Balance material and product inventory across warehouses, plants, and suppliers. Mimasa AI combines demand forecasts, available stock, lead times, freight cost, and production needs. It helps teams decide when to buy, replenish, hold, or transfer inventory.
The platform brings demand forecasting software and supply chain optimization tools into one governed workflow. Teams can move from a forecast or stock alert to an approved purchase, replenishment, or warehouse transfer.
Work with existing ERP, WMS, planning tools, databases, and files
Keep supply chain teams in control of important decisions
Explain the cost, time, and availability behind each recommendation
Deploy on-premise, in a private cloud, or in the cloud
One warehouse may face a shortage while another holds excess stock. At the same time, purchase orders may already be open. Demand may change before those orders arrive. A supplier delay can put the production plan at risk.
The required data often sits across ERP, WMS, planning tools, supplier records, and spreadsheets. Mimasa AI connects those inputs through data extraction and connectivity, so common problems become visible early.
Teams buy material that already exists elsewhere
Stock transfers begin without comparing freight cost
Reorder decisions use old demand data
Shortages appear after production is scheduled
Excess inventory remains hidden across locations
Safety stock is set once and rarely reviewed
Supplier lead times differ from actual delivery performance
Planners cannot see the effect of a stockout on production
Different teams use different inventory numbers
Many supply chain planning tools show only one part of this picture. Mimasa AI connects the inputs and turns them into a clear decision and an approved next step.
Inventory optimization is the process of deciding what stock to hold, where to hold it, and when to replenish it. The goal is to support demand without keeping more inventory than the business needs.
Which materials may run short?
Which warehouses hold excess stock?
How much should we reorder?
When should we place the order?
Should we buy or transfer the material?
Which supplier can meet the required date?
What production orders are at risk?
Which items are slow moving or ageing?
Mimasa AI works as an intelligence and workflow layer. It can use data from current inventory, warehouse, production, procurement, and supply chain systems. For teams that use British English, inventory optimisation software refers to the same type of solution: software that balances availability, service needs, and inventory cost across the network.
Connect, prepare, forecast, find, compare, recommend, and act — one flow from raw inventory data to an approved purchase or transfer.
Bring together the information needed for the decision. The exact inputs depend on the product, network, and business rules.
Inventory data may use different product codes, units, locations, and status names. Mimasa AI can help clean, map, and combine approved data, and separate stock that is available from stock that is blocked, reserved, expired, or under quality review.
Use approved history, orders, seasonality, production needs, and business inputs to estimate future demand.
Compare expected demand with usable inventory, planned receipts, and supply lead times.
When a shortage appears, compare the available choices.
Rank the options using available cost, time, risk, and business rules.
Send the recommendation to an authorized employee. After approval, a workflow can create a purchase request, stock-transfer task, supplier follow-up, or system update when the integration allows it. Every input, recommendation, approval, and action remains linked for review.
This decision needs more than a stock check. A warehouse may have the required material, but it may be too far away. The transfer may also reduce that warehouse below its own safety stock. Buying may cost more, but the supplier may deliver faster. An open purchase order may already cover part of the need.
| Decision Factor | Buy From Supplier | Transfer From Warehouse |
|---|---|---|
| Available quantity | Supplier capacity or confirmed quantity | Usable stock at source warehouse |
| Delivery time | Supplier lead time and promised date | Pick, dispatch, freight, and receiving time |
| Cost | Purchase price, freight, tax, and order cost | Transfer freight and handling cost |
| Supply risk | Supplier reliability and open-order status | Source warehouse demand and safety-stock impact |
| Production impact | Expected arrival against material need date | Transfer arrival against material need date |
| Approval | Purchase limits and supplier rules | Inter-warehouse transfer rules |
The system explains the recommendation. The authorized team makes the final high-value decision, with the follow-up handled by agentic workflow automation.
Compare usable stock across locations. Identify where excess inventory can cover a shortage elsewhere.
Use the production plan, bill of materials, inventory, receipts, and lead times to find shortages before the need date.
Suggest what to order, how much to order, and when to order it using demand, policy, lead time, and available stock.
Review whether current safety-stock levels still match demand variation, supply lead time, and service needs.
Find stock with low recent or expected use. Show which plants or warehouses may need the same item.
Identify time-sensitive stock and compare it with future demand. Route possible transfers or use-priority decisions for review.
Check the effect of a late shipment. Compare alternate suppliers, internal stock, approved substitutes, and production priorities.
Find orders that no longer match current demand, quantity, date, or inventory position.
Link material availability with open production orders. Alert teams when a shortage may affect output or delivery.
Estimate demand at SKU, location, and time levels when enough useful data is available.
Compare the impact of demand changes, supplier delays, freight changes, plant downtime, or new customer orders.
Focus planners on stockouts, excess, late receipts, and high-impact decisions instead of reviewing every item manually.
Inventory decisions depend on future demand, not only current stock. AI for demand forecasting can help teams prepare for changes before they become urgent stock or supply problems. Mimasa AI connects the forecast with inventory, open supply, warehouse availability, and production needs.
Inventory forecasting tools can estimate demand by SKU, material, warehouse, plant, or time period. A demand forecasting solution becomes more useful when it can trigger the next inventory decision.
Different products and materials may need different supply chain forecasting methods.
The right method depends on demand history, seasonality, product life cycle, and the planning horizon. No single method is best for every item. New products, spare parts, seasonal items, and stable raw materials may behave very differently.
