Inventory planning is where operational scale breaks: forecasting, deciding what to buy or make, and moving stock to the right locations.
Stok.ly includes built-in AI designed to reduce manual workload and improve availability across
multi-location retail, wholesale, warehousing and manufacturing—using forecasting,
replenishment automation, stock balancing and transfers under real constraints.
What AI inventory planning is ·
Demand forecasting (sell-through, velocity, profit) ·
Constraints: seasonality, lead times and budgets ·
Forecasting for range integrity ·
AI replenishment: stores, warehouses and reorder policies ·
AI-adjusted min/max thresholds ·
Stock balancing and automated transfers ·
AI planning for manufacturing (FG + RM) ·
Lean manufacturing: schedule runs to meet demand ·
How Stok.ly fits ·
Overview video ·
Explore related pages ·
FAQs
The value of AI in inventory is not “prediction” in isolation—it is turning demand signals into operational actions:
what to buy, what to make, what to transfer, and how to set policies per location—while respecting constraints.
Reference: Stok.ly AI functionality ·
Foundations: Multi-location inventory ·
WMS ·
Manufacturing
AI inventory planning uses demand signals (sales history, sell-through, seasonality, promotions and business goals) to forecast demand and propose actions:
purchase orders, manufacturing orders, replenishment to stores, warehouse pick-face replenishment, and inter-location transfers.
Different businesses forecast for different objectives. Stok.ly AI is designed to support forecasting and planning approaches that reflect how inventory-led teams actually operate.
Optimise availability by learning sell-through patterns over a chosen date range or season, then driving replenishment to maintain service levels without overstocking.
Focus on increasing the speed at which inventory turns—keeping winning lines in stock and reducing dead stock through better allocation and replenishment decisions.
Incorporate commercial goals by prioritising availability and investment toward higher-margin or higher-contribution products, within the realities of demand and constraints.
Related: AI demand forecasting
Planning without constraints is theatre. Real inventory planning must incorporate the constraints that determine whether a decision is feasible and economically sensible.
Many retailers and brands plan not just by SKU, but by range. Range integrity means keeping the right sizes/variants available, avoiding “Swiss cheese ranges”
that reduce conversion, and planning replenishment so the customer experience stays consistent.
Forecast at SKU/variant level (size, colour, pack) so replenishment protects the range that drives conversion.
Keep ranges intact where demand exists—by store, warehouse or channel—rather than pooling decisions at a global level that creates local stockouts.
Reduce end-of-season overhang by adapting replenishment policies as demand drops, while maintaining availability where it still converts.
AI becomes valuable when it drives the actions your team otherwise does manually: propose POs, suggest transfers, and keep store and warehouse replenishment disciplined.
Related: Retail ERP
Related: WMS · Pick, pack and despatch
Static min/max rules fail as soon as demand shifts. AI can adjust thresholds based on real sell-through rates for a specific date range or season, keeping policies aligned to reality.
Raise or lower min/max levels as seasonal demand changes, preventing overbuying at the tail and stockouts at the peak.
Adjust thresholds per store/warehouse based on local demand, rather than applying one global policy that creates local failures.
Keep humans in control: apply approval workflows and guardrails so AI suggestions follow business rules and constraints.
The fastest way to increase availability without buying more stock is to move the stock you already own to where demand exists.
Stock balancing uses sell-through and forecasted demand to propose transfers that reduce both stockouts and overstocks.
See: Multi-location inventory · Stock It
For manufacturers and brands, planning must coordinate finished goods demand with raw material availability. Forecasting should drive what to build and what to buy,
with clear visibility into constraints such as lead times, MOQs and production capacity.
Forecast demand by SKU and location, then convert it into a build plan that aligns to service levels and range integrity.
Translate the build plan into raw material requirements (BOM explosion) and automate purchase/transfer actions to ensure material readiness.
Plan and move raw materials to onsite or offsite manufacturing locations, keeping an audit trail and avoiding production delays.
Related: Manufacturing ERP · Manufacturing
Lean manufacturing is fundamentally a planning problem: produce what is needed, when it is needed, with minimal inventory holding.
AI planning supports this by tightening the loop between demand forecasting, material readiness, and production scheduling.
Stok.ly is an inventory-centric cloud ERP with built-in AI designed to support planning and execution across retail, wholesale, warehousing and manufacturing.
It connects forecasting and replenishment decisions to operational workflows—PO creation, transfer execution, warehouse picking, and manufacturing planning—so teams can scale without spreadsheet-led planning.
If forecasting and replenishment is consuming senior time—or store/warehouse availability is inconsistent—validate an AI-driven inventory planning approach.
Book a demo to review your operating model, constraints and automation opportunities.
A short overview of how Stok.ly supports inventory-led operations with built-in AI planning.
Basic rules are static and usually global. AI planning learns demand patterns (by season and location), applies constraints (lead times, budgets),
and proposes actions across POs, transfers and balancing—while keeping policies aligned to real sell-through.
Often, yes—by improving allocation and stock balancing. Moving existing stock to where demand exists can increase availability before buying more stock.
By forecasting demand per location and proposing replenishment quantities and transfers based on each store’s sell-through and service level targets,
rather than using one-size-fits-all rules.
Yes. AI-supported forecasting can drive what to build (finished goods) and what to buy (raw materials), improving material readiness and enabling leaner scheduling with lower stock holding.
Through policy configuration and governance: lead times, minimums, service levels, budget constraints, location priorities and approval workflows where needed.