Most inventory teams already have reports. What they lack is a system that translates operational truth into profit and cash impact, recommends specific actions, and can write those actions back into day-to-day execution: replenishment settings, purchasing, transfers, pricing and supplier controls.
This is what AI Pro does. AI Business Intelligence is the commercial-intelligence layer of AI Pro, built for inventory-led businesses across retail, wholesale, warehousing and manufacturing, working alongside Operational AI (forecasting and replenishment) and Executive AI (board-level risk and growth reporting) to cover the full decision stack — from a single SKU’s reorder point to the actions a CEO reviews each month.
“Business intelligence” is only useful if it changes decisions. The Stok.ly approach is: quantify the impact in £, recommend actions, and write back improvements into operational controls—so the business improves without spreadsheet-led planning.
The 12 high-impact AI BI cards
Card 1 — Stockout Losses (Revenue + GM lost)
Stockout cost is calculated as out-of-stock days × expected daily demand × net price, with gross margin impact added using the SKU’s margin estimate.
Insight: “You lost £X Net Revenue and £Y Gross Margin due to stockouts in the last 12 months.”
Quantification: OOS days × expected daily demand × net price; GM impact via SKU margin estimate.
Actions: update reorder point, safety stock, transfer rules; expedite PO triggers.
Write-backs: adjust SKU×Location reorder point; create POs; create stock transfers; set minimum inventory warnings.
Card 2 — Supplier Lead Time Inaccuracy
True supplier lead time is measured from actual receipt dates, not quoted ones, and expressed as P50, P90 and P95.
Insight: “Supplier lead times are inaccurate and drove £X lost margin via stockouts and expedite costs.”
Quantification: correlate late receipts vs OOS windows and missed demand; show P50/P90/P95 vs master data.
Actions: set planning lead time to P90/P95 for critical SKUs; optionally keep quoted lead time for reporting.
Write-backs: update supplier/SKU lead time fields; update forecasting inputs; regenerate replenishment plan.
Card 3 — Supplier Fill Rate Leakage (short-ships causing OOS)
Fill-rate leakage is calculated as (units ordered − units received) × downstream demand rate × gross margin.
Insight: “Supplier short-ships are causing £X in stockout-related lost GM.”
Quantification: (units ordered − units received) × downstream demand rate × GM estimate.
Actions: change reorder quantities, diversify supply, earlier ordering; revise supplier scorecard flags.
Write-backs: adjust MOQ / reorder quantity rules per SKU×Supplier; create additional POs; set supplier risk flags.
Card 4 — Dead Stock Cash Recovery (working capital trapped)
Dead stock value is the landed cost of inventory with fewer than a set number of units sold in the last 90 days.
Insight: “£X of inventory has <N units sold in 90 days; £Y is fully dead.”
Quantification: Value of Goods On Hand (incl. landed costs), ageing bands, demand rate.
Actions: markdowns, bundles, channel shift, transfers to higher velocity locations, stop replenishment.
Write-backs: adjust prices (markdown schedule); create transfers; disable reorder; add “clearance” tags.
Card 5 — GMROII Outliers (best/worst use of inventory investment)
GMROII (Gross Margin Return on Inventory Investment) is calculated as Gross Margin ÷ Average Inventory Cost, measured over a 7, 30 or 90-day window. A low GMROII means a SKU ties up cash relative to the margin it returns; a high GMROII means it earns its shelf space.
Insight: “These 20 SKUs are destroying GMROII; these 20 are exceptional but understocked.”
Quantification: GMROII = Gross Margin / Avg Inventory Cost (7/30/90d).
Actions: reallocate cash from low GMROII to high GMROII; fix stock policy for winners.
Write-backs: adjust reorder points / minimum pick-face; create transfers; recommend PO prioritisation.
Card 6 — Discounting and Price Integrity (margin leakage)
Discount margin leakage is the gap between gross and net revenue, tracked as a trend over time and broken down by channel and SKU.
Insight: “Discounting increased by X% and cost £Y Gross Margin, concentrated in A/B channels/SKUs.”
Quantification: net revenue vs gross revenue bridge; discount rate trend; item-level elasticity proxy (optional).
Actions: reduce discount depth, remove unprofitable promos, raise price on inelastic winners.
Write-backs: update price lists; adjust promo rules; enforce minimum margin floors.
Card 7 — Returns/Refunds Margin Destroyer (quality + content + ops)
Returns cost reverses both revenue and cost of goods sold on the day of refund, then attributes the total by SKU and by supplier.
Insight: “Returns cost £X net revenue and £Y GM; top drivers are these SKUs/suppliers.”
Quantification: refunds reverse revenue+COGS on refund day; return rate by SKU/supplier/channel.
Actions: supplier quality interventions, product content fixes (PIM), warehouse handling checks.
Write-backs: flag suppliers/SKUs; adjust reorder for high-return SKUs; update product content fields; adjust QC steps (if supported).
