Imagine a scenario: Your company has contracted a shipper or freight forwarder to complete a delivery of parts to one of your customers. Because of extensive data-collection during your research and development for the parts, you know that high temperatures over a prolonged period of time can increase the part’s failure rate. As a result of a shipping delay, these parts spend too much time in a container that’s not properly temperature controlled. 

Pop quiz: when’s the last time, either in a personal or a professional capacity, that you made a purchase from a business that did not have a website? Sure, you may have wandered into a charming little brick and mortar store and made an impulse purchase, or maybe you did a bit of antiquing, but I’ll bet that for most major purchases in recent memory you would have been loath to place your trust in a business with no online presence. This is, of course, with good reason. A web presence allows you to read product reviews from other customers, gives you the resources to make more informed purchasing decisions, and lends legitimacy to their enterprise. Once you’ve experienced the added conveniences of a digital business, it’s unlikely you’ll be eager to go back to the old way of doing things.

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?

Any production planner who has spent time working on non-clocked production processes can tell you that it presents challenges and hurdles that simply don’t exist on assembly lines or in other linear production processes. And yet, for some manufacturing outfits, non-timed production is the best way to maximize their machine and personnel resources while maintaining a relatively flexible and adaptable production environment. How do we reconcile the difficulty of scheduling production in a job shop with the obvious value that it presents for many businesses, and what can that tell us about the future of job shop scheduling? 

As experts tout the possibilities of Industry 4.0 for reshaping the world of manufacturing, it’s quickly becoming clear that the changes and paradigm shifts will not be limited to the factories in which they originate. Shipping, logistics, and commerce in general will all feel the effects of the fourth industrial revolution in numerous ways, some predictable and some not. One thing that Industry 4.0 has rapidly made clear, however, is that the age old distinction between providers of goods and providers of services is little more than an elaborate fiction. Even if you’re an OEM providing smaller parts for other business’s production streams, in the modern era you’re still essentially a service provider, offering delivery of parts at the agreed upon time and support throughout the parts’ life cycles in order to facilitate an ongoing relationship that bolsters existing production cycles.

Here at the flexis blog, we’ve spoken on more than one occasion about the inherent difficulties of job shop scheduling and the significant value added potential of developing a smart, digitized workflow for non-timed production planning. Because there is no known algorithm that can efficiently solve the problem of non-clocked production under all circumstances, the pen and paper production planners of the world are almost certainly failing to optimize their machine and personnel usage in job shop production settings. On the other hand, the path to optimal planning can appear dauntingly complex. To help you as you navigate these hurdles, we’re happy to present a case study on ENisco’s successful attempt to master the job shop problem.

Imagine you’re working in tech support, and you receive a call from someone who’s having trouble getting his phone to send and receive text messages. You try all of the usual tactics, asking the caller to turn the phone off and on again, etc., before checking to make sure that the phone is running the latest version of its operating system. The caller concedes that it probably isn’t, but as you walk him through the process of updating he continues to run into problems. “How,” he asks, “do I see what operating systems I am running?” “How do I access my settings?” “How do I get to the home screen?” It is only as you dive deeper into the rabbit hole that you realize that your interlocutor doesn’t have a smart phone at all, but an old rotary phone without the slimmest chance of ever accessing the internet.

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: 

Each year, topics like big data, advanced analytics, machine learning, and artificial intelligence dominate conversations about supply chain technology, becoming the focus of increasing amounts of speculation as the real power of these technologies becomes clearer and more apparent. As a result, it can be difficult to parse the jargon from the meaningful discourse and the pragmatic analyses from the wishful thinking when deciding how to set expectations for the evolving global supply stream. In the spirit of helping to put all of the hype into context and give supply chain managers the tools they need to look towards an ever-uncertain future, here are a few predictions for the future of advanced analytics:

Fun fact: In computer science, job shop scheduling is considered an NP-hard problem, which in this case means that the problem is complex enough that there is no known algorithm that can solve it quickly under all circumstances. If you’ve ever tried your hand at non-timed production scheduling, you can likely understand why this would be the case. As in the case of the traveling salesman problem (another NP-hard problem that famously involves finding the shortest route between a set of houses that must all be visited), non-timed production schedules generally lack an obvious linear path through the production floor, meaning that finding the option that yields the shortest product makespan is laborious at best.