difficult, e.g. due to political risks. Some scholars even argue that customers themselves could have AM machines at home, cutting out all middlemen of a conventional supply chain. In such personalized manufacturing setup, production and consumption would occur at the same place (Rayna and Striukova 2016). In this supply chain, “economies of one” benefits would allow a decentralized production of custom‐made products or spare parts. However, such production setup with numerous locations close to customers might also result in underutilized AM machines. Research suggests that to overcome the risk of such inefficiencies, machine sharing could be used, and new AM service providers emerge (Chekurov et al. 2018).³ Figure 4.2 shows the AM supply chain. While this supply chain appears to be more straightforward and beneficial, it also has a variety of disadvantages that will be discussed in the next section.

Evaluation

      This section will review the advantages and currently existing bottlenecks of AM.

      Advantages

As described in the previous section, adopting AM would create shorter, smaller, more localized supply chains (Gebler et al. 2014). This would reduce costs for holding inventory, as less safety stock is required, obsolescence rates can be reduced, and work in process would basically not exist (Atzeni and Salmi 2012; Chiu and Lin 2016; Holmström et al. 2016). Also cost for production, especially for small parts, is reported to decrease (The Economist 2012; Petrick and Simpson 2013). As costs for assembly, setup, energy, material handling, personnel, and packaging are lower, the overall costs could be reduced when compared with the conventional manufacturing (Walter et al. 2004). Therefore, small production batches, with customized designs, which also can be quickly changed, become feasible and economical (Holmström et al. 2010). Moreover, investment costs for assets such as tools, machines, or facilities could be decreased (Atzeni and Salmi 2012; Khajavi et al. 2014). And as processes are largely computer‐controlled, the required level of operator expertise could be lowered (Lipson and Kurman 2013). Employing AM reduces the time to market, especially for complex shapes, leading to increased responsiveness in the supply chain (Mellor et al. 2014; Ryan et al. 2017). Consequently, also demand fluctuations could be better managed (Chiu and Lin 2016). AM further increases flexibility, as a wide range of products can be manufactured with one process and the product mix be extended (Weller et al. 2015; Steenhuis and Pretorius 2017). In addition, sustainability benefits are predicted as resources could be used more efficiently due to improved products and production processes (Ford and Despeisse 2016). Researchers also assume that through reduced repair and refurbishment and more sustainable “socioeconomic patterns” in the form of stronger person–product affinities and a closer relationships between producers and consumers, the product life could be extended (Kohtala 2015; De la Torre et al. 2016; Ford and Despeisse 2016). Other environmental and efficiency benefits result from reduced wastage, where reports claim that it can be reduced by 90% compared with conventional manufacturing, and a more energy efficient production process⁴ (Singamneni et al. 2019). Another advantage of AM is that novel, complex structures, such as free‐form enclosed structures and channels, and lattices are achievable (Ford and Despeisse 2016). For example, due to stringent certification criteria, aircraft designers typically have very little design freedom, and optimizations beyond the normal would result in complex geometries that are not possible to produce with conventional methods. As AM removes such limitations, new opportunities for optimized designs are opened up (Singamneni et al. 2019). Finally, AM fosters innovation (Dwivedi et al. 2017). Customers can be involved in the innovation process, providing ideas via crowdsourcing or cocreating via open innovation platforms⁵ (Rayna et al. 2015). However, there are still various product‐, processing‐, and regulation‐related bottlenecks to overcome before AM can truly revolutionize manufacturing and disrupt supply chains (Verboeket and Krikke 2019).

      Bottlenecks

In a study about AM spare part production in consumer electronics, Chekurov and Salmi highlight that only certain components without strict surface requirements can be produced. They concluded that, for example, internal parts inside consumer electronics are compatible with AM where the look and feel is not so critical (Chekurov and Salmi 2017). Other researchers also report issues related to the accuracy of the product, which is depending on the AM machine device mechanism, material, and resolution (Kothman and Faber 2016; Moore et al. 2016). Experts further cite achieving the desired part strength and durability with a current set of material and AM technologies as primary barrier to implement AM (Chiu and Lin 2016; Dwivedi et al. 2017). In addition, problems with process predictability and repeatability result in increased costs due to build failure and quality issues (Baumers et al. 2016). Neely also points out that there might be product safety‐related constraints, considering that while in conventional manufacturing products are tested and certified and factories inspected, in AM the main appeal is the ability to manufacture in dispersed locations (Neely 2016).⁶ Aside from these product‐related bottlenecks, also production‐related ones exist. For instance, a main production‐related restriction is the high cost for implementing AM. This includes high material costs, high machine/equipment costs, high costs for technology acquisition, and high maintenance costs (Baldwin and Lin 2002; Dwivedi et al. 2017). Production speed is criticized for being still too slow, and throughput rates for being too low (Gebler et al. 2014; Baumers et al. 2016; Khorram Niaki and Nonino 2017). This is expedited considering that post‐processing of parts is often required, typically caused by stair‐stepping effects that arise from incrementally placing one layer on top of another or because finishing layers are needed (Ford and Despeisse 2016). Also, based on case study research, Mellor, Hao, and Zhang identified that machine suppliers partly implement restrictions such as which specific powders can be processed – that they typically also offer – or locking down process parameters that hinder R&D practices in the form of process parameters optimization (the machine suppliers offer R&D services that fill this artificially generated gap) (Mellor et al. 2014). Finally, regulation‐related bottlenecks prevail. One big concern is the ambiguous intellectual property (IP) situation. With AM machines becoming more ubiquitous, the traditional forms of IP protection will be significantly challenged (Kurfess and Cass 2014). IP protection in AM is particularly critical considering that while in conventional manufacturing, the copying of a design can be readily traced to a source, in AM there is no need for a specific infrastructure, which renders it difficult to prevent unauthorized replications (Brown et al. 2016). Other challenges include the certification for components (e.g. spare parts) and further liability‐related legal issues, including warranty (Ford and Despeisse 2016; Holmström et al. 2016). This is aggravated by the fact that traditional destructive and nondestructive tests that assess critical product characteristics might not be applicable for parts that are produced with variation by an AM machine or only produced once (Petrick and Simpson 2013). Petrick and Simpson also point out that validation of complex internal geometries equally remains an issue (2013). Considering these limitations, it also remains elusive how processes can be certified (Sirichakwal and Conner 2016). In addition, relying on AM increases the risk for knowledge leaks and product piracy as data is transferred openly and products are possibly manufactured on decentralized manufacturing stations (Bogers et al. 2016; Chekurov et al. 2018). Finally, there is also