Things like Industry 4.0 and Logistics 4.0 are often surrounded by lofty rhetoric, but at the end of the day they’re really about optimization. How can you best optimize your transport routes and tours? How can you maximize the value of your logistics network? How can you achieve the ideal partnerships with colleagues up and down the value chain to keep the entire process running smoothly from end to end? When we think about new technological paradigms in those terms, things often become a lot clearer.
When it comes to Logistics 4.0 in particular, you’re trying to optimize for things that have historically been too complicated to manage well by hand. Things like traffic, weather, labor concerns, trade legislations, and more make it difficult to consistently choose the right route for getting goods from Point A to Point B. As a result, new advances in technology have real potential for augmenting the power of human scheduling and sequencing activities—if you know how to implement and deploy those technologies. When it comes to AI in particular, this knowledge can be somewhat of a stumbling block for logistics providers.
The Challenges of Logistics 4.0 Deployment
All of the above raises the question: how should companies go about attaining Logistics 4.0 capabilities, and how important is AI for developing those capabilities? To answer this question, we have to think about the challenges that attend Logistics 4.0 deployments more generally:
- Identifying IT gaps, data silos, and planning silos within your organization. This can be tough at organizations that are operating with limited end-to-end visibility.
- Increasing digital maturity through technology deployments and operational changes. To make this happen, you need to choose the right technology—but you also need to get buy-in from the right stakeholders to create better alignment and connectivity between touchpoints.
- Managing data and analytics. Logistics 4.0 technologies like IoT devices and analytics software are fundamentally designed to capture as much data as possible as quickly as possible, and turn that data into insights that can be acted on, sometimes even automatically.
- Increasing supply chain integration. By connecting with customers and suppliers, you can improve the quality of your insights, but this too requires buy-in and careful IT considerations.
Essentially, adopting Logistics 4.0 is no mean feat—it requires a clear vision of the future of your IT ecosystem, plus a willingness to be flexible as new technological use cases become apparent. At the early stages, there’s not much that AI can do for you. After all, if your AI algorithms can’t access the digital data they need, how are they supposed to run analyses? But as you digitize, analytics technologies need to be implemented alongside the technologies that are gathering the data in the first place. In other words, once you need to get insights out of your data, AI and related technologies become crucially important.
Constraint Programming and Metaheuristics in the Supply Chain
Okay, AI may be important for turning digitization into a value-added proposition, but how does it actually do that in practice? For starters, one of the most widespread use cases for AI in logistics planning is constraint programming. Simply put, this is a generic method of modeling complex problems, such that you can automatically generate plans for getting from State A to State B and then discard the plans that don’t meet your needs. So, let’s say that you have a series of shipments that need to be sent to a particular area, but you have a number of restrictions about which trucks and containers can be used for which shipments, which cross docks are accessible when, etc. AI-powered constraint programming could model all of the potential options for your specifications, and then help you choose the best one for the task at hand.
Conversely, let’s say your goal is to optimize your container-loading strategies in order to maximize your use of space. Here, AI can power metaheuristics processes designed to find workable solutions. In situations like these, their might not be a perfect solution (since varied sizes and shapes of shipments will make it virtually impossible to achieve complete space optimization), meaning that a higher level process like metaheuristics might offer a better alternative to constraint programming. This same technique can be applied to larger fleet routing options when you’re not working with as many constraints. In this way, you can turn digital data points about your existing transport network into a clear vision of which shipments should be transported on which vehicles via which routes.
The Future of Logistics
Considering that AI is crucial for powering the kinds of analytics flows we described above, its potential value for logistics planners should be obvious: fewer disruptions, more efficient network management, and smarter route planning. But does this really qualify as a revolution in logistics management? We’d argue that it does. Why? Because AI changes not just the outcome but the nature of the planning process. By moving away from human planners poring over spreadsheets, you can drive towards connected planning interfaces that promote collaboration and flexibility. The more you reduce the need for human intervention in analytics processes, the more planners can devote their brainpower to more strategic activities aimed at larger-scale business transformation.
In this way, AI gives you the power to transition towards a truly Logistics 4.0-enabled supply chain. With a radically reduced quantity of disruptions, you can begin to get leaner and more efficient than ever—utilizing strategies like production into the truck and others in order to reduce lead times and speed up deliveries. Throughout all of these processes, the presence of AI in the value stream will give you more and more chances to reduce the need for human intervention, ultimately driving towards the kinds of autonomous processes that will come to define Industry 4.0 and Logistics 4.0 alike. As these kinds of technology-driven processes become more common up and downstream—leading to new and improved integration opportunities—previously impossible degrees of flexibility in rerouting shipments and responding to disruptions will become commonplace. In this way, AI is poised to be one of the key technologies ringing in the era of Logistics 4.0