§ Manufacturing · Growing Practice

Averaging last year's demand was costing them $19M a year.

Consigned inventory placement on Monte Carlo simulation. Demand forecasting on Microsoft Fabric. For multi-site manufacturers and distributors where the constraint is data assembly and placement, not model accuracy.

Book a 20-minute discovery callSee client results
$19M
Inventory loss identified
$4M
Returned in 6 months
20 hrs/wk
Returned to planners
13x
Forecast cadence
§ Who this is for

Multi-site manufacturers and distributors where placement and cadence are the constraints.

You are usually one of these four before you show up on a discovery call.

VP of Supply Chain

Managing consigned or field-held inventory across many locations. Watching averaged demand leave capital stranded in the wrong SKUs at the wrong sites.

COO / Head of Planning

Running quarterly forecasts by hand across seasonal product lines. Planners spending twenty hours a week being a data pipeline instead of judging demand.

CFO / Finance Leader

Sitting on a stock-and-bill operation where a large share of revenue depends on placement accuracy. Wanting the leak sized and the fix funded.

Head of Data or IT Modernization

Consolidating ERP, supplier, and regional feeds onto Microsoft Fabric. Wanting the plan pressure-tested before build starts.

§ Problems we solve

The sentences that arrive first.

Every manufacturing engagement starts with one of these lines. The resolution starts on the right.

No. 01
“Half our locations run short while the other half sits on stock nobody will ever use.”
Supply Chain Leader, US Medical Device Manufacturer
What we do about it

Consolidate consumption, billing, transfer, and location records. Monte Carlo and supply chain simulations produce SKU-level and location-level placement models. Colorado pilot, then national rollout. $19M identified, $4M returned to profit in six months.

No. 02
“By the time the forecast is finished it describes a quarter that already happened. We are not planning. We are reporting, late.”
The conversation we keep having with planning teams
What we do about it

Bottleneck analysis first, then daily data automation, then Microsoft Fabric consolidation, then best-fit models per product line, then a GenAI natural-language layer over the forecast tables. Quarterly to weekly cadence, twenty hours a week returned to planners.

No. 03
“You are about to fund a data platform. Does anyone know if it works?”
The conversation we keep having with sponsors
What we do about it

Fixed-scope Data Assessment and Roadmap. Four two-hour sessions, seven phases across technology, process, and people. One written assessment and roadmap document, not a slide review, handed straight to whoever builds the thing.

§ Client work

Two anchor case studies. Real numbers.

Client names anonymized. Every metric is from a live engagement.

US Orthopedic Bracing and Mobility Device Manufacturer

Averaging last year's demand was costing them $19M a year.

“We know roughly what each site uses, so we stock to the average. Then half our locations run short while the other half sits on stock nobody will ever use.”

Supply Chain Leader, US Medical Device Manufacturer
$19M
Annual loss identified
$4M
Returned to profit in 6 months
20%
Of revenue re-optimized
Read the full case study
The challenge

A US orthopedic bracing and mobility device manufacturer distributed consigned inventory across a national hospital network. Every location had the right average. Almost none of them had the right stock. Half the locations ran short while the other half sat on stock nobody would ever use. The affected department carried 20% of total revenue.

What we delivered

DATA4AI consolidated consumption, billing, transfer, and location records into a single view, then ran statistical testing across seasonality, location, SKU value, and demand variability. Monte Carlo methods and supply chain simulations produced SKU-level, location-level placement models. We piloted the model in Colorado, validated the outcomes, and rolled it out across the national US network.

Outcomes
  • $19M annual loss identified in the stock-and-bill operation
  • $4M returned to profit within six months of rollout
  • 20% of total revenue affected, now placed against modelled demand
  • Colorado pilot to national rollout inside two quarters
Global Outdoor Equipment Manufacturer

Quarterly forecasting was not a strategy. It was a constraint.

“By the time the forecast is finished it describes a quarter that already happened. We are not planning. We are reporting, late.”

The conversation we keep having with planning teams
20 hrs/wk
Returned to planners
13x
Forecast cadence
7 months
End to end
Read the full case study
The challenge

A global outdoor equipment manufacturer forecasted six seasonal product lines by hand in Excel, quarterly, from a dozen sources. ERP, supplier, and regional inputs had no common structure. Cadence was set by spreadsheet time. A critical input arrived as a third-party quarterly file. Planners were spending twenty hours a week being a data pipeline.

