AI for insights and analytics
A site reports forecast accuracy of 82 percent while thirty SKUs are forecast eighteen percent high every month and hold most of the site's aged stock. AI analytics reads the forecast, order, receipt and inventory files, and answers the supply chain director: where is bias hiding, which suppliers are deteriorating, and what is causing misses.
These are the questions supply chain teams ask. Each one maps to a measure our tools compute from your files. The AI model chooses the measure and explains the result; the arithmetic is done by our code, and every total is checked.
| The question | The measure behind it | What comes back |
|---|---|---|
| Where is the forecast biased, not just inaccurate? | Forecast bias | Signed error by SKU and site over the trailing months, with the items consistently high or low. |
| Which suppliers are deteriorating against their own record? | Supplier deterioration watch | OTIF and lead time against each supplier's own baseline, with the ones sliding. |
| What stock is ageing, and which forecasts caused it? | Inventory ageing | Stock over 180 days by SKU and site, joined to the forecast bias on the same items. |
| Where are we exposed to a single source? | Single-source exposure | Spend and volume on items with one qualified supplier, by category and site. |
| Why did we miss customer OTIF? | Cause of service misses | Misses under the customer's rule by cause: stock, transport, order error, with the count and value. |
| What are expedites costing, and why? | Expedite cost by cause | Expedite spend by cause and site, against the forecast bias and supplier misses behind it. |
Every figure below was computed by a tool from the rows and checked before it was shown. The lines sum; the percentages match; each line opens to the rows that make it.
Where is aged stock coming from?
Stock aged over 180 days totals $2.2m across 3 sites, and most of it sits on items that were forecast high for months. Northgate plant holds $1.1m, 50% of it.
| Northgate plant | $1.1m | 50% of the total |
| Southport depot | $700k | 32% of the total |
| Eastfield plant | $400k | 18% of the total |
| The 3 lines sum to | $2.2m | 0 unexplained |
Which Northgate items?
3 item groups account for $640k of the $1.1m aged at Northgate plant, 58% of it. Each was forecast an average of 18 percent high every month at this site and nowhere else.
| Fastener range | $380k | 30 SKUs; bias +18% for 9 months |
| Seal kits | $190k | bias +14%; two suppliers over-delivering |
| Coatings | $70k | bias +11%; shelf-life risk |
| These 3 are | $640k | 58% of Northgate plant |
Who fixes the forecast?
The site planner, this cycle, on the thirty items with the bias figures in hand. Purchasing pauses replenishment on the aged items, and the director reviews bias by site monthly beside accuracy.
| Northgate planner | Correct the forecast on the 30 biased SKUs | This cycle |
| Purchasing | Pause orders on items with more than 180 days of cover | This week |
| Supply chain director | Report forecast bias beside accuracy by site | Monthly |
Each measure has one formula, one source and one meaning. They are computed per site and supplier and in total, and every one carries an identity that must hold before it is shown.
| Measure | Formula | From | What it tells you |
|---|---|---|---|
| Supplier OTIF against own baseline | Receipts on time and in full ÷ receipts due, per supplier and site, against the supplier's trailing 12 months | Purchase order and receipt files | Suppliers that have changed |
| Lead time variability | Interquartile range, and 90th percentile, of actual lead time, per supplier and site | Purchase order and receipt dates | The number behind the safety stock |
| Customer OTIF, customer's rule | Orders on time and in full by the customer's definition ÷ orders due | Order and shipment files; customer terms | The score the customer sees |
| Forecast bias | Mean of (forecast − actual) ÷ actual, by SKU and site | Forecast snapshots; actuals | Plans that are always high or low in one place |
| Inventory ageing | Stock value with no movement in 90, 180, 365 days, by site | Inventory file; movement history | Cash in stock that is not moving |
| Single-source exposure | Spend, and parts, with one qualified supplier ÷ total, by site | Supplier master; purchase orders | Where one failure stops a line |
| Days of cover against target | Stock ÷ average daily demand, by SKU class, against target | Inventory; demand | Too much and too little, at once |
| Expedite cost by cause | Premium freight and expedite fees, by cause: supplier late, forecast miss, order change | Freight ledger; reason codes | What unreliability costs |
| Supplier deterioration watch | Suppliers worse than own baseline by more than the margin for two consecutive months, ranked by spend | Receipts; quality file | The call list for procurement |
| Cause of service misses | Customer OTIF misses by cause: stock-out, pick, carrier, order data, customer date | Shipment and warehouse files | Who owns each fix |
Each measure is worked through, with the export it comes from and what to drop, in KPIs for supply chain teams.
From your question and the measures declared for supply chain, the model picks the one that answers it, and the period and comparison the question implies.
Deterministic code reads the rows, computes the measure, and checks the identities below. The same question on the same data gives the same answer, every time.
The AI model writes the sentence around the result, naming the site or account behind it. It states no figure that is not in the result, and every figure links to its rows.
| Receipts | Receipts due = on time and in full + late only + short only + both |
| Customer OTIF | Orders = OTIF + late only + short only + both |
| Inventory | Opening stock + receipts − issues ± adjustments = closing stock; aged bands sum to total |
| Expedite | Expedite cost by cause sums to the freight ledger premium lines |
The exports supply chain teams already produce. Column names are mapped once and the mapping is reused. A file is the way in; scheduled delivery and connections to your systems come with the plan, and every source is listed here.
An answer is a list with an owner and a cadence, or it is a chart nobody works.
| Measures | Owner | Cadence |
|---|---|---|
| Supplier deterioration watch; supplier OTIF | Procurement and supplier quality | Monthly |
| Customer OTIF; cause of misses; expedite cost | Operations director; site heads | Weekly to monthly |
| Forecast bias; days of cover; ageing | Demand and supply planning | Monthly |
| Lead time variability; single-source exposure | Chief operating officer | Quarterly |
Accuracy averages the size of the errors; bias keeps their sign. A site can be 82 percent accurate with thirty items forecast high every month and others low, because the errors cancel. Bias by SKU finds the thirty; accuracy never will. The tool reports both.
From its own history in the receipt file: OTIF and lead time over the trailing period. Deterioration is a fall against that, so a supplier that was always at 80 percent is not flagged while one that fell from 96 to 88 is. Fixed thresholds miss the second and punish the first.
The customer's, when the contract file carries it, because that is the one the customer measures you on. The internal rule is shown beside it. Misses are then listed by cause from the order and shipment files, so the meeting is about causes rather than the definition.
Forecast and actual demand by SKU and site, purchase orders and receipts, inventory by age, and shipments against customer orders. The supplier master with qualified sources adds single-source exposure. Each is an ERP export you already run.
Forecast bias · OTIF (on time in full) · Inventory ageing · Lead time variability · Single-source risk · Weeks of cover
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Bring a few thousand rows. The data map opens next, every column mapped once, and the first question is answered in minutes. Free, in your browser, no account.