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.
Stok.ly’s AI-driven BI is designed for inventory-led businesses across retail, wholesale, warehousing and manufacturing.
What “AI BI” means in operations ·
How insights are quantified ·
Write-backs: turning insight into action ·
The 12 high-impact AI BI cards ·
Where it connects in Stok.ly ·
Explore related pages ·
FAQs
“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.
Reference: AI functionality ·
Related: AI inventory planning ·
Multi-location inventory ·
WMS
AI BI for operations is not a generic dashboard. It is an operational decision layer that:
(1) identifies the highest-impact leakage, (2) quantifies profit/cash impact using your transaction truth,
and (3) recommends and applies the actions that fix it.
This page focuses on “decision cards”. Execution is delivered via Stok.ly’s inventory-first ERP:
Inventory-Centric ERP.
The difference between BI and operational AI is write-back. Insights are useful; system-level changes are transformational.
Examples of write-back endpoints in an inventory-led ERP include:
Operational context: WMS ·
Pick, pack and despatch ·
Multi-channel ecommerce
Each card follows the same format: Insight Quantification Actions Write-backs.
These are designed to map directly to how inventory-led leaders and operators actually work.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
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).
Stockouts, transfers and pick-face replenishment connect to WMS and
AI inventory planning.
Supplier performance connects to purchasing and replenishment controls in Inventory-Centric ERP.
If your team spends time arguing about “what happened” instead of fixing it, AI BI can compress the loop:
detect → quantify → act → write back. Book a demo to review the insights most relevant to your operating model.
Dashboards show metrics. AI BI 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.
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.
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.
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.