AI-Ready 3PL Food Logistics: Modern WMS That Bills Right

A lot of food logistics “failures” aren’t caused by bad drivers or a shortage of refrigerated space. They’re caused by something less visible: systems that can’t keep up with constant change—especially when storage rules, handling steps, and value-added services shift order by order.

That’s why the Castellini Group of Companies’ story matters. They’re a mid-market, asset-owning 3PL running cold-chain transportation and distribution across the eastern U.S., with a 200,000-square-foot warehouse outside Cincinnati and their own fleet. Their customers range from refrigerated produce to home-delivery meal kits—exactly the kind of operation where temperature bands, shelf-life, grading, and compliance don’t just add complexity… they multiply it.

Here’s my take: modernizing your WMS is the price of admission. The next step—especially heading into 2026 planning cycles—is making that WMS AI-ready so you can predict workload, prevent spoilage, and price services correctly as conditions change. This post is part of our “AI in Transportation & Logistics” series, and it’s aimed at operators and procurement leaders who want modernization that actually shows up in margin and service levels.

Food logistics breaks when billing can’t match operations

If your WMS can’t describe what actually happened on the warehouse floor, your invoices will be wrong. And in cold chain, “wrong” usually means underbilling.

Castellini ran into the classic mid-market problem: a legacy WMS that couldn’t handle different customer needs across different warehouse zones while also capturing costs dynamically. Produce and meal kits aren’t simple pallet-in/pallet-out businesses. One order might need cross-docking and rapid staging; another needs inspection, rework, labeling, kitting, or special storage. The minute those services change, your cost-to-serve changes too.

When billing can’t keep up, you get:

• Margin leakage (value-added services performed but not charged)
• Invoice disputes (because you can’t prove the work performed)
• Operational shortcuts (teams avoid “non-standard” work because it’s hard to record)
• Customer churn (service failures often start as systems failures)

Snippet-worthy truth: If your WMS can’t bill for complexity, you’ll end up subsidizing it.

Why “bolt-ons” usually disappoint in cold chain

The source article calls out a key detail: bolt-on APIs meant to add automation/optimization—often designed for omnichannel retail—weren’t enough.

That tracks with what I’ve seen. Cold-chain distribution adds layers that general-purpose add-ons don’t model well:

• Temperature compliance and zone moves (ambient → chilled → frozen)
• Shelf-life rules and FEFO logic (first-expired-first-out)
• Lot traceability and recall workflows
• Time-sensitive staging to avoid warm-up events
• Risk amplification: one miss can mean spoilage—or a food safety incident

In other words, you can’t “API your way out” of a WMS that lacks the right operational primitives.

What a modern 3PL WMS needs to do (before you add AI)

A WMS upgrade shouldn’t be treated like a software refresh. In cold chain, it’s a control system.

Castellini chose to rip-and-replace its legacy WMS and selected a SaaS WMS provider focused on complex, regulated environments. Regardless of vendor, the capabilities to look for are consistent—especially for mid-market 3PLs trying to scale without breaking processes.

Capability 1: Orchestrate inventory, labor, and compliance in one model

A cold-chain WMS has to coordinate three things at once:

1. Inventory truth (lot, date, grade, condition, location, temp zone)
2. Labor truth (who did what, when, and how long it took)
3. Compliance truth (holds, inspections, audit trails, SOP adherence)

If these are separate systems, you’ll spend your life reconciling exceptions.

Capability 2: Support “dynamic operations,” not just static workflows

The source highlights frequent order changes and add-on demands. That’s normal now—especially with meal kits and subscription fulfillment. A modern WMS should support:

• Rules-based tasking that adapts to priority changes
• Configurable value-added service workflows
• Real-time exception handling (late inbound, temp excursions, substitutions)
• Customer-specific SOPs without custom code for each account

Capability 3: Capture cost-to-serve as it happens

This is where many modernization projects win or lose.

A practical way to evaluate a WMS: Can it automatically generate billable events from operational events? For example:

• “Case pick” vs. “pallet pick” pricing
• Repack/relabel charges
• Inspection fees
• Special handling for frozen items
• Urgent same-day fulfillment surcharges

If your billing still depends on spreadsheets and after-the-fact time studies, you’re leaving money on the table.

Adding AI: where food logistics gets materially better

Once the WMS foundation is solid, AI becomes useful—not as a buzzword, but as a way to reduce waste and stabilize service.

