Ever had one of those days where you set out to run three easy errands but end up driving in multiple circles, making 7 unplanned stops and not getting home until well after dinner? Us too, and this is an apt analogy for how some supply chain transportation logistics networks are running these days—with trucks making multiple partial runs, unplanned stops being added to the itinerary at the last minute, and containers sitting on the dock for days. Transportation logistics is chock full of low hanging fruit when it comes to optimizing your network and reducing waste. With the unnecessary movement of goods, half-empty trucks, and trucks sitting around while warehouse staff scramble to find the entire order—this is an area ripe for optimization. We’re going to look at a handful of examples of how you can start working toward the goal of cutting transportation logistics waste from your supply chain.

Remember your last car trip? You spent all that time planning your route to maximize the sights you would see. Then you organized your equipment and snacks and packed the car just right so everything was easily accessible. Can you imagine what would have happened if you went to leave and the car didn’t start? Now your whole itinerary is thrown off, you have to call roadside assistance, change the hotel reservations, not the way to start a great vacation. The analogy to the transportation leg of your supply chain is clear—if you neglect one segment the whole system can come crashing down. In order to optimize your end-to-end supply chain, you need to pay close attention to the transportation network. This is often the place where systems break down and costs can spiral out of control. On the other hand, just as doing preventative maintenance on your car eliminates the possibility of the failure of your vacation, optimizing your transportation network can eliminate cost overruns and other disruptions to your smoothly functioning supply chain. Follow these best practices and you’ll be off to a great start.

Of all the stages of the supply chain, logistics often gets a bad rap. This appears to be largely due to a combination of the seeming unpredictability of the unknowns like weather patterns and fuel costs; and the skyrocketing costs associated with last-mile delivery in recent years. This potent combo makes it all the more unexpected that logistics is also quite often overlooked when it comes to applying learnings from demand forecasting. The predictive analytics used by demand forecasting solutions takes historical data, runs it through advanced AI algorithms and generates predictions for demand in a specified upcoming time period. That sounds pretty useful for cutting logistics costs and leveling out some of the uncertainty that’s endemic to this sector, doesn’t it?

Industry 4.0 is making waves in the manufacturing and supply chain sectors. But what about logistics? How are these same technological advances helping move those products faster and more efficiently? The goal remains unchanged: to use connected workflows and technologies to give people the tools and freedom they need to adapt and pivot with the changing environment and to seek creative solutions to increasingly complex problems. Logistics 4.0 is here, and one of the primary drivers of this revolution is the Internet of Things (IoT). IoT refers to devices of all sorts, be they tablet computers, sensors monitoring machinery or vehicles, or even wearables that track biometrics to ensure the health and well-being of the workforce, that are all connected to the network.

When we discuss Industry 4.0, we often mention the origins of its name, i.e. the concept of the fourth industrial revolution defined by machine-to-machine communication and autonomous processes. With Logistics 4.0, on the other hand, there is typically no such history lesson involved, perhaps because we think of the new logistics paradigm as fundamentally an outgrowth of Industry 4.0. Whether or not that’s the case, it’s becoming increasingly clear that this new era in logistics is very much its own entity—and it’s already changing the way that shippers and freight forwarders (to say nothing of their customers) do business.

This all begs the question, what are the distinct elements that define Logistics 4.0 systems? How do these elements incorporate the logic of Industry 4.0, and how do they build on the logistics paradigms of the past?

As the worlds of manufacturing and retail evolve to meet the unique challenges of the 21^st century, logistics (including shipping and freight forwarding) will have to evolve with them. This will likely mean not just a push towards increased digitization, but a continued reimagining of how logistics operates and provides value. Though many of these changes will no doubt seem daunting, they will also present new opportunities for businesses to grow and gain competitive advantages. Here are a few of the most interesting emerging trends to watch out for: 

Though the industrial revolution was nominally about the introduction of steam powered machinery into factory production, its long-term effects are almost impossible to overstate. What began as an ingenious change to the inner workings of factories became a catalyst for widespread social and political change, arguably leading to the formation of modern capitalism and paving the way for a fundamental redefinition of people’s relationships to labor, their environments, and each other. Though the so-called second and third industrial revolutions were not quite as earth-shattering, they did stimulate the global spread of electricity and the internet, two technologies without which the modern world would be virtually unrecognizable.

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?

Imagine for a moment that you’re at an antiques auction. You’ve scoped out a handful of items that might meet your needs, and you have a strong but flexible sense of how much money you would be willing to spend on any given item. But when the first of your lots is on the auction block, instead of sitting in the auction house, you’re situated at a remote location, watching a live video feed of the proceedings. When you want to place a bid, you have to instruct your representative at the auction house to raise her paddle. Naturally, by the time you’ve done this, the price that you’re acting on is already out of date. As a result, you wind up with none of the items you had hoped for, even in cases where you might have been willing to spend more on them than the price that they ultimately went for. 

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.