Card 8 — Landed Cost Drift (COGS rising silently)
Landed cost drift is tracked at the receipt level — freight, duties and surcharges included — by supplier and by shipping lane.
Insight: “Landed cost per unit increased X% for Supplier A / Lane B, reducing GM by £Y.”
Quantification: receipt-level landed cost components; WAC drift; GM erosion by SKU.
Actions: renegotiate freight/duties, shift suppliers, change order cadence to reduce surcharges.
Write-backs: update landed cost allocation rules; update supplier cost tables; adjust sourcing rules.
Card 9 — Forecast Bias & Volatility (where forecasting is wrong)
Forecast bias compares forecast to actual demand per SKU, measuring both the direction of the error and its volatility.
Insight: “Forecast error is causing overstock (cash trap) and understock (lost margin).”
Quantification: forecast vs actual; bias; demand standard deviation; P50/P90/P95 demand tails.
Actions: change forecasting model parameters; change service level targets; segment SKUs (stable vs volatile).
Write-backs: update forecasting settings per segment; adjust reorder policies; revise buffers.
Card 10 — Inter-location Imbalance (stock exists, but in the wrong place)
Imbalance is measured as simultaneous stockout value in one location and excess stock cover of the same SKU in another, weighed against the cost of moving it.
Insight: “You had £X stockouts in Location A while Location B carried £Y excess of the same SKUs.”
Quantification: simultaneous OOS + excess cover; transfer ROI (GM preserved vs transfer cost proxy).
Actions: implement transfer thresholds; automatic rebalancing cadence.
Write-backs: create suggested transfers; set transfer automation rules.
Card 11 — Pick-face Min/Max Errors (warehouse productivity + availability)
Pick-face minimum and maximum levels are calculated from demand rate and replenishment frequency.
Insight: “Pick-face mins are too low (causing replenishment churn) or too high (wasting space).”
Quantification: pick-face stockouts; replenishment events; cycle-time tail (P95).
Actions: set minimum pick-face stock using demand rate and replenishment frequency.
Write-backs: update minimum pick-face stock; update minimum inventory warnings; recommend slotting changes.
Card 12 — Executive Action Queue (ranked by £ impact)
Each recommended action is scored by £ upside, a confidence level, and expected time-to-impact, then ranked into a single priority list.
Insight: “Here are the 10 actions that will deliver the most profit/cash impact in the next 30–90 days.”
Quantification: each action has £ upside, confidence score, and time-to-impact.
Actions: one-click approve; staged rollout; monitor and rollback.
Write-backs: whatever endpoints are relevant (POs, transfers, pricing, reorder points, supplier lead times, warnings).
FAQs
How is AI Pro different from dashboards?
Dashboards show metrics. AI Pro identifies the biggest drivers, quantifies impact in £, recommends actions, and can write those actions back into operational controls like reorder points, POs, transfers and pricing.
Can AI Pro really quantify stockout losses?
Yes, by combining out-of-stock windows with expected demand (historic sell-through or forecast) and net prices, then estimating margin impact using SKU margin estimates.
What does “write-back” mean in practice?
It means changes are applied in the system: new reorder points, new minimum warnings, generated POs or transfers, updated supplier lead times, or updated price lists—so insight becomes execution.
Is this only relevant for retailers?
No. The same leakage patterns show up in wholesale, distribution and manufacturing: stockouts, lead time variability, fill rate risk, dead stock and forecast bias. The difference is how actions are executed in the operating model.
Does this replace the Power BI reporting we already use?
No — it complements it. AI Business Intelligence’s quantified insights (the £ figures, the ranked actions) can feed into the Power BI reporting layer already built into Stok.ly, rather than requiring a separate tool or a second login. You get the automated detection and write-back from AI Pro, viewed through the same reporting layer you’re already using.
What is GMROII, and how is it calculated?
GMROII stands for Gross Margin Return on Inventory Investment. It’s calculated as Gross Margin ÷ Average Inventory Cost, typically measured over a 7, 30 or 90-day window. It tells you how hard a SKU’s inventory investment is working: a low GMROII means cash is tied up for comparatively little margin return, while a high GMROII means the stock is earning well relative to what it costs to hold. Stok.ly’s AI calculates GMROII automatically for every SKU, every night, and flags the outliers at both ends.
Do I need to set anything up separately to get these insights?
No. AI Business Intelligence runs on data your Stok.ly account already captures — sales, stock levels, receipts, returns and supplier performance. There’s no separate import or integration step; it’s part of AI Pro as an upgrade to your existing Stok.ly ERP, not a different system sitting alongside it.
Next step
If you’re ready to take your financial analysis to the next level, Stok.ly AI Pro does it automatically: detect → quantify → act → write back, every night, across every SKU and location. Book a demo to see which of the 12 cards would surface the most value for your operation first.