SKU-level demand forecasting software estimates future demand for a specific product and location. This level of detail can help teams:
SKU-level forecasts require enough useful history. Sparse or irregular demand should be marked for closer review.
Demand can change. New products may have little history. Irregular items may be hard to predict. A useful forecast should show:
The forecast supports the decision. It should not be treated as a guaranteed outcome.
The best inventory forecasting software is not simply the product with the most models. It should fit the way your team makes inventory decisions. Look for an inventory forecasting tool that can:
The goal is not only a forecast. The goal is a better stock decision, published through insights, analysis and reporting.
A basic reorder point may use average demand and a fixed lead time. Real supply chains often need more context. Supply chain demand planning connects expected demand with inventory, open supply, lead times, and service goals. Demand forecasting tools provide one input. Available stock and supply constraints complete the decision.
The recommended quantity can be sent for planner or procurement approval. High-value orders, new suppliers, unusual demand, and weak forecasts should receive closer human review.
Demand and supply planning software may identify a gap, but teams still need to resolve it. Mimasa AI can add an action layer across current systems using AI agents to route a replenishment, purchase, transfer, or supplier follow-up for approval. This supports inventory management software demand forecasting integration without moving every process into a new application.
Supply chain optimization tools need to follow company policy. Mimasa AI can work with the supply chain planning tools already used by the business. It adds governed data, AI analysis, recommendations, approvals, and agentic workflows across them, with permissions handled through data governance and collaboration.
| Decision Status | Meaning | Suggested Action |
|---|---|---|
| Ready | The option fits approved rules and available data | Send it for release or final review |
| At risk | Cost, time, supply, or stock creates a concern | Compare other options |
| Approval needed | The option crosses a set value or policy limit | Route it to an authorized person |
| Blocked | A required rule or input is not met | Hold the action and escalate |
Rules should be reviewed when suppliers, freight, products, or operating conditions change.
Many inventory dashboards stop after showing a shortage. Mimasa AI can connect the alert with the next workflow.
Demand rises for a finished product.
Material requirements are updated.
A raw-material shortage is predicted.
Stock across warehouses is checked.
Transfer and supplier options are compared.
The system shows cost, time, and production impact.
Procurement reviews the recommendation.
An approved purchase or transfer task is created.
The action and later receipt are linked to the original exception.
This creates a closed loop from forecast to supply action.
Find likely shortages before they stop production or delay delivery.
Check whether stock already exists at another plant or warehouse before buying more.
Identify items with more inventory than current and expected demand require.
Use demand, lead time, and open supply to decide when action is needed.
Review cost, time, production risk, and safety-stock impact together.
Direct planners to shortages, late supply, excess, and high-value decisions.
Give production, procurement, warehouse, and supply chain teams the same context.
Record the data, recommendation, approval, and action behind each workflow.
Results depend on inventory accuracy, demand data, supplier information, lead times, freight inputs, and business rules.
Mimasa AI adds analysis and action workflows across current systems. Prepared inventory data stays reusable through intelligent data snapshots and no-code data transformation.
| Existing System | Main Role | How Mimasa AI Can Support It |
|---|---|---|
| ERP | Orders, purchasing, inventory, finance, and master data | Combine data, explain exceptions, and send approved actions |
| WMS | Warehouse stock and movement | Compare location-level availability and transfer options |
| MRP | Material requirement calculations | Add demand context, cross-location stock, and workflows |
| APS or planning system | Supply and production planning | Add scenario analysis, natural-language questions, and approvals |
| TMS or freight data | Transport rates, routes, and shipment status | Add freight cost and time to transfer decisions |
| Supplier systems | Quotes, orders, dates, and confirmations | Compare supplier options and monitor delays |
| Spreadsheets | Local plans, rates, and adjustments | Prepare governed inputs and reduce manual consolidation |
Integration depends on available APIs, databases, files, and permissions. Mimasa AI does not replace warehouse execution, barcode scanning, picking, packing, or other core WMS functions.
Authorized users can ask questions across governed inventory and supply data, and publish the result through visualisation and dashboards.
Which materials may run short next month?
Can another warehouse cover this shortage?
Is transfer cheaper and faster than a new purchase?
Which purchase orders no longer match expected demand?
Which items have excess stock across all plants?
Which supplier delays put production at risk?
What happens if demand rises by 15%?
Which near-expiry items can another location use?
Why did the system recommend this replenishment quantity?
The answer can show the source data and assumptions. Approved workflows control any action that follows.
Review stockouts, excess, replenishment needs, and location-level inventory.
Balance demand, supply, inventory, cost, and service across the network.
Receive purchase recommendations with need date, quantity, alternatives, and production impact.
See approved transfer needs and the stock status behind each request.
Understand which material shortages may affect the plan.
Review high-impact supply exceptions and open actions.
See the cost and working-capital context behind major inventory decisions.
Connect systems, govern data, and control workflow access.
Build each recommendation from wider business context, not one inventory number.
Use availability, cost, time, and production impact in the same decision.
Turn an exception into an approved purchase, transfer, or follow-up workflow.
Show the data, rules, and assumptions behind the preferred option.
Use approval steps for high-value, unusual, or policy-sensitive decisions.
Add intelligence across ERP, WMS, MRP, planning tools, databases, APIs, and files.
Prove the data and workflow before expanding across more locations and products.
Inventory and supply data can reveal product demand, material cost, supplier terms, customer needs, and plant performance.
The final design depends on integrations, data policy, plant networks, and response-time needs.
A strong first use case has:
