Connect demand, inventory, supplier, warehouse, order and logistics data. Mimasa AI helps teams detect changes, analyse options and coordinate approved action.
Use AI in supply chain processes without replacing your operational systems. Add intelligence, AI agents and human review where decisions matter.
Supply chain visibility | Demand forecasting | Inventory optimisation | Logistics intelligence | Exception workflows
Supply chain automation uses software to complete repeatable planning and operational tasks. It can connect data, monitor conditions, route exceptions and prepare actions.
AI for supply chain teams adds analytical and language skills. It can find patterns, explain changes and compare possible responses.
Artificial intelligence in supply chain work needs clear data and limits. People own consequential planning and operational decisions.
Mimasa connects intelligence, AI agents, workflows and human control around existing systems.
Supply chain information often sits across many systems. Teams may also rely on spreadsheets, emails and reports.
A delay can affect stock, customer orders, production and purchasing at the same time.
This creates an automated supply chain process with clear ownership. It supports response without hiding source information.
Bring together authorised demand, stock, order, warehouse, supplier and logistics data.
Match identifiers, locations and units. Apply shared definitions and flag data gaps.
Watch configured demand, inventory, supplier, shipment and process signals.
Estimate impact, compare scenarios and prepare a recommended response with evidence.
Ask an authorised person to review consequential planning, movement or purchasing decisions.
Create a task, update a report or trigger a permitted action in a connected system.
Forecasts and recommendations should state uncertainty. The evidence remains available for review.
See how Mimasa connects data, agents and workflows →Supply chains change every day. Effective automation connects planning with current operational conditions.
Demand forecasting uses historical and current data to estimate future requirements. AI demand forecasting can compare patterns and changing signals.
Mimasa works with existing demand planning software. It does not guarantee forecast accuracy.
Connect stock, demand, lead time, freight cost and production impact. Compare whether to buy, transfer, replenish or wait.
AI inventory management can flag shortage, excess, ageing and imbalance signals.
Inventory automation can monitor defined stock conditions. It can prepare a recommendation or task when a threshold is reached.
Intelligent inventory management can also consider demand, lead time and other locations.
Connect current information across orders, inventory, warehouses, suppliers and shipments. See status and relationships in one context.
Data freshness depends on each source system's update frequency.
Combine shipment, route, order and event data. Logistics intelligence can identify delays and help teams investigate likely impact.
Logistics automation can create an alert, task or approved response. It does not guarantee arrival times.
Monitor configured supplier, inventory, order and logistics signals. Collect context and prepare a response for review.
Mimasa cannot identify or prevent every disruption.
Supply chain visibility connects related operational events. Teams can understand how one change may affect other work.
AI supply chain visibility should show evidence and freshness. Existing visibility platforms remain the source for their data.
See shipment visibility and exception management in logistics
Supply chain intelligence turns connected information into decision context. It combines current conditions, historical patterns and business rules.
Predictive supply chain analytics estimates possible outcomes. Results remain forecasts, not facts.
Compare carrier, lane and freight cost performance with freight and transportation analytics
Coordinate cross-functional exceptions with a supply chain control tower
Track vehicle utilisation, cost and maintenance with fleet performance intelligence
Supply chain workflow automation connects monitoring, analysis, decisions and tasks. Each decision goes to the right owner.
| Workflow step | What Mimasa can do | Human control |
|---|---|---|
| Demand review | Analyse history and approved current signals | Confirm assumptions and forecast use |
| Inventory review | Compare stock, demand, lead time and location options | Approve buy, transfer or replenishment decisions |
| Supplier delay | Assess affected orders, stock and production | Decide sourcing and priority changes |
| Logistics exception | Connect shipment status with business impact | Approve customer or operational response |
| Risk monitoring | Flag configured changes and collect context | Assess risk and choose action |
| Reporting | Prepare a dashboard, report or presentation | Confirm interpretation and distribution |
A supply chain AI assistant helps with one task. An agent can complete several approved steps inside a governed process.
Generative AI for supply chain work can prepare summaries. People confirm the facts and operational meaning.
An autonomous supply chain uses automation with limited manual work. Safe autonomy depends on the action and business impact.
Mimasa can automate defined, low-risk steps. It stops when data, confidence, authority or approval is missing.
This supports a cognitive supply chain without removing accountability.
Supply-chain teams already use ERP, WMS, MRP, TMS, planning and analytics tools. Mimasa connects selected work across this environment.
This supply chain automation software layer does not replace operational systems. They retain their data and ownership.
Supply chain automation tools should fit existing controls. Mimasa applies permissions and review steps to each configured action.
Supply-chain data can include demand, stock, supplier, order and customer information. Access must be controlled.
The organisation decides who can see data and approve actions.
Explore Data Governance →Choose recurring work that uses several data sources. Define the output and approval before building the workflow.
Build agents for demand, inventory, logistics and exception tasks.
Explore capability →Connect signals, analysis, tasks and approvals.
Explore capability →Extract information from orders, documents and operational files.
Explore capability →Clean, map and validate supply-chain data.
Explore capability →Control operational data, agent access and actions.
Explore capability →Review demand, inventory, logistics and exceptions.
Explore capability →Manage tasks, discussions, approvals and agent hand-offs.
Explore capability →Connect supplier and purchasing workflows with supply-chain signals.
Explore function →Connect material availability with production plans and plant operations.
Explore function →Analyse operational data and prepare evidence-backed decisions.
Explore function →Common questions about supply chain automation, visibility, analytics and governed action with Mimasa AI.
Bring a demand, inventory, logistics, visibility or exception workflow. We will show how Mimasa can connect the data, apply controls and reduce manual work.
See How Mimasa Works →