Inventory AI

Integrate intelligent inventory planning

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What is Inventory AI

Inventory AI is a unified decision engine that helps software platforms optimise both inventory planning and day-to-day stock control. It combines two capabilities into a single intelligence layer:

Inventory optimisation: deciding how much stock should be held and when it should be replenished across the short and medium term.

Stock control optimisation: supporting the operational decisions needed to manage stock day to day, such as ordering, allocation, and replenishment.

By bringing policy optimisation and operational execution together in one engine, Inventory AI allows platforms to embed advanced inventory intelligence without having to build and maintain complex inventory algorithms themselves.

Direct solutions this engine powers

AI Inventory Optimisation

This solution sets optimal stock targets, buffers and replenishment policies across weeks and months, using internal and external data to align inventory planning with expected operational needs. By integrating it, your platform can offer customers a structured, data-driven approach to long-range inventory planning without maintaining complex optimisation logic in-house.

AI Stock Control Optimisation

This solution recommends what to order next — and when — based on real-time stock positions and operational constraints. It gives your platform the ability to surface accurate, execution-ready ordering decisions that adjust to live conditions and supplier rules.

Why partners need it

Sets inventory targets and buffers by SKU/location, and optional network-level targets across warehouses and stores.

Recommends order quantities and timing that are actionable against supplier rules.

Flags exceptions and priorities for stockout risk, waste/spoilage risk, excess stock, supplier delays.

Supports perishables and shelf-life constraints with tunable waste-vs-availability trade-offs.

Enables what-if planning (service level, lead time, cost and constraint changes) where the host platform supports scenarios.

Learns from real-world execution signals (sales/usage, adjustments, overrides) to improve recommendations over time.

Typical inputs

Minimum viable inputs

Demand history or demand forecast (by SKU/location and time period).

Current stock position (on-hand; optionally on-order / in-transit).

Product, location and supplier identifiers (master data mapping).

Common optimisation inputs (recommended)

Lead times and delivery calendars (including variability where available).

Ordering constraints: MOQ, case packs, rounding rules, cut-off times, delivery windows.

Service level targets (by category/item class/region/channel).

Cost signals: unit costs, holding cost proxies, waste penalty (especially for perishables).

Shelf-life / hold-time rules, spoilage curves (perishable categories).

Adjustments: waste, shrink, stock counts, transfers (if available).

Typical outputs

Recommended orders (quantities and timing) per SKU/location, including rounding to supplier constraints.

Inventory policy recommendations: targets, buffers, reorder points, purchase cadence.

Exception alerts and ranked action lists (stockout risk, waste risk, excess/aged stock, constraint violations).

Explainability fields to support user trust (e.g., “projected cover < safety threshold”, “limited by MOQ/case pack”).

What partners need to implement

Provide the inventory inputs required to generate recommendations (stock position, forecasts/history, constraints, master data IDs).

Ingest Inventory AI outputs (recommended orders, targets/buffers, exceptions) into your data model and workflows.

Optionally return closed-loop feedback (actual sales/usage, overrides, adjustments) to improve performance.

Surface recommendations inside your existing replenishment / purchasing / planning screens (review → override → approve → publish).

Provide exception views and “action lists” so users can triage risks quickly.

If relevant, expose scenario controls (service level, lead time, cost assumptions) and show impact.

Expose a small set of KPIs to validate outcomes and build trust (availability/service level vs target, working-capital indicators, stockout/waste risk).

Track adoption signals (acceptance rate, override magnitude and reasons where captured).

Monitor operational outcomes (stockouts, waste, turns, aged stock) with simple baseline comparisons.

Inventory optimisation sets stock and consumable levels based on expected demand and uncertainty. It identifies risk early, allowing teams to adjust before shortages or excess occur.

In leisure, this supports better planning for retail stock, food and beverage, consumables, and operational supplies by site or facility, reducing waste while maintaining availability during busy periods.

