Let’s say it’s the day after Thanksgiving, and you’re trying to see how far you can stretch your leftovers. You take a good hard look at the fridge, where you note that given the amount of turkey, some mashed potatoes, some cranberry sauce, etc., you could probably put together another 6 person-meals for your family of 5. One of your family members, however, is a vegetarian, which means that any meals you put together for them can’t have any turkey or gravy. As such, they’ll need additional yams and mashed potatoes, which affects the proportions of the other plates that have to be assembled.
We’ve all been there at one point or another: you’re scheduling production for a large, complex item like an automobile in a plant that produces multiple different models. Your materials and capacity are relatively well synced, and your plans for the rest of the week or month seem pretty stable. Then, all of a sudden, a huge rush order comes in from an important client. All of a sudden, you're scrambling to allocate the materials and capacity necessary to slot this order into your existing plans. You pull resources from one order to give this one the resources it needs, but that has a complex ripple effect through your ordering flows; by the time you’re done reworking the schedule, your production plans are confusing and far from optimal.
Reports of your job’s impending obsolescence have been greatly exaggerated. Sure, as Industry 4.0 systems continue to gain traction the nature of work, not just in the automotive and industrial spheres but across the entire global economy, is likely to be affected in tangible ways by the rise of connected, cyber-physical systems and the increased use of internet of things (IoT) devices. But despite what you might have heard, this doesn’t mean that people’s jobs are going to vanish at an unprecedented rate. After all, the first three industrial revolutions (steam power, electricity, and computers, respectively) helped to expand the labor force rather than contract it—why should the fourth industrial revolution be any different?
Let's say you're an OEM, with a sleek manufacturing space and a sophisticated, technologically cutting edge process for creating a particular automotive part. But you have a problem: At this point, your incredibly sophisticated production techniques aren't not being complemented by an equally sophisticated, multi-level approach to production planning and resource scheduling. This results in a disconnect between the high quality of your products and your ability to maximize capacity and meet customer delivery requirements. How can you build towards a production planning workflow that complements your product and fulfills your business goals?
IT pioneer and philosopher Ted Nelson, who coined the term hypertext, once famously said, "The good news about computers is that they do what you tell them to do. The bad news is that they do what you tell them to do." Historically, in the automotive supply chain, this couldn’t be more true. New technological developments like early computerized workflows and simple process automation were hampered by information silos and integration issues not because the technology lacked sophistication, but because they still had to be told what to do in very specific ways.
Imagine you own and operate a pin factory at the dawn of the Industrial Revolution. One day, you come in and announce to your workers that you’ll be implementing steam powered machinery into your production processes, completely reimagining many existing workflows in the process. How do you think your employees, especially those involved in planning out production workflows, are likely to react? Some of them might be excited or intrigued, certainly, but many others are likely to meet the news with apprehension or even distrust. After all, they were doing just fine making pins by hand all this time.
New technologies—from steam power to modern computers—have been the driving forces in supply chain management since the beginnings of industrialized society. Today, supply chain technology is changing at an exponential rate, providing supply chain planners with possibilities that would have seemed like science fiction even a few decades ago. If you follow the news and trends in SCM, you’ve no doubt noticed that machine learning (ML) is often touted as the next major innovation in this long line of technological evolutions—but what, exactly, is it, and how can supply chain managers put it to use?
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?
In February of 2018, popular fast food brand KFC was in the midst of making some big changes to its UK supply chain. They were in the process of switching from Bidvest Logistics to DHL as their primary distributor, while simultaneously streamlining their warehouse system from six facilities serving the country to only one. Anyone who keeps current with supply chain management likely knows what happened next: the restaurant was forced to temporarily close more than 700 of its 900 locations in Great Britain. The reason? Chicken supplies were not reaching the stores.
Imagine you’re a trader on the floor of the New York Stock Exchange. Every morning, you check the prices of the stocks that you’re interested in, and you act on those numbers, not checking them again until the end of the day. Your competition, on the other hand, is using real-time information to inform their trading decisions. Which technique seems more likely to yield a profitable trading strategy? Your knee-jerk reaction is probably that you’re going to lose money virtually every day, because your competition has a more accurate picture of the real financial landscape while you’re using information that’s obsolete virtually as soon as you set foot on the trading floor.