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Function pillar Practitioner-led · production AI · ships in weeks

AI demand forecasting,
inventory and supplier risk — shipped in weeks, not quarters.

Mid-market manufacturers, distributors and fleet operators run on forecasts that are too coarse, signals that arrive too late, and supplier risk visibility that's spreadsheet-deep. AI moves the needle on every one of those — when the data work and the eval work happen first. We do both.

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1 Day 1 2 Day 2 DISCOVERY & MAPPING 3 Day 3 4 Day 4 ANALYSIS & SCORING 5 Day 5 DELIVERY 5 Days 6 Deliverables $5K Flat Fee

What ships

Three places AI moves supply-chain numbers.

Each one is a candidate for a Discovery sprint and a 2–4 week first-production deployment via the Fractional team.

AI demand forecasting

Replace flat-line / seasonal-naive baselines with models that read promotions, weather, supplier-lead-time variability, and rolling category trends. Forecast accuracy improvements are use-case specific and require a measured baseline first — we set the baseline as part of the Discovery sprint.

Inventory & replenishment

Multi-echelon inventory optimization, automated replenishment triggers, slow-mover detection. AI on top of an ERP that already exists — not a forklift replacement of the system of record. We embed; we don't rip-and-replace.

Supplier risk monitoring

Automated lead-time variance scoring, supplier-news monitoring (financial distress, regulatory action, geopolitical exposure), and tier-2/tier-3 dependency mapping. Surfaces tomorrow's stockout before it shows up in next month's review.

How AI demand forecasting actually works

Data, model, eval — in that order.

The reason most AI demand-forecasting pilots fail isn't the model. It's the baseline that was never set, the data that was never cleaned, the eval methodology that was never agreed before training. We do all three in the first two weeks.

What we ship

What's in a first-production AI forecasting deployment.

Not a research paper. A working forecasting workflow that runs every week and writes its outputs into the system your planners already use.

Baseline measured against current forecast

MAPE, MASE, bias — by category, by lead-time bucket. Before any model goes near production.

Data plumbing wired before the model

Sales history, promotions calendar, lead-time variance, returns. Cleaned, joined, version-pinned.

Eval methodology signed off in writing

What constitutes a win, what's a regression, what triggers a rollback. Agreed before training starts.

Human-in-the-loop for top SKUs

AI proposes, planner overrides, system records the override. Trust earned, not assumed.

Production monitoring on day one

Forecast drift, data drift, intervention rate. Weekly digest to the planning lead.

Owned, documented, exitable

Code in your repo. Models versioned. Run-book your team can execute without us.

Where it fits

Three buyer profiles we ship for.

If your operation looks like one of these, the Discovery sprint pays for itself in the first week of planning calls.

Mid-market manufacturers

Multi-SKU, multi-line production planning where forecast error eats margin. AP / supplier-risk / demand all live in the same conversation. See [AI consulting for mid-market manufacturers](/posts/ai-consulting-for-mid-market-manufacturers).

Distributors with high SKU velocity

Tens of thousands of SKUs, replenishment decisions are mostly automated already, but the forecasts driving them are flat-line. AI reads what the rules can't.

Fleet operators with idle-time exposure

Vehicle-utilization variance, driver scheduling, route optimization — measurable margin in better dispatch decisions. See industry credential below.

Industry credential

Logistics & fleet — sector experience.

<strong>Luxembourg fleet operator</strong> — fleet management system, improved fleet performance, reduced idle time. Delivered by our parent group Ascendix. <strong>Work type: fleet-management software, not AI.</strong> We use this as <em>sector-experience evidence</em> — we have shipped operational software for fleet ops at scale — not as an AI case study. The first AdvantageWorks-native fleet AI case lands later this quarter.

Common questions

About AI in supply chain.

What planning leads, COOs and CFOs ask before they pick us.

We embed; we don't rip-and-replace. AI sits on top of the system of record and writes its outputs into the workflow your planners already use. We've integrated with SAP, Oracle, NetSuite, MS Dynamics, and several mid-market WMS / TMS stacks via our parent group's 13-year delivery practice.
Use-case specific. The honest answer is we set a measured baseline against your current forecast in the first two weeks of the Discovery sprint, then commit to a target only after we've seen the data. Anyone quoting a number without that baseline is selling, not forecasting.
Yes — and we often do, as the first production step. Spreadsheet → AI-augmented spreadsheet → model-backed forecasting service is the typical path. We don't force a platform migration before the AI work has earned its keep.
A blend: news + filings + supplier-disclosed lead-time data + your own historical receipts. We wire the data first, score the suppliers second, surface the alerts third. The model is the smallest piece of the work.
Two weeks to first run, four weeks to a planner-overrideable production workflow, six to twelve weeks to measured forecast-accuracy uplift against the baseline. We publish the run-book so your team owns the cycle by the end of the engagement.

Get an AI demand-forecasting roadmap in one week.

$5,000 Discovery sprint — we measure your current forecast baseline, identify the 5–7 highest-ROI cases across forecasting, inventory and supplier risk, and hand you a costed implementation roadmap. Service-quality commitment: if we don't identify at least 3 actionable opportunities, you don't pay.

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