Last month, Abu Dhabi Ports introduced a unique blockchain solution, enabling freight forwarders and their customers to digitally check on the statuses of shipments and transports while facilitating real-time tracking of cargo and documents. Officials expect the result to be a more efficient shipping environment in which reduced paperwork and administrative tasks could potentially slash 20% off of physical shipping costs for freight forwarders who take advantage of the new system. While this might seem revolutionary, it’s actually emblematic of shipping trends that have been evolving for some time. Digitization has been increasing for some time, and the world is finally starting to see the results of this paradigm shift.

Just as the modern factory is adding new, intelligent technologies in order to create connected, interoperable workflows, the modern supply chain is rapidly becoming smarter, more networked, and more technologically advanced. Though the so-called fourth industrial revolution gets most of the attention, there is another revolution occurring simultaneously within the world of logistics, and it’s changing the way that products make their way from production facilities to customers. In the spirit of Industry 4.0, some have taken to referring to this new logistics paradigm as Logistics 4.0—but what exactly does this term mean?

Industry 4.0, also known as the Fourth Industrial Revolution, has been hailed as the underpinning of the modern smart factory, promoting the rise of cyber-physical systems, increased machine-to-machine communication, and decentralized decision-making within production processes. The concepts that make up the Industry 4.0 framework have been suitably revolutionary, and they're rapidly changing the way that manufacturing businesses operate, but many organizations are realizing that this framework doesn’t have to stop at the edge of the factory floor. Indeed, the very same principles that drive modern, digitized manufacturing are also bringing about the era of Logistics 4.0.

Let’s say you’re driving down a winding country road to some remote destination. At first, navigating is easy, but as the sun goes down and your headlights come on it becomes more and more difficult to make sure that you’re driving safely down the correct course. Eventually, night has fallen completely and your headlights provide the only illumination—you have to slow your driving speed, so that if an animal or other unexpected nocturnal being wanders into the path of your headlights you’ll have enough time to stop the car. If there’s stormy weather, the visibility becomes even more limited, and the possibility of an unexpected snafu increases.

Let’s say you’re tasked with designing a factory. You need to decide how to integrate various production lines, where to locate specific resources, how to organize space in a way that maximizes efficiency within and between processes, and how to leave room for potential future process changes. The interplay of a complex series of elements and structures will ultimately determine the success or failure of many planned production programs, so your grasp of the interrelations between these elements must be excellent. Once the factory has been established, things become even trickier. If you want to reposition a piece of machinery, for instance, you should know in great detail what processes involve that machine and how those processes will be affected. In short, these are tasks that you wouldn’t undertake without a carefully devised strategic plans that accounts for a variety of modalities.

Imagine you’re at the grocery buying cooking supplies for the coming week. You see that tomatoes are on sale if you buy them in quantities of ten. Hoping to make use of the savings, you do some quick calculations in your head: the ripe tomatoes will remain fresh for about a week; you cook roughly one meal a day; your favorite dish requires two tomatoes. You determine that you could easily utilize ten tomatoes before they go bad, but you would have to commit to making the same dish five nights out of seven, and you might not be in the mood for it later in the week. 

Imagine for a moment that your company is in the businesses of manufacturing parts for both conventional and hybrid vehicles. Based on the demand from your customers over the past year or multiple years, you develop a plan for allocating resources and person-hours to produce the right proportion of hybrid parts to conventional parts based on expected demand. Unexpectedly, a sharp increase in worldwide oil prices triggers a shift in demand away from hybrids and toward conventional automobiles that rely on gasoline. How will your production plans cope with the sudden change? Will your factory floors continue to produce a surplus of hybrid parts while orders for conventional parts go un-filled, or do you have the necessary planning agility to shift production to align with new demand paradigms?

Hopefully, you are in a position to safely assume the latter. But for many complex businesses, rapidly adjusting to demand is a perilously involved tasked, requiring the ability to assess and respond to new circumstances virtually instantaneously. One of the keys to building this level of agility into production processes is the integration of real-time and production planning.

Machines are nothing new to the manufacturing industry - in fact, to say that is quite an understatement. Since the Industrial Revolution, the production facility floor has ground zero for how manufacturing companies incorporate non-human elements or intervention into how goods are produced and distributed. Fast-forward to today’s manufacturing landscape and the introduction and proliferation of modern machine-based aspects such as robotics or artificial intelligence to streamline production processes and increase production efficiency is perhaps the most pressing, pertinent issue in modern production processes.

But what’s slowly gaining more and more prominence in the manufacturing industry is machine learning outside of the actual production space and the ways in which a digitized manufacturing platform can enhance both the production and logistics side of global supply chain management. Understanding machine learning in this context — a holistic reimagination of how this technology can be a disruptive force in a cross-organizational way from sales and procurement to transport logistics — puts machine learning on a grander stage in terms of shaping the future of the automotive supply chain. In addition, machine learning can provide planners and managers with a critical competitive advantage in a somewhat uncertain, variant-rich manufacturing space.

Modern day supply chain management is often about finding reductions in costs, expenditures, wasted resources, or misallocations in how raw materials are spread across complex manufacturing networks and value chains. But this worldview often neglects or places little value on the fact that supply chains in and of themselves can be a key driver in affecting growth, increasing revenue, creating business moments, and forging new partner networks or footprint expansion.

But, as with almost anything in modern SCM (supply chain management), such achievements are often more easily discussed than realized. However, that doesn’t mean manufacturing companies don’t have the tools necessary to transform their supply streams from merely a vessel of procurement and product distribution into an important vehicle for engineering long-term, sustainable growth and productivity. Forward-thinking planners and managers can, with relatively minor adjustments to their SCM strategy, create a supply stream with the power to not only drive growth and innovation, but also the capacity to generate real revenue for companies in an increasingly competitive marketplace.