Supply Chain Visibility
That Arrives In Time To Use
Inbound, work in progress and finished goods in one model — built by an engineer who rebuilt General Motors' supply chain ETL, where the pipeline was the bottleneck and no dashboard would have fixed it.
The Challenge
Inbound sits in the ERP, warehouse state in the WMS, freight with the carrier portal, and forecasts in a spreadsheet. Nobody can answer a question that spans two of them without a person assembling it first, which means the answer arrives after the decision.
Our Approach
We rebuild the pipelines first. Supply chain analytics fails far more often on data movement than on modelling, and a control tower fed by an overnight batch that sometimes fails is worse than no control tower, because people trust it.
What You Get
One model spanning inbound, WIP and outbound, refreshed fast enough to act on. Exceptions surfaced rather than buried. Forecast error located rather than averaged away.
What We Build
Built for
Supply Chain
On-time-in-full by supplier and lane, with the exceptions surfaced while a decision is still possible.
Inbound materials, work in progress and finished goods in one model instead of three systems.
Turns, ageing and days of cover by location, with imbalance flagged while transfer is still worth doing.
Forecast versus actual at SKU level, showing where error concentrates and what it costs in safety stock.
Not just average lead time — the variance, which is what actually drives your buffer.
Freight cost per unit by lane and mode, connecting operational routing choices to the P&L.
Proven Results
Measured Outcomes
From Real Engagements
Context
Why Supply Chain Analytics
Is Different
Supply chain data has a property that makes it harder than it looks: the useful version of almost every metric is a distribution, not an average. Average lead time tells you very little. Lead time variance tells you how much safety stock you are carrying and why, and most reporting shows the first and not the second.
The second difficulty is that the data crosses organisational boundaries. Procurement owns supplier data, operations owns WIP, logistics owns freight, and finance owns the cost of all three. Each has its own definitions, and a control tower that papers over the differences produces a number nobody in any of those functions recognises.
The third is latency. A stockout you learn about on Friday from Wednesday's data is a historical fact. This is why we rebuild pipelines before building dashboards — the constraint is nearly always how fast data moves, not how it is presented.
How It Runs
A Clear Process.
No Surprises.
FAQ
Supply Chain Questions
We Get Asked
For a single-source view, no. For anything spanning ERP, WMS and carrier data, effectively yes — joining three systems inside a reporting tool is slow, fragile and hard to maintain. We would usually build a light warehouse layer rather than a full enterprise platform, sized to the question you are answering.
Both, in that order. Reporting forecast error at SKU level nearly always shows the error concentrating in a small subset of items, which is actionable on its own and usually the fastest win. It is also the dataset any predictive work depends on, so it is the right first step regardless.
No. Most supplier performance analytics is built from your own receipt and purchase order data rather than anything the supplier provides. On-time-in-full, lead time variance and quality rejection rates are all measurable from your side of the transaction.
It depends on the decision, and the honest answer is usually less real-time than people specify. Inventory rebalancing is a daily decision. Production scheduling can be hourly. Supplier scorecards are monthly. Building everything to the tightest requirement is the most common way these projects become more expensive than they needed to be.