AI-Based Load Planning Systems

Making “AI” work in the field I enjoy posting on my ideas for sequential decision analytics, but boy do I love it when it actually works in the field. Below are the results of the planning system by Optimal Dynamics running at two truckload carriers. The tools include optimal bidding, load acceptance, and real-time dispatch. Within weeks of implementation, we are getting bumps of 23 percent and 13 percent in revenue per driver!   These are not simulations – these are the benefits in the field. These are numbers that can change an industry.   It starts with using the right analytical technologies, and the planning systems are all built around the universal framework that I have been posting about.    But there is much more to this success than just analytics: data engineering, communications, performance monitoring, user interface, working with dispatchers, business process change, … “AI” is not the magic that we read about in the press – there is a lot of work that goes into making it work in the field.

Modern consumers, spoiled by Amazon's super-fast delivery, dictate new delivery standards. I'm not going to complain about them, since I also like getting my orders fast (and who doesn't?). #logistics sector is trying to fit new standards, but the way companies make that happen is kinda messy behind the scenes. They’re sending out trucks half full, doing lots of small deliveries instead of fewer efficient ones. And it’s all adding up to way more pollution and higher costs. Like, they’re burning a ton of fuel just so someone can get socks the next morning. But companies can’t just slow down and make customers wait (they'll simply go elsewhere, if you’re not a monopoly). The best way for now is to implement AI in your ops. AI can help avoid wasteful stuff like empty vans driving around or three deliveries going to the same street at different times (I’m exaggerating a little, of course, but I’m sure someone has run into situations like that.) I’ll go over the main solutions out there: 1) smart route optimization. AI can analyze real-time data like traffic, weather, low-emission zones, and road restrictions to plan routes that avoid delays and wasted fuel. 2) dynamic load matching. AI can fill half-empty trucks smarter by grouping parcels with similar routes or time windows. It can even delay some non-urgent packages by an hour or two if it means sending one full van instead of two half-empty ones. (or, on the other hand, if you've got a lot to fit into one truck, you can try using 3D-loading) 3) real-time re-routing. When vans break down/ customers cancel/ traffic builds up, AI can reroute deliveries, reassign drivers, and make sure you still get your order without extra waste or delays. But it only works if the company actually has clean and connected data, like what’s in the truck, where it’s going, what time it has to arrive. Otherwise, AI’s just guessing. What are your thoughts on such approaches? btw, at Crunch we have one case where we delivered real-time GPS tracking of vehicles, automatic generation of optimal routes, and inventory management. You can read the full version here: https://lnkd.in/eU44Tnaz. #ailogistics #supplychainautomation

⚡ Forecast → Plan → Chaos. Traditional supply chains run sequentially—forecast drives supply plan, which drives manufacturing, which drives transport. The result? ❌ Bottlenecks at DCs ❌ Half-empty trucks ❌ Service misses & spot freight Enter the Agentic Supply Chain. Instead of batch planning, intelligent agents perceive, decide, and act across the network in real time. With ProvisionAI’s LevelLoad agent: ✅ DC congestion dropped by shifting loads earlier/later ✅ Millions saved by reducing spot freight ✅ Higher first-tender acceptance ✅ Less volatility for planners & carriers This isn’t theory—it’s live today, implemented in under 9 months at a global CPG. 📖 Read the full story: Agentic AI Supply Chain https://lnkd.in/eaC_ZWwq 👉 Are you still planning sequentially—or orchestrating with agents?