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.
By now, you might be wondering just what this exercise has to do with the manufacturing of a complex industrial product like an automobile. Well, simply put, this kind of order customization and balancing of different production ratios is becoming increasingly common as auto manufacturing changes and evolves. Consumer expect to have more control over their purchase without sacrificing a fast turnaround, which puts you in a position like the one we described above: you need a clear view of which resources to acquire in which quantities and how to turn those into finished goods that meet customer specifications. If you're an at-home chef, you might just throw your hands up and order a pizza. But if you're a manufacturer, you might find yourself in need of an advanced planning and scheduling (APS) solution. The question is: what, exactly, is APS, and how can it help manufacturers to better manage their resources?
Challenges in Effective Production Planning
In a nutshell, APS is a system for aligning resource usage and production plans with emerging demand and other constraints. This tends to be a digital process—one that thrives on open information and agile planning workflows. This might not sound like the most impactful thing in the world, but when you consider the challenges that modern manufacturers have to contend with on a daily basis, it becomes easy to understand how such a thing could add value. Because many manufacturing plants produce a range of products that all “compete” for resources—including raw materials, time, machine-hours, and person-hours—a planner’s ability to manually track these resources is going to become more limited as they grow more numerous. Sure, at a pin factory with a fairly simple daily throughput a single person could manage demand with a hand-written ledger, but at a large manufacturing plant it can be difficult even to answer a question like, “can we fulfill this order with the parts we have on hand?”
Of course, the risk of letting this kind of ambiguity persist can be significant. Imagine that you receive two large customer orders on the same day. In the morning, you allocate a certain amount of time and resources to meeting the first—then, later on, you wind up allocating some of those same resources to the new order that’s just come in: by the time you realize that you don’t actually have the resources to dedicate that you thought you did, it will be too late. You’ll be stuck rushing to allocate resources and produce the product on an expedited timeline, which probably means using expedited freight to get the product to the customer and potentially reshuffling existing production plans so significantly that it takes days to sort things out. Without a digital planning infrastructure, these kinds of disruptions become commonplace.
Given the complexities of modern manufacturing, it’s easy to see how a situation like the one we described above warrants some new paradigm for effective resource management and demand planning. This is where APS comes in. By giving planners a complete view of every machine, person, process, and other resource at their disposal, plus an accounting of the costs associated with each of these things, APS helps to create a planning environment in which the right resources can be applied to the right orders at the right time. Instead of using spreadsheets and guesswork to try and arrive at the optimal use of your plant any given week or month, you’re able to match your throughput more closely to real demand by analyzing various planning possibilities across multiple touchpoints.
Like we said above, this is by nature a digital process. Thus, in the case of the two large, competing orders that we considered in the section above, you would suddenly be in a position to clearly visualize the ways in which the orders did and didn’t play nicely with existing plans. You might see that you needed to reshuffle a few existing people to make sure they were able to perform their functions in the optimal way, or that you needed to expedite the next order of raw materials from your supplier. Further, you’d be able to make sure that extra shipping costs involved in the expedited delivery would be offset by the ROI of getting the particular product out on time.
The level of visibility that APS can bring to any given planning flow can be a critical tool for reducing disruptions and breaking down silos—but even that doesn’t represent the limit of its potential. In point of fact, it’s the combination of APS with advanced analytics that really adds value in a new way.
Picture this: you’re trying to plan out production for the week based on the new orders that have come in. Rather than looking at your available resources and picking the schedule that looks optimal, you utilize a digital twin (i.e. a digital representation of your factory designed to run simulations) to test out a few different possibilities. Using advanced predictive analytics, you’re able to pick out the best plan from among all the possibilities. Later, you’re able to train those some predictive algorithms on your demand forecasts in order to gain a better estimate of what future demand levels for particular products are likely to be. In this way, you might be likely to anticipate a big demand spike in advance, meaning that you could stockpile some raw materials without having to pay for expedited shipping. Thus, the whole supply chain begins to move a lot more smoothly while encountering many fewer disruptions—all thanks to advanced planning and scheduling.