consulting firm. However perhaps the thing that will help companies the most is a clear written understanding of their global processes and procedures. Having a roadmap of processes helps to lead the way to be able to identify where commonly occurring tasks happen and facilitate a smooth transition to improving internal mechanisms. This has been traditionally not very well documented in logistics as much of the process has relied on email communication. Companies with business processes that necessitate a significant amount of person‐to‐person communication have held on to their legacy communication mechanisms because their stakeholders valued it and there was not a more or “equally efficient digital alternative” (McAfee and Brynjolfsson 2017). Logistics, which is representative of the B2B service industry, fits this description well. Even container ship operators, multibillion‐dollar juggernauts who went through their evolution as they adopted the use of the standardized container in the 1960s and 1970s, are struggling to upgrade their communication methods (Tipping and Kauschke 2016). To learn about how China is using its Belt and Road Initiative to lift its trading partners, see Wheeler's chapter on the Digital Silk Road.
While companies like Maersk have set up shared service centers in developing countries, the adoption of cutting‐edge processes is staggeringly slow as can be seen by response times and incorrect invoicing still plaguing the industry. Processing hundreds of thousands or more of virtually identical transactions that had traditionally been handled, monitored, or audited by a human worker has been the best use case so far for process automation and implemented across the banking, insurance, telecommunication, and travel industries. This perfectly applies to logistics and transportation companies whose operations are at high risk of automation (Bughin et al. 2016). Chasing the top line has been long integrated into the mindset of logistics companies who are consistently looking for the next target customer to improve organizational health instead of looking toward process improvement.
Foundations of a Digital Transformation
Prioritization of Technology Exploration
Logistics companies must adapt and proactively look for ways to use technology to reinvent themselves (Tipping and Kauschke 2016). Consistently, logistics companies have been looking outward to new investments in assets or new client acquisition instead of building technological prowess in‐house that would help to best prepare for a digital transformation. Unlike finance or manufacturing companies, commercially focused individuals who spend a large part of their time selling and catering toward their tier‐one customer lead most logistics companies.
In addition to the financial and time constraints, companies are faced with an overload of exciting new technologies that they should or are being encouraged to evaluate. Logistics companies could invest in capturing data through IoT devices; developing sophisticated self‐learning algorithms based on multilayer regressions to create some form of machine learning AI; eliminating emails, faxes, and spreadsheets by capturing data via new forms of ecosystems using blockchain or optical character recognition (OCR) technology; rolling out digital handshakes in the form of electronic data interchange (EDI); or allowing systems to communicate better with each other through application programming interfaces (APIs). They could even outsource their data management, integration, and software development. These are the conversations that have happened in board rooms of leading logistics companies around the world as they scramble to come up with a technology investment plan.
Connectivity Standardization in Logistics
It is feasible to imagine a network of systems that independently coordinate the facilitation of efficient movement of goods without human intervention, assuming all went according to plan. The novel concept of smart contracts powered by blockchain technology shows the potential for powering this system by eliminating trust and security concerns that plague the current system, but this has yet to reach mass adoption. For more details on this, see Ariguiz, Tran, Margheri, and Xu's chapter on smart contracts.
As if navigating the hundreds of three‐letter acronyms used in logistics was not enough, when it comes to the IT portion of logistics, systemic standardization and incompatibility issues are holding the industry back. From a systems integration perspective, just as a company needs staff that speaks the same language, the global trade network also need systems that speak the same language. Information may need to flow through 12–15 different systems ranging from manufacturers in developing world countries to automated ports. An industry must have some standards in place, as these are the foundations of a company's ability to implement these technologies.
For a company who wants to ship goods and to get shipment information back into its ERP system, it has to connect to the freight forwarder who is coordinating the transportation, who in turn has to communicate with a trucker to pick up the goods, a customs broker, and a shipping line often through specialized messaging partners called. The freight forwarder also needs to link to its internal origin, in China, for example, and the destination office, in the United States, either of which could be a different agent company. With little coordination in terms of standardizing digital connectivity beyond EDI between core segments of the supply chain, there is a huge opportunity for companies like Chain.io who are offering a digital connectivity platform to translate all the different fields from different providers to the freight forwarding ecosystem. With no player having more than a 12% market share, it will take the efforts of entrepreneurs and startups to innovate solutions (Riedl and Chan 2019).
New Business Models Emerging
Now that the majority of logistics companies have adopted enterprise resource planning (ERP) systems as their core systems, such as from Oracle, SAP, or CargoWise One, there has been a rise in the standardization of some global processes that have enabled them to potentially use other technology. Native integrations with SaaS providers like Salesforce, QuickBooks, and Workday are allowing ERPs to exchange data seamlessly and instantaneously through APIs. This exchange creates a mountain of data that if correctly analyzed can be used to identify inefficiency, improve forecasts, and reduce labor costs. AI for logistics is particularly attractive in that there are bill of lading databases, shipment logs, and customs documents of millions of previously moved shipments that could be scrubbed to backtest algorithms.
AI, defined here as sophisticated algorithms that can parse information, has been shown to predict when a customer is going to buy something and when an aircraft engine needs servicing or alert a person that they are at risk of disease (Economist 2017). The hub economy companies have shown the amazing potential of data wrapped with algorithms to solve consumer problems. Technology and investment simply are not enough to enable AI to help give sophisticated business intelligence or better evaluate customer needs. To mine the data that drives AI, companies must have the infrastructure in terms of data management, the will and power to ensure data governance, and the talent to be able to identify, isolate, and cleanse data flows. Talent, as shown in Figure 1.3, is the last step in the foundations of a digital transformation. Traditionally programmers had set about training AI or a robot in rule‐based “teach” patterns. However, with neural networks, raw data can be fed into the network, and the patterns are identified (Lee 2018). All of this is great but is useless unless an organization can feed the network huge amounts of data with clear algorithmic parameters focused on a narrow and specific goal (Lee 2018). In short, it takes a concerted effort by a talented, well‐funded team with a clear set of business goals and strong organizational support in terms of technical and permissioned access to unlock the potential for AI.
Figure 1.3 Foundations for digital transformation.
As more logistics companies move away from working off spreadsheets and emails, they will be able to more effectively utilize technology due to the offering of Software as a Service (SaaS) that is discussed in Berry's chapter on the rise of cloud‐based systems in logistics.