It’s a story one hears surprisingly frequently: a mid-sized enterprise company adopts a new mega-ERP solution, and they’re almost immediately mired in constant disruptions. Of course, changing out your entire IT infrastructure all at once (instead of piecemeal) is bound to cause some short-term disruption, especially if the process is long overdue—but that doesn’t account for all of the late or missed orders, the expensive IT support requirements, and other issues that we see in these scenarios.

In the past few years, the industrial world has seen an increase in the use of so-called digital twins, i.e. digital representations of physical factories. Maybe you’ve heard about technology that makes use of this concept—maybe you’ve even wondered why and how this concept could theoretically be applied to your own operations. If you have, then you’ve come to the right blog. Today, we’ll give a quick rundown of the top 5 uses for factory simulations, and how those uses can drive value and reduce disruptions for modern manufacturers.

Artificial Intelligence (AI) can refer to a number of things, from machine learning to computer vision, but in general the phrase is used to indicate computer programs that can reason, learn, and problem-solve from data in a way that’s reminiscent of human intelligence. This takes any number of forms, from digital personal assistants like Siri and Alexa to competitive chess playing to autonomous vehicles—each of which involves a slightly different understanding of what AI is and does. For manufacturers, the most pertinent uses of AI are likely going to be the ones that are most heavily focused on gathering insights from large quantities of data—simply because of the sheer amount of information collected and stored by most industrial and supply chain planning platforms. The question still remains, however, of what manufacturers in general and production planners in particular should expect from AI in the coming months and years.  

Whether you're creating a more synergistic relationship with a supplier of raw materials as an auto manufacturer or developing special relationships with retailers to improve the performance of your packaged consumer goods, collaborative supply chain partnerships often feel like the holy grails of the modern value stream. This is with good reason: a strong partnership in which information, risk, and benefits are shared equitably can add real value on both sides of the relationship in the form of reduced costs, smarter forecasting, or any number of other benefits. It's easy to see why people are willing to devote time and mental energy to it. 

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.

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.

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.

Let’s say you’re a homebrewer, and you’ve just finished drafting your recipe for a dry-hopped pale ale that you plan to brew in the coming weeks. If you’re like most people, you go to a homebrew supply site and order your hops, malt, and yeast all at once, plus some clean bottles for your brew to wind up in. This strategy works perfectly well, but as you go, you find that it leaves something to be desired. While your beer is fermenting, you have a bunch of bottles taking up unnecessary space on your floor; and by the time you’re ready to dry-hop (which involves adding more hops during the fermentation period), the ones you bought from the homebrew site are a little stale.

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.