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Logistics and transportation industry is arguably among the top industries that had to rise to the challenge during the COVID-19 pandemic in order to keep some sense of normalcy. In a disastrous year for the economy and life as we know it, it was the logistics and transportation industry who kept supply chains operating, thus allowing people to acquire the goods they required while in lockdown.

Those ahead of the curve during this time had all but one stark difference with respect to the laggards : data. Talking about data in logistics is not new and many logistics and transportation companies nowadays incorporate some degree of digitalization in their day-to-day operations. However, in most cases they only scratch the surface or even miss completely the point of realizing what data-driven analytics can do for their business.

If hitting a moving target sounds hard, then try hitting it while blindfolded. End-to-end visibility allows companies not only to anticipate disruptions, but also to manage and reduce risk and cost throughout the supply chain, you probably already know that, but this is the fundamental first step in growing and protecting your business through digital logistics strategy.
This may not seem that obvious for companies with just a few concentrated supply points, but for companies focused on volume and time-sensitive delivery, the ability to monitor and respond to external events as transportation delays, order management, warehousing, and in-store delivery can prove critical.
Take car manufacturing as an example, during the disruptions caused by the COVID-19 crisis, the fact that that automotive companies had end-to-end visibility of their internal and third party suppliers meant they could anticipate disruptions in their supply chain, identify at-risk suppliers and factories and take action accordingly to mitigate or handle the impacts of such disruptions, because at the end of the day you can’t fully make a car if you’re missing even the tiniest part. Those actions may have varied from rerouting components from other suppliers, rerouting shipments, changing the output on the factory floor or even —in the worst cases— financial restructuring that in the end allowed the companies to handle and mitigate what otherwise could’ve been a deadly blow to their business.

Companies that were ahead of the curve in the use of their end-to-end data to generate forecasts and models through artificial intelligence (AI) and analytics, could have seen the coronavirus trends gestating as early as January 2020. Analytics would have identified come key clients increasing orders, booking requests and loads of cargo being stored anticipating shortages. If so, these companies probably had the time to anticipate and prepare before the disruptions hit the global market, but companies that were not flexing their data muscles, were caught off-guard.
The best way to be ready for the next big disruption is through a Control Tower model. Control Tower makes use of AI and Machine Learning (ML) algorithms to analyze the data you collect from your end-to-end visibility model to identify potential disruptions and protect your supply chain through optimization of resources and routes, thus saving you trouble and of course, money. Some of these models can even anticipate and suggest one or more course of actions to manage the event; this is what we call prescriptive analytics.

Yet while computers and other «smart» machines can do much for you and even see more than you could there’s still lots of processes that require the human presence and input; however all of these processes can be improved and accelerated through technology. At the end of the day all these steps where humans are required are also data points and generate their own data in the form of documents that can be digitally signed, sales insights that could prove vital for closing a proposal or even a heads up regarding some sensitive event along the chain.
By integrating all these entries along with documents as datapoint within your digital workflow allows not only for richer analytics but also speeds up, optimizes collaboration among your internal and third-party stakeholders and even opens new possibilities for automated and in-person customer service; specially in a mobile world where customer expectations regarding speed and accuracy are higher than ever.
It's all about information and communication, at the right time, for the right process(es) and the right people, in the right context and for the right purposes.

So yeah, data is great! It can tell us what to do next, where we are being inefficient, show our disadvantages, open windows of opportunity and even reveal bits about the future. It shows us what can be possible and help us create a new reality… as long it’s the correct data.
Data is not a commodity, it’s an asset in that provides a competitive advantage and must be kept accordingly. Data quality measures the condition of your data based on its accuracy, consistency, completeness, recency and reliability, ensuring the quality of our data and data sources should be as important as other company metrics like revenue, customer satisfaction and profitability, in all honesty ¡It will help you improve them all!.

As we have discussed a digital logistics model driven by data, analytics, digital collaboration and ensuring data quality has many advantages and right now you may be asking yourself where to begin and how to justify the investment. The latter question is the easiest to solve, in the end the cost of staying stuck will be always higher and may even be a death sentence for your business, so start now! And for the former… well, that’s harder to answer, but you can begin by asking yourself «What problems in my company I’m trying to solve and how solving this problem help me achieve my company’s business goals».