What to Expect from ARC Forum 2026: AI for Supply Chains

Peak season 2025 reminded a lot of teams of an uncomfortable truth: you can’t spreadsheet your way out of volatility. Carrier capacity shifts in days, demand signals whipsaw by region, and one missed ETA can cascade into labor overtime, detention, and customer churn.

That’s why the theme of the 30th Annual ARC Industry Leadership Forum (Feb 9–12, 2026, Orlando) lands so well: how AI is driving the future of industrial operations and the supply chain. Events are easy to dismiss as “nice networking.” I don’t see this one that way. When a forum pulls industrial ops, logistics, IT, and analytics into the same room, you get something rarer: practical alignment on what AI can do now—and what it still can’t.

If you’re responsible for transportation, warehousing, planning, or end-to-end supply chain performance, here’s the value: AI is finally being judged on operational outcomes (service, cost, resilience, emissions), not demos. This post breaks down where AI is actually paying off, what to listen for at an AI-focused industry forum, and how to turn the conversations into a 90-day execution plan.

AI in logistics is shifting from “tools” to “systems of decision”

AI in supply chain is no longer just an add-on dashboard. The most mature companies are using AI as a decision layer across planning, execution, and continuous improvement.

Here’s the core shift: AI is moving upstream from reporting to recommending—and in some workflows, acting.

In transportation and logistics, that looks like:

• Routing and dispatch optimization that updates when conditions change (weather events, yard congestion, missed pickups)
• Predictive ETAs that become operational triggers (call the consignee, reschedule dock appointments, reallocate inventory)
• Warehouse labor forecasting that converts volume uncertainty into shift-level staffing plans
• Exception management where AI highlights the 2% of shipments that will create 80% of the pain

What tends to separate real outcomes from “AI theater” is integration. If predictions aren’t connected to your TMS/WMS/OMS workflows, they don’t change behavior. You get better charts—and the same late loads.

The myth to drop before 2026 budgeting

Most companies get this wrong: they try to buy “AI” when what they actually need is a decision pipeline—clean-ish data, clear ownership, and defined actions when the model fires.

If you attend an AI-driven supply chain event in early 2026, listen for who can explain:

• What decision is being improved?
• Who takes the action?
• How fast does the system respond?
• What metrics move (and by how much)?

If those answers are fuzzy, the project will be, too.

Where AI is paying off right now (and what it takes)

AI value in transportation and logistics isn’t evenly distributed. Certain use cases are consistently delivering because they sit close to measurable outcomes.

1) Routing and network optimization (when constraints are real)

Answer first: AI improves routing when it incorporates operational constraints, not just map distance.

The strongest routing results come when models account for:

• Time windows, driver hours-of-service, and rest requirements
• Dock appointment availability and yard dwell time
• Drop-trailer utilization vs live load constraints
• Customer-specific service rules (accessorials, delivery preferences)

A practical example I’ve seen work: teams combine predictive travel time with stop-level service time predictions (how long unloading actually takes at each customer). That turns route planning from “best guess” to “probabilistic scheduling,” which reduces failed deliveries and end-of-day route blowups.

What to ask vendors or peers about at ARC Forum 2026:

• Do you re-optimize intraday based on exceptions?
• Do you model service time per location, or assume fixed minutes per stop?
• How do planners override, and what happens after they do?

2) Forecasting that’s actually usable by operations

Answer first: Forecast accuracy matters less than forecast usefulness—how well it drives purchasing, labor, and transportation commitments.

By 2026, more teams are blending:

• POS and e-commerce demand signals
• Promotion calendars
• External signals (port conditions, macro indicators, even localized weather)
• Real-time inventory positions

But here’s the operational catch: forecast outputs must be consumable at the level decisions are made—SKU-location-day for inventory, lane/week for transportation, wave/hour for warehouses.

If the forecast is only reviewed monthly in an S&OP meeting, it won’t prevent next Tuesday’s labor shortage.

3) Warehouse automation + AI: orchestration is the multiplier

Answer first: Robots and automation deliver the biggest lift when AI orchestrates work across humans, AMRs, sortation, and storage systems.

Automation alone can increase throughput, but AI orchestration is what stabilizes the operation when reality hits:

• A trailer arrives early and floods receiving
• A high-priority order drops mid-wave
• Pick paths conflict with replenishment moves

The most effective setups treat the WMS/WES as a “conductor” and AI as the “composer” that predicts bottlenecks, balances zones, and reallocates labor dynamically.