What we delivered

Phase one, three months: bottleneck analysis, daily data automation into a database, and reverse-engineering the third-party supplier logic so it could run monthly in-house. Twenty hours a week came back before any modelling shipped. Phase two, four months: Microsoft Fabric consolidation, model selection across Prophet, supply chain models, and machine learning with full feature engineering, best-fit per product line. A generative AI natural language layer over the forecast and dimension tables let planners self-serve.

Outcomes
  • 20 hours per week returned to the planning team
  • Forecast cadence moved from quarterly to weekly, a 13x increase
  • Third-party supplier dependency removed, cadence moved from quarterly to monthly
  • Seven months, first pipeline to consolidated Microsoft Fabric platform
§ Fit

Where this works, and where it does not.

Managing expectations before the discovery call, not after.

Where this works
  • Manufacturers and distributors running consigned or field-held inventory across many locations
  • Multi-region, multi-SKU operations with transaction-level consumption history
  • Seasonal-demand product lines forecasted across regions with manual data assembly
  • Organizations with an existing BI or warehouse estate and a funded modernization program
  • Sponsors who want the plan pressure-tested before build starts
Where it does not fit
  • Single-site operations
  • Networks with no location or SKU-level consumption records
  • Environments with a single clean automated source already in place, where the constraint is model accuracy rather than assembly
  • Greenfield environments with no estate to assess
  • Programs mid-build past architecture lock

We would rather say this now than three weeks into an engagement.

§ Free interactive tool

Size your recoverable losses in two minutes.

Five inputs, directional dollar output across chargebacks, shrinkage, and dead stock. Grounded in the benchmarks from the medical device and outdoor equipment engagements referenced on this page. Nothing is stored on our servers.

Run the calculator
Free · 2 min · no email
§ 09 · Questions

Frequently asked, answered plainly.

The questions that come up most often before a first call.

Distributor chargebacks leak 3 to 8 percent of revenue in medical devices when consumption records, billing records, and contract pricing live in separate systems. We consolidate the four sources into one reconciled platform so invalid claims are flagged pre-issue. Pre-issue catch rate typically moves from under 10 percent to over 60 percent within two quarters. See our full post on how to eliminate distributor chargebacks in consigned medical device inventory.

Shrinkage in consigned networks runs 2 to 4 times owned-inventory rates. Dead stock is the mirror image of a stock-out and is caused by the same mistake: stocking to the average. Our five-signal assessment quantifies the recoverable pool from consumption, billing, and transfer records, then Monte Carlo placement modeling rebalances stock per location. In one engagement this identified $19M in annual loss and returned $4M to profit in six months.

Consumption, billing, transfer, and location-level records for inventory work. ERP, supplier, and regional demand inputs for forecasting. If you have transaction-level history at SKU or location grain, that is usually enough to model against.

SAP, Oracle, Microsoft Dynamics, NetSuite, and mid-market ERPs where consumption and supplier records exist. Delivery is Microsoft Fabric-heavy for the consolidated analytics layer, with Prophet, supply chain models, and ML for forecasting depending on best fit per product line.

Data Assessment: four two-hour sessions with a written roadmap. Inventory placement: Colorado-style regional pilot, then national rollout, with impact typically inside six months. Forecasting automation: three-month phase one that returns operator hours before any modelling, four-month phase two for full Fabric consolidation and GenAI layer.

Yes. Small, senior teams based in the US and Canada, supported by a dedicated India offshore delivery team. Executive-level trust with CEO, VP, and owner-partner buyers, senior expertise at competitive cost, and speed scaled up or down as your program moves.

Those are our named anchor cases. The delivery model, the assessment engagement, and the Microsoft Fabric analytics stack apply across discrete, process, and CPG manufacturing wherever the constraint is data assembly, placement, or forecast cadence.

A fixed-scope Data Assessment and Roadmap: four two-hour sessions with your team, one written assessment and roadmap document. Scheduled immediately after signing. Best used when a platform investment is already funded and you want the plan pressure-tested before build starts.

§ 10 · Get in touch

Let’s talk about the second pilot.

Working on a healthcare AI, manufacturing analytics, EHR integration, inventory placement, or demand forecasting project? Book a 20-minute discovery call. We will tell you honestly if we are the right fit.

Book a 20-minute discovery call[email protected]
Response
Within one business day
Delivery
US, Canada, Australia, India
Focus
Healthcare and manufacturing