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.
Participation in Platforms and Marketplaces
As logistics companies upgrade core systems and storage mechanisms and open up new forms of connectivity options, the reality of submitting rates to a marketplace or having an externally facing quotation portal becomes more of a reality. Platforms will continue to gain attention and capture market share from the traditional service providers. Platforms are discussed in Margand and Heuck, Chapter 16, and in Sullivan, Wong, and Tang, Chapter 23. These marketplaces not only can scale and connect suppliers and buyers with the click of a mouse bypassing the traditional sales cycle, but they also offer instant quotes and the ability to book immediately as opposed to the traditional email‐and‐wait model (Riedl and Chan 2019). Marketplaces are gaining attention especially as they enable a low‐cost solution to direct selling entrants, like steamship lines Maersk and CMA, who now can open their digital sales channels and accelerate disintermediation.
Ironically, research has found that using the performance metric of return on assets (RoA), publicly traded companies in the United States have declined by at least 75% since 1965 (Hagel et al. 2016). This means that despite all the technological improvements that have happened in the past 50 years, companies still are not doing a great job of utilizing technology. Perhaps this could be explained by the lack of focus and the sheer complexity of a large organization. To start a digitalization journey, logistics companies need to invest in talent who have some understanding of logistics, the processes involved, and the integration bottlenecks between core systems that technology could potentially address.
Zoom Out/Zoom In Approach
Assuming that a company can get these foundations in place, leadership teams can then brainstorm on what they know or have learned about their companies' ability to adapt and look to hone their strategy on achieving more in an unpredictable world (Hagel and Brown 2018). The zoom out/zoom in approach to successfully navigating the waters of digital transformation explains how a company can explore not only the short‐term stakeholder‐driven targets but also the longer‐term survival needs of the company (Hagel and Brown 2018). Here are a few takeaways that can be applied:
Zoom Out
1 Take leadership to an outside event or company to open their eyes and give them a glance at the future.
2 Bring