At an AI-focused industrial operations forum, this is where you’ll hear the most concrete stories: throughput gains, reduced travel time, lower overtime, fewer missed cutoffs.

4) Predictive maintenance and asset health for logistics operations

Answer first: Predictive maintenance pays off fastest when downtime has clear operational penalties—missed loads, service failures, rental surges, or safety risk.

In transportation and logistics, relevant assets include:

• Material handling equipment (conveyors, sorters, forklifts)
• Cold chain systems (refrigeration units)
• Yard equipment (hostlers, gates)

The “AI part” is often less glamorous than people expect: anomaly detection on vibration/temperature patterns, then automated work order creation, then parts planning.

It’s not sexy. It’s profitable.

Digital twins and “cognitive analytics”: what to listen for

The RSS source highlights technologies like digital twins, predictive technologies, and cognitive analytics—and those terms can mean anything from rigorous simulation to marketing fog.

Here’s a clean way to evaluate them.

Digital twins: the useful definition

Answer first: A supply chain digital twin is a living model of your network that can test decisions before you execute them.

A serious digital twin typically includes:

• Network structure (plants, DCs, customers, lanes)
• Capacity constraints (dock doors, labor, equipment)
• Service targets and penalties
• Cost structure (transport, handling, inventory)

What makes it valuable is scenario speed. If you can answer “What happens if we shift 15% volume from DC A to DC B next week?” in hours rather than weeks, you get a planning advantage.

At ARC Forum 2026, the best conversations won’t be “we have a digital twin.” They’ll be:

• “We used it to reduce expedite spend by 12%.”
• “We simulated three carrier mixes and improved on-time delivery by 4 points.”

Cognitive analytics: good when it drives exception handling

Answer first: Cognitive analytics is most useful when it turns noisy data into prioritized action.

In practical logistics terms, that means:

• Detecting risk on shipments before a service failure happens
• Explaining why risk is rising (weather, facility delay, carrier trend)
• Recommending what to do next (re-route, swap carrier, split shipment)

If the “insight” ends at “you have risk,” you’re still doing the hard part manually.

A 90-day plan to turn forum learnings into results

Conferences can create momentum—or create a stack of notes you never open again. Here’s a simple way to turn an event focused on AI in industrial operations and supply chain into pipeline.

Step 1: Pick one operational KPI and one workflow

Answer first: Tie AI to a single measurable KPI, then anchor it in a workflow that already exists.

Good KPI options:

• On-time-in-full (OTIF)
• Detention and demurrage
• Expedite spend
• Warehouse overtime hours
• Cost per shipment / cost per line

Pick the workflow where that KPI is “made,” such as:

• Appointment scheduling
• Dispatch and route planning
• Slotting and replenishment planning
• Exception management for late shipments

Step 2: Demand proof of “time to action”

Answer first: The ROI of AI collapses when insights don’t reach the people who can act in time.

Ask for:

• Typical latency from signal → recommendation
• How recommendations surface (inside TMS/WMS, email, control tower queue)
• How actions are logged and measured (did the recommendation help?)

Step 3: Set a pilot boundary that prevents scope creep

Answer first: Successful AI pilots are narrow, fast, and tied to controlled data.

A practical pilot boundary:

• 1 region or 1 DC
• 2–3 lanes or a defined customer segment
• 60–90 days of execution
• Predefined success metrics and a rollback plan

Step 4: Build the “operating model” alongside the model

Answer first: AI fails more often from ownership gaps than from math.

Define:

• Who owns model performance monitoring
• Who approves changes to business rules
• Who can override recommendations
• What triggers retraining (season changes, new carriers, network changes)

If you leave this undefined, the system will degrade quietly until nobody trusts it.

Why this event matters for transportation and logistics leaders

The ARC Industry Leadership Forum is positioned around AI, digitalization, and resilience—exactly where transportation and logistics leadership is being tested. 2026 planning cycles will reward teams that can explain, in plain language, how AI improves service and cost without creating operational fragility.

If you’re going, I’d focus your time on two things:

1. Peer stories with numbers attached (service level changes, cost reductions, throughput improvements)
2. Architecture conversations (how data moves, how decisions get executed, how humans stay in control)

That combination is where lead-generation value comes from, too. The best vendors and partners will be the ones who can map their capabilities to your workflow and show exactly how they’ll measure impact.

Before February arrives, ask yourself one forward-looking question: If your largest customer demanded weekly proof of AI-driven service improvements by mid-2026, could you produce it—credibly, with operational metrics—not slideware?