If you’ve looked into artificial intelligence applications in the supply chain space, you know that the umbrella concept of AI comprises a number of distinct technologies and techniques, each one with a different set of potential applications. Constraint programming, for instance, involves creating and then pruning possible decision trees in order to find optimal methods for getting something from one state (e.g. an unproduced mass of raw goods) to another (a finished automobile); machine learning and time series algorithms, on the other hand, use varying numbers of data points in order to forecast potential future outcomes.
This is before we even get into metaheuristics (which offers optimizations in areas where the search space is too large for constraint programming) and things like path search and clustering (both of which are specialized techniques for planning out potential paths and grouping different elements together, respectively). Though these technologies are all related, each one has the potential to power slightly different areas of added value in supply chain and manufacturing contexts. The better handle planners and schedulers have on these different techniques and their use cases, the more effectively they’ll be able to leverage this new and emerging technology into a competitive advantage.
1. Aiding Human Planners
Before we talk about the specific ins and outs of how you might leverage this different techniques within your production plant, yard, or transport network, it’s important to hit one overarching point: at the end of the day, the point of any and all AI is to assist human planners and give them the tools to do their jobs more effectively. This runs counter to the popular representation of AI as a replacement for human work and know-how. And, of course, implementation matters: If you’re relying on black-box AI to spit out recommendations without any interpretable reasoning, human planners may feel like they’ve been supplanted. On the other hand, if you have a user-centric or explainable AI that offers users a glimpse into how it arrived at its recommendations, as a planner you’re more likely to find that your abilities are being augmented by a tool that can simply analyze information faster than you can. In this way, AI becomes a complement to your existing expertise.
2. Smarter Sequencing and Scheduling
Now, let’s say you’re a production planner in an auto plant, and you need to optimize the sequence and schedule for a run of car batteries (which have to be charged as part of the production run). This involves a fair amount of complexity on its face, but by implementing constraint programming workflows that take in all of your production parameters to generate optimized sequences and schedules you can remove that complexity and get to the correct answer that much more quickly. In general, the more constraints and complexities you have to deal with as a planner (which materials need to be where, when; which order specific tasks need to occur in, etc.), the harder it is to achieve the right answer on the back of an envelope or in an Excel spreadsheet. With constraint programming, all the necessary analysis happens automatically.
3. 3D Container Loading
While sequencing and scheduling problems are appropriate tasks for constraint programming-based AI, less linear processes like container loading are better suited to metaheuristics. Why? Because there are virtually infinite ways in which the space inside a large container could be divided up, and to tackle this problem effectively you need a method for generating partial searches that concentrate on a good-enough plan, rather than a truly optimal plan that might take too much time to calculate. In this way, you can arrive at container loading plans that waste a minimum of space without requiring extremely lengthy calculations. Like the example above, this is something a human planner unaided would essentially have to eyeball, potentially resulting in miscalculations—which could in turn lead to either inefficient space usage (meaning that you would use more containers than necessary) or insufficient space (meaning that you’d be scrambling to find carrying capacity for the goods that didn’t fit in the allotted containers). Either way, the AI-powered workflow helps you to stave off disruptions.
4. Transport Network Planning
One technique in the world of AI that we haven’t mentioned yet is clustering—i.e. the process of grouping different elements together in the optimal way. This is a technique that’s often used in—but distinct from—both data mining and machine learning. By identifying the similarities between different hubs in your transport network, for instance, you can begin to figure out what the best organization for those hubs is going forward. Because clustering can also be a useful technique for uncovering the best spots to start and stop transport logistics routes, it can also be valuable in showing the potential areas in your existing transport network where there might be gaps or redundancies—thereby giving you a clear sense of how best to organize your transport network for efficient movement of parts to and from your warehouses, hubs, etc. and either on to the final customer or into the hands of your logistics partners.
5. Autonomous Vehicle Routing
Okay, no article about the potential uses for AI in manufacturing would be complete without quickly mentioning autonomous vehicles (which, in a manufacturing context, might be useful for moving parts around the yard or warehouse) and the other autonomous processes that go hand in hand with Industry 4.0 deployments. Here, path search-based AI can help power algorithms that give robots and autonomous vehicles the best path through whatever space they have to traverse. Unlike the other examples we listed out above—all of which are things that human planners have been doing themselves up till now—this represents an area where AI is making completely new processes feasible. With this kind of AI-powered technology operationalized in your value chain, you can do more than reap the benefits of faster and more accurate analysis—you can revolutionize the way that you administer the entire value chain, driving increasingly connected and autonomous processes designed to eliminate waste and improve responsiveness. In this way, AI can and will help to ring in the future of manufacturing and supply chain management.