Autonomous routing targets perishable freight waste and delays

By Marcus Bennett

USDA estimates 30% to 40% of the food supply is wasted, while 66% of supply chain decision-makers say their operations only sometimes kept pace with demand or struggled to do so. The pairing sets a blunt test for routing technology: protect product value, not just transport efficiency.

A cold-chain survey cited in the analysis also found that 72% of respondents said logistics providers did not meet all their needs. For food and beverage shippers, that service gap can surface as improvised loads, idle trailers and rushed reroutes, each of which consumes planner time and raises spoilage exposure.

The figures come without a disclosed sample size, respondent geography, commodity mix or measured cost savings. They indicate operating strain, but they do not quantify the return from autonomous routing.

George Ninikas, senior vice president of sales and accounts, supply chain planning, Americas, at ORTEC, frames the technology as a managed operating loop rather than an automated handoff. “Autonomy is a practical response to the variability that perishable networks cannot avoid,” he wrote.

What does autonomous routing change?

It connects knowledge, decisions, execution and learning so product can arrive fresh, compliant and on time. AI can read emails, retailer notices and specification sheets, then propose routing rules for human review.

Those rules can capture customer minimum shelf life at receipt, prohibit shared trailers when allergen or temperature-zone restrictions apply, and retain practical knowledge such as which store has a tight dock.

For shippers, the proposed benefit is consistent application across planning cycles and an audit trail showing how handling standards were met. For refrigerated carriers and forwarders, the test is operational: fewer improvised loads, idle trailers and rushed reroutes, with less need to rebuild routes by hand.

Why does a small delay threaten the load?

Shelf life, temperature windows and retailer receiving appointments leave little margin. Refrigeration faults, traffic, weather or long dock waits can change a delivery after departure. Comparing planned routes with live progress can flag a deviation, estimate its effect on product condition and delivery windows, and suggest resequencing stops or redirecting the load to a nearer customer.

A warning before options disappear can separate a managed delay from a write-off. Even modest additional lead time can change the outcome for a temperature-sensitive load, although the analysis offers no measured reduction in spoilage or delay minutes.

How should trade and tariff changes enter the plan?

Seventy-three percent of respondents expect tariffs to hurt their financial performance in 2026, and 92% have already adjusted operations. Their changes include shifting sourcing lanes and volumes, which can invalidate static plans. Because spoiled product cannot be resold, a routing mistake can cost the full value of the load, not merely an added freight premium.

This is the commercial reason to test options before commitment. Planners can compare freshness, cost, service and capacity through parallel scenarios, including stop sequence by remaining shelf life, splitting a load across vehicles or using additional storage.

Is storage capacity the binding constraint?

Forty-seven percent named flexible storage capacity as their greatest need from cold-storage partners. That figure makes storage a stated service gap, although the source provides no warehouse square footage, trailer count, lane tonnage or rate data to establish whether storage, vehicles or transport pricing is the larger constraint.

Planners need to see the trade-off before a delivery fails. A route that adds slightly more miles can protect a full load. Optimization can identify feasible plans, while AI prepares alternatives so people can judge product quality and customer relationships.

Can each delivery improve the next route?

Actual dwell times, receiving delays and temperature excursions reveal how the network behaves under real conditions. Yet they often sit as records rather than planning inputs. Feeding them back into planning makes future routes more realistic and identifies recurring problem locations or delivery windows.

Over time, the data can show which customers or docks hold trucks longer than planned, giving partners a basis for correction. Three in five respondents, or 60%, said data and AI investments had positively affected their supply chains. The analysis also warns that returns vary and depend heavily on execution. A positive association among respondents does not establish causality, savings or performance against a defined baseline.

Where must people remain in control?

The first automation target is work that crowds out judgment: cleaning order data, matching loads to temperature and customer constraints, and rebuilding routes after routine delays. Handling that groundwork first creates a track record before software handles higher-stakes actions such as diverting a load or releasing a delayed shipment.

Food safety and customer commitments require clear boundaries. A system may recommend, act within agreed limits or escalate when product integrity or compliance is uncertain. Users must see why an action occurred.

In Ninikas’s formulation, “The objective is not to remove planners from a perishable network but to give them cleaner information, better options and more time for the exceptions that protect products and customers.”

The foundations remain uneven. A 2025 operations survey found that 42% of respondents identified integration with existing systems as a major challenge, while 37% cited data availability and quality. Autonomous routing therefore depends as much on clean interfaces and usable records as on the routing engine.

Will it reduce waste?

The evidence supports a workflow proposition, not a guaranteed return. AI can standardize rules, compare scenarios, flag deviations and feed actual network performance back into plans. Whether that reduces waste will depend on adoption, integration quality, exception handling and the discipline to keep rules current as demand, trade policy and capacity change.

As those pressures keep shifting, routing that learns from every delivery could reduce spoilage, but the source says progress depends on sustained choices about data, rules and workflow rather than one software purchase.