Notes
1 1 Original equipment manufacturer.
2 2 Computer‐aided design.
3 3 An alternative approach is specialized platforms such as Shapeways or Materialise (Wolff and Kern 2019) that offer “printing on demand” services to consumers (Rayna et al. 2015).
4 4 A subject matter expert in a study by Mellor, Hao, and Zhang relativizes this, suggesting that “in terms of material usage … particularly with the metals it's very good but you have to go through a high energy process of turning it into powder in the first place, do you really gain a benefit there? It's marginal to be fair. The actual machines are they really efficient at building stuff now? No … In terms of efficiency of use of energy they are not very good at all (Mellor et al. 2014).”
5 5 For an overview about the main services offered by 3D printing platforms cf. (Rayna et al. 2015).
6 6 She suggests that this could be overcome by regulating software instead of physical objects. As AM machines manufacture objects according to specifications provided in a design plan, product safety could be addressed by controlling the sharing of created plans and preventing unsafe ones from being distributed or sold (Neely 2016).
7 7 The seven categories are photopolymerization (including the technologies VAT, SLA, DLP, CDLP), powder bed fusion (DMLS, SLS, SLM, MJF, EBM), material extrusion (FDM), material jetting (MJ, NPJ, DOD), binder jetting (BJ), sheet lamination (LOM, SL), and directed energy deposition (DED, LENS, EBAM) (Wohlers 2012; Dassault Systèmes 2020).
8 8 Physical and mechanical properties differ with the part orientation.
9 9 Gartner explains all phases as follows. Innovation Trigger: A potential technology breakthrough kicks things off. Early proof‐of‐concept stories and media interest trigger significant publicity. Often no usable products exist and commercial viability is unproven. Peak of Inflated Expectations: Early publicity produces a number of success stories – often accompanied by scores of failures. Some companies take action; many do not. Trough of Disillusionment: Interest wanes as experiments and implementations fail to deliver. Producers of the technology shake out or fail. Investments continue only if the surviving providers improve their products to the satisfaction of early adopters. Slope of Enlightenment: More instances of how the technology can benefit the enterprise start to crystallize and become more widely understood. Second‐ and third‐generation products appear from technology providers. More enterprises fund pilots; conservative companies remain cautious. Plateau of Productivity: Mainstream adoption starts to take off. Criteria for assessing provider viability are more clearly defined. The technology's broad market applicability and relevance are clearly paying off (Gartner 2020).
5 The Role of Robotic Process Automation (RPA) in Logistics
Mac Sullivan1, Walter Simpson1, and Wesley Li2
1 NNR Global Logistics, Dallas, TX, USA
2 Konica Minolta, Tokyo, Japan
Introduction
Companies Under Pressure
With margins shrinking across the board for many freight forwarders, executives are looking for ways to either increase top‐line revenues, for instance, through value‐added services, or reduce their operating costs. Outsourcing of manufacturing has matured with the help of cheaper transportation costs and the improvements in international communication, for example, through the Internet. This led the world's largest brands to have access to cheap manufacturing labor. In turn, these developing countries had more resources to devote to education, which then also opened other service avenues like call centers and information technology (IT) development centers. The delegation of such IT‐enabled business processes to an external service provider is called business process outsourcing (BPO) (Mani et al. 2010).
Many of the large freight forwarders, otherwise known as logistics service providers, started to take advantage of this lower service labor by creating BPO centers of excellence. Instead of paying a person in Germany or the United States $10–20 an hour to create a bill of lading or track a container, they could do this same process in India, Malaysia, or the Philippines for a fraction of the price. However, many small‐ to medium‐sized logistics companies did not have the scale or worked in a too decentralized, loosely knit environment that deterred them from outsourcing their documentation work up to this point. As especially these companies now look to reduce their operating costs, automation and digital workflow optimization are quickly becoming a real alternative to outsourcing to a cheaper labor cost country.
RPA as a Solution
Automation has been placed at the forefront of the digitalization trend that is sweeping across the business community. There are several forms of automation, but in this case, we will be evaluating robotic process automation (RPA) as an emerging technology that is garnering substantial attention in the logistics community. According to the Institute for Robotic Process Automation, “Robotic process automation is the application of technology that allows employees in a company to configure computer software or a ‘robot’ to capture and interpret existing applications for processing a transaction, manipulating data, triggering responses and communicating with other digital systems” (Institute for Robotic Process Automation 2020). Research into the application of RPA shows that this technology is enabling automation in areas that had in the past shown too expensive to do so (Barnett 2015). The cost of virtual RPA robot worker is between 10 and 19% of a local full‐time employee (FTE) and roughly 33 and 50% of an FTE in an outsourced location (Prangnell and Wright 2015; Slaby 2012; Willcocks et al. 2015). Considering such price points, it seems that there is a potential for substantial savings over the way companies are currently operating. Therefore, in this chapter, we will investigate the use case of automating white‐collar documentation work using RPA.
To address the subject of RPA, an explanation of what the technology is and how it fits into the context of an international logistics company is initially needed. A logistics company typically has large departments of clerks who are creating documents like bills of lading or invoices. These documents are often created by directly copying parsed information from documents received from clients and pasted into new templates. By mimicking the actions of these clerks, it is feasible that a properly trained automated technology could take over this task. Specifically, an RPA bot works in the presentation layer of a system and acts like a human using the same inputs that a mouse and keyboard would by clicking and typing (Slaby 2012). An RPA bot does not necessarily live within a source system or software like Microsoft Word or SAP's enterprise resource planning (ERP); it lives on top of infrastructure like a Windows desktop (Aguirre and Rodriguez 2017). This allows the bot to cross multiple software and reduces the need for integrations and the risk of disrupting system logic. By not living in the source code or database, RPA bots are much more like a human worker and do not require the user to have a lot of coding skills as opposed to traditional system‐to‐system automation (Asatiani and Penttinen 2016; Slaby 2012).
Evaluating Heavyweight IT to Lightweight IT Automation
When evaluating any project, it is important to figure out which resources you have