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.

Remember the “paperless office?” Back in the early days of computing for the masses, and particularly after the invention of the public internet, it was all any of the pundits could talk about. They expounded on the wonders of digital this and online that, with the underlying theme being the elimination of physical copies of paperwork. It’s been well over two decades, and I don’t know about you, but my last office had a 30’ run of filing cabinets that certainly weren’t empty. Today, those same pundits are going into great detail about the “disruption” of the automotive industry that electric vehicles (EVs) signal. But is the shift to EVs really going to have that much of an impact? As with so many things, the answer is a resounding “it depends.”

People say that the only constant is change. When they say that, they’re usually not talking about sales and operations planning (S&OP). And yet, what could be more relevant? If you’re an automaker, for instance, your business constantly needs to adapt to changing market conditions, customer expectations, technological realities, and other factors that can have a big impact on the success of your production plans, supply chain, and profits. There are any number of strategies that decision-makers use to try and address these constant internal and external changes, but one of the most commonly talked about (in some circles, anyway) is S&OP.

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.

One of the most common metaphors you hear for forecasting in supply chain management is that it’s like the rear-view mirror in your car: you need to understand what’s happening behind you, but it’s not necessarily enough information to keep you slamming into the car in front of you. As the supply chain has evolved, however, forecasting has evolved along with it. So, for that matter, have cars: in the modern supply chain, forecasting can encompass not just the rear-view mirror, but the back-up camera, and even the smart sensors that alert you when you’re getting too close to another car.

These new processes that move beyond the scope of the rear view mirror use technology to take in additional information, and then spit out new insights for the driver to use—from immediate course-correct notifications to more granular data about when you’re going to hit the curb while parallel parking. In each case, digitization has played a big role in giving you a more comprehensive overview of events that are about to take place. In an industrial context, we might think of these digital enhancements as things like IoT (internet of things) devices and other smart sensors that provide live information to planners. In this way, forecasting becomes more thoroughly integrated into the way that businesses make decisions and optimize their supply chain management. And it’s lucky for us that it does so, because accurate forecasts are becoming more important than ever in the world of supply chain planning.  

The automotive industry is no stranger to technology. It’s also no stranger to the rapid pace of change that’s overtaken global manufacturing in the early 21st-century. And when it comes to planning and organizing your entire automotive supply chain, advanced planning and scheduling (APS) is the key that will unlock increased ROI and decreased lag times. APS represents a sea change from traditional methods that looked at materials and production capacity as separate things, a view that often led to incompatible plans. Adoption rates of APS in the automotive sector are on the rise, paralleling the rise of make-to-order and additive manufacturing; and the increasing complexity of the automotive manufacturing world as a whole. And it’s that last factor that we’re going to focus on today, the increasing complexity of the automotive world and how APS can help. Whether by assisting with inventory leveling or by helping planners better schedule materials deliveries, APS can be a boon at every stage of the automotive manufacturing supply chain.

Let’s says you’re playing chess. Traditionally, a chess player looks at the whole board and comes up with an overarching strategy, which she can then adjust as needed when new conditions (i.e. her opponent's strategies and maneuvers) emerge. For this game, however, you decide to do something different: you have a series of different plans, one for the pawns, one for the bishops, one for the queen, etc. with no obvious connections or interplay between them. As situations arise in which multi-step, cross-functional movements would be helpful, you stay in your lane and stick to the separate plans for each function. At the end of the game, your rooks have performed admirably, and everything went according to plan for your pawns, but you still found yourself in checkmate.

Even manufacturers themselves may sometimes forget how tremendous the global manufacturing sector really is. Manufacturing in the U.S. on its own, for instance, would be in the world’s top 10 economies. Because this sector encompasses so many different businesses with so many different missions and products, it’s easy to prove or disprove almost any prediction. Sure, someone among the incredibly diverse array of global electronics producers is probably using voice activated AI in their plants—just as someone else is probably bucking every emerging trend by continuing to eschew digitization and connectivity. Still, as general trends emerge, it can be helpful to identify and understand them. To that end, here are some predictions for the world of global manufacturing in 2020.

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.

Demand forecasting plays an important role in manufacturing. That fact isn’t changing; what is changing is how it’s done. Whereas in the past, forecasting had an aura of magic about it, relying heavily on the intuition and experience of the planner doing the forecasting, today it’s largely a data-driven practice. Before Industry 4.0, historical data was combined with gut feelings to produce a sort of crystal ball-like prediction of what the future held for the company in terms of demand and buyer behavior, and by extension what the company should focus on producing. Everyone basically crossed their fingers and hoped for the best. Post-Industry 4.0, this has changed dramatically, with a heavy reliance on advanced predictive analytics being fed data from IoT sensors deployed throughout the supply chain.