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 21st 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:
Today’s supply chain managers are often in pursuit of that elusive measure of supply chain health: end-to-end (E2E) visibility. But how do you determine your business's existing level of visibility compared to the level that would best support its particular needs? Just as attaining a high degree of visibility can be a significant operational challenge, measuring visibility can be somewhat of a hurdle in its own right. To the end of helping you clear that hurdle, here are five strategies for ascertaining your level of supply chain visibility.
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.
One of the most crucial facets of the Industry 4.0 revolution is the integration of more robust data analytics into industrial processes. The idea is that by utilizing data in a thoughtful manner driven by specific business goals, businesses will be able to gain new insights into their workflows and potentially add value. As businesses have begun to adopt this mindset the results have frequently been promising—but why should data be relegated to complex analytics processes? In the spirit of turning data into insights, here are six important statistics about Industry 4.0 to consider as you navigate the complexity of the modern supply chain.
There’s no denying it: the pace of the global supply chain is getting quicker every day. Broad increases in connectivity have led to equally broad increases in customer expectations, meaning that when things inevitably diverge from expectations, it’s imperative that supply chain managers react swiftly and decisively. This growing need for lightning fast response times comes with increased pressure to build a value stream that is visible and connected enough to provide planners with the information that they need about existing operational plans and potential plan b’s—including inventory levels, transport routing information, and delivery requirements.
Imagine for a second that you’re an NFL quarterback: you have a plan to throw a forward pass to one of your wide receivers, to whom you’ve dictated a specific pass route. Unfortunately, you’ve neglected to inform any of your other teammates of what you plan to do. Even worse, you haven’t bothered to ask any of your fellow players if they have plans of their own and, if so, how they might conflict with the plan you’ve devised. As a result, when something goes awry, none of your teammates are able to make adjustments on the fly, and your plan has no way of overcoming whatever hurdles crop up.
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.