Task scheduling plans what work should be done, when, and by whom, based on expected demand and available capacity. Tasks are prioritised and sequenced to reflect how work is actually completed on site.

For leisure teams, this improves execution across cleaning routines, safety checks, facility preparation, changeovers, and compliance tasks, ensuring critical work is completed before and during peak usage.

Resource allocation supports decisions on where to deploy limited resources such as space, equipment, capital, and budget. It evaluates options using demand forecasts and expected return.

Leisure leaders use this to decide which facilities to prioritise, where to invest, and where to scale back, improving utilisation and returns from existing assets rather than expanding capacity unnecessarily.

Price optimisation supports pricing and promotion decisions based on expected demand and utilisation impact. Prices adjust in response to changing conditions rather than reacting late through blanket discounts.

In leisure, this helps optimise membership pricing, day passes, peak and off-peak rates, and promotional offers, protecting revenue while improving utilisation across quieter periods.

Opening hours optimisation evaluates when sites or facilities should operate based on expected demand and financial impact. It identifies which hours contribute value and which create unnecessary cost.

This allows leisure operators to adjust opening times by site or facility, maintaining access when demand exists while reducing exposure during low-usage periods.

Budget forecasting builds budgets based on expected demand rather than fixed assumptions. Forecasts adjust as conditions change, giving finance and operations teams clearer visibility of risk and cost pressure.

For leisure organisations, this improves control, reduces variance, and limits late-year corrective action when demand shifts.

(Best suited to asset-heavy leisure operations)

Predictive maintenance identifies early failure risk in critical assets that affect safety, availability, or customer experience.

In leisure, this applies to equipment such as pool systems, HVAC, rides, lifts, and facility-critical infrastructure, reducing unplanned downtime and protecting service and safety.

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FAQ

Inventory AI is a unified inventory decision engine that combines inventory optimisation (planning) with stock control (execution). It uses demand forecasts and inventory signals to recommend targets, buffers and replenishment actions that balance service levels, waste and working capital.

Yes. Inventory AI supports short-term execution (“what should we order next?”) and medium-term planning (“what targets and policies should we use over coming weeks/months?”). Many partners start with execution and progressively add planning targets and scenarios.

Outputs typically include recommended order quantities and timing, inventory targets and buffers, replenishment policy suggestions (e.g., reorder points), and exception alerts such as stockout risk, waste risk, excess/aged stock, and supplier constraint violations. Explainability fields can be returned to support audit and trust.

Most customers run execution recommendations daily or multiple times per week, aligned to delivery cycles and volatility. Planning outputs (targets/policies) are typically recalculated weekly or whenever key assumptions change.

Partners need modular building blocks they can embed, name, package, and price inside their own product. The partner site is therefore “Engine-first”: it presents the partner-ready capabilities in a consistent structure that fits many SaaS platforms. The direct site is “Solution-first”: it presents use cases in outcome language for end customers.

Yes. Shrink/waste and stock adjustments can be incorporated into the stock position. If you provide these signals over time, the engine can learn typical loss patterns and improve recommendations.

This measures adoption of the ordering recommendations. It compares suggested order quantities to final submitted quantities (and optional override reasons). Partners often track % accepted unchanged, % modified, and average adjustment size to identify trust and training gaps.

Common KPIs include: fewer stockouts, improved availability/service level, reduced waste/spoilage, reduced working capital, improved inventory turns, reduced excess/aged stock, and time saved for planners and managers.

Yes. Users can accept, adjust, or lock recommendations. Overrides can be captured as feedback (optionally with reasons) to improve future performance and increase trust.

Partners can set service level goals (e.g., 95% availability) by category, item class, region, or channel. Inventory AI uses these targets to size buffers and plan inventory at the lowest cost while meeting the desired availability.

TL;DR

Inventory AI is a unified inventory decision engine that combines inventory planning (targets, buffers and policies) with day-to-day stock control (ordering recommendations). It helps platforms improve availability while reducing waste and working capital, with actionable outputs that fit existing replenishment and planning workflows.

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