AI Engines for Inventory Management Systems

Become a Partner
Man using tablet for Enterprise AI Solitions
We support Inventory Management Platforms looking to improve how stock decisions are made across locations and time intervals.

If your platform relies on static rules, thresholds or manual inputs to drive replenishment and allocation, and you’re looking to introduce AI data-driven decision-making without building it in-house, SolvedBy.Ai engines provide a direct path to that capability.

Embedding these engines enables your platform to generate more accurate demand signals and translate them into replenishment, allocation and stock control decisions within your existing workflows.

For your customers, this results in more precise stock positioning, reduced excess inventory and improved availability across locations.

What solutions we power in Inventory Management Systems

AI Demand Forecasting

Enables your platform to generate accurate demand forecasts at SKU and location level, using both historical data and external demand drivers. This gives your customers a reliable view of future demand to inform replenishment and stock planning decisions.

AI Inventory Optimisation

Calculates optimal stock levels based on demand patterns, variability and service requirements. This allows your customers to balance availability with working capital, reducing excess inventory while maintaining service levels.

AI Stock Control

Generates replenishment quantities and ordering plans based on forecast demand, current stock position and lead times. This ensures stock is ordered and positioned in line with expected demand across your customers locations.

AI Resource Allocation

Allocates warehouse resources such as pickers, zones and tasks based on expected workload, order profiles and operational constraints. This allows your customers to improve picking efficiency, reduce bottlenecks and align warehouse activity with demand.

Case Study

How it works

1

Forecast the future

SolvedBy.Ai engines ingest stock levels, movements, order history and demand signals via API, alongside external drivers, to generate forecasts at SKU and location level. Forecasts reflect both demand patterns and inventory dynamics, providing the basis for downstream stock decisions.
2

Decide the outcome

Forecast outputs are processed into decision layers, including replenishment quantities, safety stock levels and expected warehouse workload. These decisions account for constraints such as lead times, stock policies and operational capacity.
3

Execute in your platform

Decision outputs are returned via API and embedded within your platform’s inventory and warehouse workflows, where users review, adjust and execute replenishment, stock control and picking activities.
4

Improve

Stock movements, order fulfilment and warehouse execution data are fed back into the models, enabling continuous recalibration of both forecasts and decision logic as demand patterns and operations evolve.

AI Engines that can be used in Inventory Management Systems

Powers the AI Demand Forecasting solution and generates demand forecasts at SKU and location level that underpin replenishment, inventory optimisation and stock planning decisions.
Learn more
Enhances all solutions by providing structured external demand drivers that are layered into forecasting and decision models to improve accuracy and responsiveness.
Learn more
Powers the AI Inventory Optimisation solution by calculating inventory targets, safety stock levels and buffer positions based on demand forecasts, variability and service requirements.
Learn more
Powers the AI Inventory Optimisation solution by translating demand forecasts and stock data into replenishment quantities and ordering logic.
The engine optimises the allocation of labour and assets based on forecast demand and operational constraints.
Learn more
....
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.

Ready to explore a partnership with SolvedBy.Ai?

Become a Partner

FAQ

Inventory partners use Forecasting to predict demand at SKU/location level and then apply stock control / inventory optimisation: reorder points, safety stock, reorder quantities and replenishment timing. Our engines can also support workforce or resource scheduling in warehouses (picking, packing, replenishment) where required.

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.

Turns measure how efficiently stock is moving through the business. A common formula is COGS (or usage) divided by average inventory value over the period, with the exact definition agreed with the partner/customer and reported on a consistent cadence.

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.

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.

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.

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

Yes, where the host platform supports scenario workflows. Planners can override assumptions (service levels, lead times, constraints, costs) and compare the impact on availability, inventory value, and risk metrics.

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.

This flags items with stock levels above target cover, or stock held beyond an age threshold. Inputs often include on-hand stock, target days of cover, shelf-life/age bands and demand forecast. It supports actions like reducing orders, promoting stock, or transferring between locations.

Use the Engine name for technical scope, integration work, payloads, and platform architecture (because Engines are the modular units you embed). Use the Solution name only when you want a customer-facing description of the use case. Many partners also choose their own UI label (e.g., “Schedule Assist”) while the underlying capability remains “Scheduling AI”.
TL;DR for AI Forecasting

TL;DR

SolvedBy.AI enables inventory management platforms to embed AI-driven forecasting, inventory optimisation and replenishment via API. We turn demand signals into inventory targets and reorder recommendations, supporting a forecast → decide → execute → improve loop—while you retain inventory workflows, UI and customer ownership.
Become a Partner
Scotia Bank House, 6 South
Charlotte Street, Edinburgh,
Scotland, EH2 4AW
AI Engines
AI Engines Forecasting AI Demand AI Scheduling AI Inventory AI Resource Scheduling AI Task Scheduler AI Production Scheduling AI Exogenous Data
Partners
Partner Solutions Workforce Management Platforms Enterprise Resource Planning Platforms Supply Chain Management & Planning Platforms Restuarant Management Systems Point of Sale Systems Inventory Management Software Budgeting & Forecasting Software
About
Partnering Pricing Security and Trust
Resources
Developer Centre Case Studies Terms & Conditions
© 2026. SolvedBy.Ai. Solved By Ai Ltd. All Rights Reserved.
Privacy Policy | Cookie Policy