In the AI in transportation and logistics context, I think of AI as answering three operational questions faster and more accurately than a human team can.

1) “What will break tomorrow?” (predictive risk + spoilage prevention)

Cold chain isn’t forgiving. The highest-value AI applications focus on predicting exceptions before they become claims.

Examples of AI-ready signals:

• Temperature telemetry and dwell time history
• Lane-level delay patterns
• Inbound variability by supplier and commodity
• Congestion patterns by dock door and shift

With that data, AI models can flag:

• Loads likely to miss appointment windows
• Items at risk of expiring before allocation
• SKUs likely to be short-picked due to poor slotting/velocity changes

Operationally, this translates into fewer warm-up events, fewer shorts, and fewer emergency moves.

2) “How many people do we need—by zone and hour?” (labor forecasting)

In Q4 and holiday periods, meal kits and perishable volumes can swing hard. AI forecasting is a better fit than static labor standards because it can incorporate:

• Order mix (case vs each)
• Value-added service mix
• Cutoff times and carrier schedules
• Historical productivity under similar conditions

A strong outcome target here is simple and measurable: lower overtime hours without hurting OTIF (on-time, in-full).

3) “Are we pricing this customer correctly?” (cost-to-serve + procurement discipline)

Procurement and commercial teams often price cold-chain accounts using averages. AI can help segment and price using actual behavior:

• Variability in order patterns
• Frequency of “special requests”
• Returns/holds/inspection rates
• Peak-day demands

That enables tighter contracts and cleaner supplier/customer coordination:

• Minimums and surge pricing that reflect reality
• Service catalogs that reduce ambiguity
• Fewer “free” value-added services slipping through

Another snippet-worthy truth: AI doesn’t fix bad pricing. It exposes it.

A practical modernization roadmap for mid-market 3PLs

If you’re running food logistics and thinking about modernization, here’s a roadmap that avoids the two common traps: buying shiny tools too early, or waiting so long you end up doing a panicked rip-and-replace.

Step 1: Map the 20% of workflows that create 80% of complexity

Start with a short list:

• Cross-dock vs storage moves
• FEFO allocation and shelf-life constraints
• Holds/inspections/rework
• Kitting/meal kit assembly steps
• Customer-specific labeling and documentation

If the WMS can’t model these cleanly, everything else becomes expensive workarounds.

Step 2: Turn operational events into billable events

Make this a hard requirement. Not a “phase 2.”

Define your billable event catalog (what triggers a charge) and test it with real historical orders. When teams see that the system can capture revenue without extra admin work, adoption improves fast.

Step 3: Get your data in shape for AI

AI in logistics fails when data is inconsistent. You don’t need perfection, but you do need discipline.

Minimum viable data readiness checklist:

• Clean SKU master with temp zones, case pack, shelf-life attributes
• Standard location naming and zone logic
• Consistent labor activity codes
• Timestamped event history (receive, move, pick, pack, ship)

Step 4: Pilot 1–2 AI use cases with clear ROI targets

Pick use cases with measurable outcomes within 90 days:

• Labor forecasting by zone (reduce overtime hours)
• Exception prediction (reduce claims, spoilage write-offs)

Avoid starting with “full autonomous planning.” Teams need wins they can trust.

Questions procurement leaders should ask before signing a 3PL + WMS stack

If your job touches supplier selection, contracting, or SRM, here are questions that separate a strong operation from a risky one.

1. How does the 3PL track and prove value-added services performed?
2. Can they produce an audit trail for lot traceability in minutes, not days?
3. What’s their process for handling frequent order changes without manual rework?
4. How do they prevent margin leakage when operations get messy?
5. What data can they share for joint forecasting and replenishment?

The last question is where AI comes in. If the answer is “we’ll email a weekly spreadsheet,” you’re not building an AI-ready food supply chain.

The bigger point: 3PL modernization sets the stage for AI logistics

Castellini’s modernization story is a clean example of the right sequence: fix the execution layer first (WMS that fits cold chain), then scale performance with analytics and AI.

If you’re planning 2026 initiatives, I’d put this on the whiteboard:

• Modern WMS = operational control and accurate billing
• AI = prediction, prevention, and smarter coordination across suppliers, warehouses, and transport

If you want to pressure-test your operation, start with one question: Where are we still relying on humans to “remember” complexity? That’s usually where your next modernization win is hiding.