Distribution
Where is margin leaking?
Margin usually erodes in small increments -- discounts, freight, returns -- spread across many transactions: easy to miss line by line, easy to see once customer, product and territory are compared side by side. FactSmith surfaces exactly which combination is dragging margin down.
Why this matters
Discount creep and inconsistent pricing hide inside aggregate reports. Nobody notices until a monthly finance review, by which point it has already cost real rand.
What data is needed
Sales, cost, discount and pricing facts, at customer, product and territory grain.
The evaluation copy runs against PostgreSQL. SQL Server is on the adapter path for a customer warehouse; that path is not what the package proves end to end today.
How the analysis works
Realised margin per combination is compared against an expected or historical margin, and gaps are ranked by rand impact, not just percentage -- so a small-percentage leak on a big account still surfaces.
What can go wrong
A deliberately low-margin account -- a loss-leader agreed on purpose -- can look identical to leakage in the numbers alone. That distinction needs a human call, which is what vouching a fact is for.
Example
A distributor asks which customer/product combinations fell furthest below their usual margin last month. Ask returns a ranked list with the rand gap, not just the percentage.
How FactSmith approaches it
Ask is what is available now. Easy Data -- turning messy sources into that dimensional foundation -- is in early development.
FactSmith calls this the Fact Layer: a data integration (ETL) step, a knowledge base, and a business ontology, working together underneath the answer. On screen it stays simple -- ask a question, get a trusted answer -- with the layer-by-layer detail available to anyone who wants to see how the answer was built.
Permissions are enforced beneath the AI, not by it -- see Trust and security.
Related questions
Declining customers · Slow-moving inventory · For distributors · How it works