Right now, even if your factory is relatively well equipped with IoT devices and RFID chips that can send production information back to your control tower, there’s a good chance that you’re still relying heavily on time-triggered events as your products make their way across the production floor. Sure, you’re gathering data at various stages of the production process, but that data isn’t automatically causing anything to happen. If something seems to be going catastrophically wrong, a production planner might get an alert and perform some manual triage, but most of the time the data functions as something of a post-mortem.
Do you check the weather report before leaving for work? What about for the upcoming weekend, to be sure your plans for a hike won’t be washed out? OK, how about paying attention to the long-range outlook, like how much snow is expected next winter and how that will affect the prospects of a drought the following summer? Weather forecasting shares many aspects with demand forecasting for your supply chain. You need to be able to look at the near term—say the upcoming few weeks—as well as the next 3-18 months and beyond. This is equivalent to checking the weather for tomorrow, the next two weekends, and the upcoming few seasons. If you want the whole picture, you need to gather as much information as you can on all three time frames.
Life in the digital age is meant to be easier for manufacturers: rather than using spreadsheets to plot out potential production and logistics plans that attempt to meet customer needs within existing constraints, you’re supposed to be able to plan digitally—arriving automatically at the optimal route for your fleet to take from the factory floor to the distribution center, or the right production ratio to minimize downtime. This is where things like advanced planning and scheduling come in. They offer digital planning processes for the digital era, helping manufacturers to boost efficiency and limit disruptions.
If you could see the future, what would you do? Well, first off you would probably buy a bunch of winning lottery tickets—but you might also attempt to optimize your day to a certain extent. Instead of being taken off guard and having to scramble to make arrangements when you get an unexpected call from school that your kid is sick, for instance, you’re already on the road, having made arrangements to work from home for the day so you can tend to him or her. On the way home, you know that your child’s going to want their favorite comfort food, so you’ve already called in a pizza order.
Today we’re dusting off our supply chain crystal ball and are going to give you a glimpse of what inventory management may very well look like ten years from now given the advancements made in just the last five years or so. Logistics 4.0 is combining with Industry 4.0 to lead to massive disruptions in how the manufacturing value chain is run. IoT sensors, RFID tags, and AI are allowing for more automation, and advanced analytics are giving planners unparalleled transparency into the supply chain. This combination is potent in its ability to allow for more accurate forecasting and planning adjustments down to the day or even hour. In the future, these practices will eventually reach even wider adoption, becoming ubiquitous across sectors and allowing inventory management to ditch the pen and spreadsheet once and for all.
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.