We’re going to be honest with you: there won’t be any big surprises in this list of the benefits of AI in logistics. Prognosticators have been touting the potential virtues of new analytics technologies for many years, and by and large those virtues align with exactly the sorts of process and planning improvements that logistics providers and supply chain managers look for whenever they’re evaluating new technology: increasing efficiency, reducing delays, and ultimately optimizing costs while improving operations. The crucial difference, then, is how different technologies make these benefits possible.
So, when it comes to AI, it can be useful to look beyond the benefits and try to get a sense of what’s going on under the hood. After all, you don’t want to be totally reliant on a black-box that doesn’t give you any insight into how you’re supposed to get the benefits you’re looking for. Instead, you want a sense of what tactical and strategic changes these technologies are going to power within your existing value chain. Thus, without further ado, 5 benefits of AI integration—and how AI can actually bring them about.
1. Improved Transportation Forecasting
One of the technologies that we find most exciting right now is transportation forecasting—i.e. using AI and machine learning algorithms to predict your own future shipping capacity needs, likely price fluctuations in the logistics market, and future logistics capacity available by lane, mode, and carrier. Essentially, by collecting huge caches of market data from every possible touchpoint on the value chain (a feat that becomes easier and easier to accomplish the more you focus on supply chain integration), you can get proactive about reserving capacity at a reasonable price. In traditional transportation planning workflows, you only have a window of a few days after an order is created to find the right transportation options. With an AI powered solution, you could reserve some capacity before customer orders have even come in, meaning that you radically decrease your chances of being shut out by prospective carriers or locked into expensive premium freight prices.
2. Reduced Bottlenecks and Delays in Production and Logistics
Now, assuming you have the level of integration we described above, you can use a similar method aimed at inbound logistics in order to reduce the possibility of production delays. Here, your predictive algorithms can identify potential parts shortfalls within your suppliers’ chains, thereby enabling you to take proactive countermeasures to ensure that you don’t experience outages on your end. By the same token, you predict incoming customer demand more effectively in order to reduce the likelihood that your production capacity can’t handle emerging customer orders. Thus, S&OP solutions that are able to incorporate AI can more effectively match demand to capacity (since their demand estimates are much more accurate), resulting in an upstream increase in accuracy for inbound logistics, meaning fewer production delays. On the outbound side, the same technology applied to your TMS can reduce shipping delays in the same way—enabling you to get proactive by letting you know what’s coming.
3. Increased Space Efficiency
Of course, AI comprises not just one thing, but a whole host of related technologies—machine learning, constraint programming, metaheuristics, path search, clustering, etc.—each one with different applications. Machine learning, in which algorithms are “trained” on large datasets in order to better predict future outcomes, help to power the sorts of AI benefits that we described in the two bullets above. But it’s far from the only way that this technology can add value. Metaheuristics, for instance, can help you create and prune decision trees in search spaces where there are too many possibilities for constraint programming algorithms to deal with. This is useful for cases where there might be virtually infinite options, and you simply need to choose the one that will be good enough. Thus, it’s ideal for something like 3D container loading. Depending on exactly what you’re loading into a given shipping container or truck, there are likely to be an almost unlimited number of ways to load them, and your AI simply needs to choose something that’s efficient enough within a reasonable runtime. In this way, you can decrease the number of containers you need or even the number of individual shipments required to fulfil orders—thereby reducing costs.
4. Decreased Transport Network Waste
While metaheuristics can help you use space more efficiently, clustering is your best option when it comes to the organization of an entire transport network. Why? Because it’s the best technique for grouping different elements together based on their similarities. In this way, you can analyze your network of hubs, crossdocks, warehouses, and other network elements in order to uncover areas of waste or redundancy. In the same way, you can identify gaps where it might be useful to have network elements. Without any sort of analytics-driven visualization of your entire network, it would virtually impossible to achieve the optimal balance and placement elements to ensure quick shipping turnarounds at minimum cost. With AI, it’s as simple as modeling your network and letting the algorithms point you towards potential new efficiencies and process improvements.
5. Anticipatory Logistics
Any of the benefits that we’ve listed above can be extremely enticing on their own. But taken together, they don’t just paint a picture of cost management and waste reduction. On the contrary—taken as a whole, these various approaches to the integration of AI into the logistics chain represent the foundation for a radical transformation that’s already underway in the digital era. This is what’s sometimes referred to as Logistics 4.0 (i.e. shipping and transportation planning’s counterpart to Industry 4.0), but a more descriptive term is anticipatory logistics.
Anticipatory logistics involves preemptively acting on orders before the customers have even placed them. This might be as simple as adjusting your inventory levels in advance of a forecasted spike in demand, or as complex as freight routes that are adjusted in real-time to maintain a distribution of goods throughout warehouses that lines up with demand that hasn’t even emerged yet. Either way, it represents a new paradigm in logistics—and a huge competitive advantage for early adopters in terms of shipping costs and turnaround times. And the only way to make this possible is through AI adoption.
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