As the era of Industry 4.0 approaches in earnest, production managers will soon have access to more data and information than ever before. Internet of things (IoT) sensors and RFID chips throughout the production chain will offer real-time monitoring for your planned production programs, just as robust software integration will help you to better understand what’s happening at various other touchpoints on the supply chain. This is exciting, but it can also be a bit daunting. After all, what exactly are you supposed to do with all of that data?
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.
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.
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.
Imagine for a second that your factory is essentially a black box. Materials go in, and finished products come out, but what happens in between is fundamentally mysterious. What challenges would this present from an advanced planning and scheduling perspective? Sure, in this environment you can get a small sense of the correlation between raw material volumes and finished product volumes—you might even be able to gain a sense of which raw materials loosely correspond to which products. But surely there’s a lot of information you’d really like to have: how do different products differ in resource usage? What are the most common causes of delays and disruptions? How can you more effectively align your capacity with emerging demand levels?
In his seminal work of economic theory, “The Wealth of Nations,” Adam Smith famously uses a pin factory as his example to illustrate a number of basic concepts in what was then modern capitalism. Today, the production of something as simple as a pin can essentially be a global affair. In all likelihood, your production facility needs to receive shipments of raw material from elsewhere in the world via a complex set of routes and distribution points. The factory itself may be part of a larger, international organization with diffuse planning processes taking place in parallel all across the world. And the finished product, once it’s been produced, might be sent anywhere in the world—after all, people all of all nationalities and backgrounds sometimes need pins.
The best laid plans of mice and men often go awry—and nowhere is that more true in the worlds of manufacturing and supply chain management. Sometimes it seems like even the most visible and adaptable supply stream is always one disruption away from chaos. For production planners in particular, you’re constantly battling the risk that new, unexpected orders will come in and you won’t know how to slot them into your existing flows, or that a machine on your production floor will break down and bring your whole operation grinding to a halt. To some extent, occurrences like these are just a fact of life. But that doesn’t mean planners can’t work to prevent them, just as it doesn’t mean that planners can’t work to gain more value from the processes that are already working smoothly.
Let’s say you have to schedule a medium-sized meeting with some of your coworkers. If you’re a traditionalist who likes to do this kind of scheduling by hand, you’ll first need to brainstorm a list of which people (i.e. which creative and organizational resources) will need to be in attendance. Then, you’ll have to pick a time that works for you, and check with each person on the list to see if that time also works with their schedules. In the extremely likely event that the time does not work for everyone, you’ll need a master list of everyone’s availabilities so that you can find a time slot that works for everyone. Or, if not everyone, then at least the largest possible number of vital attendees.
If there’s one thing today’s planners and managers wish they had to ensure their planning and production strategies, it would be a crystal ball. A magical ability to glimpse into the future in order to cut the complexity and uncertainty of modern manufacturing and provide a path of stability and certainty in a variant-rich value stream. While a crystal ball is obviously an impossibility, planners and managers do have a critical tool to help predict future planning and production needs while at the same time managing inventory levels and job allocation strategies for maximum efficiency and productivity.