Most professionals in the food logistics space would say that it’s all about setting up the right systems — ERP as a base, SCP for planning, then WMS and TMS for execution.
However, it can be argued that it’s not primarily about better systems anymore.
Take, for example, the replenishment process connecting all these systems — it’s not primarily a software problem. With reliable systems in place, getting the right product on the right truck at economically sensible full-truckload quantities while protecting freshness still takes huge manual effort.
Your SCP gives the exact amounts you need to send — for each of your products, to each of your DCs, on each of the days (maybe even with the truckload builder optimization done). WMS shows where each pallet resides in the warehouse. TMS shows trucking capacity and cost. However, the plan is only good until the operator finds that some pallets are on quality hold, a few cases here and there are committed to customer pickup, and the truck is lost to a mechanical breakdown.
Each system is still rock-solid. The day’s replenishment plan is dead in the water, without urgent resuscitation by the person in charge. At best, that leaves the logistics team working overtime. Typically, also with suboptimal shipments driving up costs and damaging service.
Here is where your operations hinge on the hidden operating system that is not software but people.
For decades, this human coordination layer has been the only way to process semi-structured work. Experienced planning and logistics teams extract data, produce and compare options, discover and investigate exceptions, call somebody, update a spreadsheet, rerun something, negotiate, and finally come up with decisions that should work. Assuming you have great people, fully up to speed — the show will go on.
The food industry has normalized coordination burden as simply the cost of running logistics. Long hours, overtime — that’s what it takes. Dropping smaller issues to focus on bigger ones — that’s good prioritization. And when experienced people leave, hell breaks loose, so we’ll operate in emergency mode until we find and train replacements.
Recent advances in AI finally gives a way to challenge this normalization. Until now, much of the coordination workload resisted automation because the inputs were too loosely structured, with exceptions too varied. AI agents, powered by models capable of handling these inputs, change that equation. An important distinction: however tempting it is to automate this work entirely, that’s still not a viable call. Experienced people’s judgment, ability to negotiate and deal with novel issues are what’s needed to preserve.
AI is not best at making decisions in this area, but it can do an excellent job preparing them. Think of how much time the experienced planning/logistics team spends on simple but tedious work that doesn’t need their qualifications: collecting data, putting together alternatives, iterating through options, checking feasibility.
Imagine, instead of the transportation planner finding, after an hour of work, that the shipment plan isn’t compatible with the warehouse capacity outlook and restarting, the planner starts with the results of the cross-checks, exceptions to address and a few potential solutions for each. This is exactly how to design processes inside well-built TMS, WMS and SCP systems but not between them.
And this is one of AI’s sweet spots in food logistics. Not autonomous logistics management, but an amplifier for the logistics team, allowing them to reduce residual issues and unnecessary costs while getting the day’s work done on time.
“Attention: can’t ship all planned pallets to Reno due to weight limitations on the available route. Recommended options: (1) push 3 pallets of ABC by 2 days [impact: shelf life down to 60%, SS down to 30%]; (2) ship 3 pallets of ABC with LTL carrier [impact: cost +$350].”
The agent can discover that three pallets won’t fit. It can also calculate the alternatives. At the same time, it may not know that next week’s endcap promotion makes one customer strategically more important, or that the less-than-truckload carrier has been less-than-reliable lately, or that two of those three pallets are for a certain grocery retailer, and are particularly sensitive after last month’s shortage.
Start your own experiment to prove that better coordination between systems and functions can unlock value. Make it a no-regret exercise where you spend minimal resources upfront, and either prove value or learn where the approach falls short in the business:
· Find your “human bridge” situations, where a frequent workflow requires someone to manually reconcile information from SCP, WMS and TMS.
· Choose an exception-rich but bounded workflow, not an “optimize our entire network” one. Pick something as simple as possible, with measurable results.
· Start with the agent observing, not acting. Feed it the same information a person uses and compare its preparation and recommendations against actual decisions.
· Measure coordination and outcomes, not AI sophistication. Think of time spent, touches, time to decide, residual issues (freshness, service and cost impact).
· Start with decision preparation. Consider delegating decisions only when the agent has proven its reliability and only in situations unlikely to require an experienced operator to handle an unusual exception.
· Once you prove that it works, get it properly automated and industrialized. At this time, you will have a rock-solid business case that the tech team will have every reason to say “yes” to.
This gives a low-risk way to pilot, then improve, some of the stubbornly manual workflows in food logistics. Not through a major investment in a new WMS or TMS. Not by trying to replace your people. But by relieving the team of the coordination burden between the systems and functions.
Reducing the burden around their core work lets logistics professionals spend more time where AI remains weakest. While AI agents are unusually good at assembling context and working with messy inputs, they are less trustworthy when context is missing, problems are novel, or accountability is needed for consequential decisions. To handle those situations, preserve and develop the human expertise and make sure that the updated operating model leaves people enough opportunity and practice to keep their judgment sharp.
