Build practical production plans using customer demand, confirmed orders, BOM requirements, available capacity, due dates, and shop-floor progress.
Mimasa AI connects data from ERP, MRP, MES, databases, and planning files. It helps planners find material gaps, capacity limits, schedule risks, and plan-versus-actual differences before they affect delivery.
Move from scattered orders, BOM records, and capacity files to one production plan planners can review, approve, and revise.
Turn demand and orders into production requirements
Calculate material needs from the bill of materials
Compare required and available machine capacity
Prioritize orders using due dates and business rules
Build and review production schedules
Track planned output against actual output
Replan when machines, materials, or priorities change
Route important changes for human approval
A production plan starts with known demand, available resources, and delivery commitments. But factory conditions rarely stay fixed.
A customer order becomes urgent
A machine loses capacity
A material arrives late
A quality hold reduces usable output
A changeover takes longer than planned
A previous order remains unfinished
Planners often respond by collecting data from several systems and updating spreadsheets by hand. This slows decisions and creates many versions of the plan.
Mimasa AI brings planning inputs into one governed view through data extraction and connectivity. It helps planners understand what changed, which orders are affected, and which schedule options remain possible.
AI production planning uses operational data, business rules, models, and AI agents to support manufacturing plans.
AI prepares the evidence and possible actions. The production planner remains responsible for important schedule and commitment decisions.
Connect, prepare, translate, calculate, check, schedule, monitor, and replan — one flow from raw production data to an approved schedule.
Bring together the inputs needed to build and monitor the plan. The exact inputs depend on the plant and planning method.
Different systems may use different product codes, units, dates, and line names. Mimasa AI can help clean, map, join, and prepare approved planning data, giving planners a consistent view of orders, materials, capacity, and progress.
Translate approved demand and confirmed orders into required finished-product quantities and dates. This is the start of demand-led production planning. It links what customers need with what the plant must make.
Use the approved bill of materials to calculate the parts and materials required for each production order. Compare those requirements with material expected to be ready for use.
Compare required production hours with available machine, line, shift, tool, and labour time. Flag periods where planned load is higher than usable capacity.
Apply rules for due dates, changeovers, batch sizes, routings, customer priority, and maintenance windows. Compare possible sequences before changing the approved plan.
Track actual output, cycle time, stoppages, rejection, and order completion against the plan. Highlight important gaps while there is still time to respond.
When conditions change, Mimasa AI can recommend a schedule adjustment. The planner reviews the reason, impact, and alternatives before approving a major change.
Demand-led production planning begins with what the business needs to deliver.
Mimasa AI can combine these inputs into a production requirement by product, date, plant, or line.
This page treats demand as an input to production planning. Detailed demand forecasting belongs on inventory and supply chain optimization.
A bill of materials defines which components and quantities are required to make a product. Accurate BOM requirements are essential to a workable production plan.
This page focuses on whether the production plan is material-ready. Replenishment, warehouse balancing, and purchase-versus-transfer decisions remain on the dedicated inventory page.
Production capacity planning compares required work with the time and resources available to complete it.
Required capacity is the time or resource needed to complete planned orders.
Required production time = planned quantity × standard cycle time
The real calculation may also include setup, changeover, testing, and defined allowances.
Available capacity is the usable time remaining after planned stops, maintenance, changeovers, and other constraints.
A capacity gap appears when required work is higher than available production time. Mimasa AI can flag the gap and compare actions such as:
The recommendation should show which orders, dates, and resources are affected.
A schedule converts the plan into a sequence of production work. Mimasa AI can help rank or compare orders using configured rules such as:
Two orders need the same line. One order is due earlier. The other uses the current machine setup and can start faster. Mimasa AI can show the trade-off:
The planner selects or approves the final order sequence.
A plan is useful only if teams can compare it with what is happening on the shop floor.
A production gap may be linked to:
Mimasa AI can bring the relevant evidence into one view. It should not claim a root cause unless the available data supports it.
When a gap crosses a set limit, the workflow can:
Build a practical plan using orders, BOM requirements, material readiness, capacity, and due dates.
Confirm whether the required components are expected to be ready before an order enters the schedule.
Compare work across approved machines or lines. Find overloaded and underused periods.
Review another approved production route when the preferred resource is full or unavailable.
Apply due-date, customer, batch, and material rules to compare production sequences.
Group or sequence compatible products when the process allows it. Show the effect on due dates and capacity.
Connect planned maintenance or equipment risk with future production load.
Recalculate expected output when a quality hold or rejection changes available production.
Show how a new priority order affects the existing schedule, capacity, material readiness, and delivery dates.
Compare approved production routes and available capacity across plants when the required data is connected.
Compare planned and actual output during the shift. Alert teams before a small gap becomes a missed daily target.
Focus planners on the orders, lines, and constraints that need action instead of reviewing the entire plan manually.
Production changes create trade-offs. Mimasa AI can help teams compare scenarios before updating the approved plan.
Compare remaining capacity, alternate routings, maintenance estimates, and affected orders.
Identify which scheduled orders depend on the delayed component. Review whether another ready order can move forward.
Show the required material, capacity, changeover, and effect on current commitments.
Recalculate accepted output and show which order or delivery may need recovery production.
Estimate the expected completion time using current actual performance. Compare recovery options.
Each scenario should show its assumptions. The planner approves important production changes.
AI can compare options. Business rules define what is allowed. Access and permissions are handled through data governance and collaboration.
| Planning Status | Meaning | Suggested Action |
|---|---|---|
| Ready | Material, capacity, and routing meet the defined rules | Planner reviews or releases the order |
| At Risk | A gap may affect schedule or delivery | Compare available options |
| Approval Needed | A change crosses a policy or authority limit | Send it to an authorized person |
| Blocked | A required condition is not met | Hold the order and escalate |
Human approval is important when a change affects customers, cost, safety, quality, or major production commitments.
Many planning dashboards stop after showing a delay. Mimasa AI can connect the exception with the next action through agentic workflow automation.
This creates a closed loop between monitoring, replanning, and execution.
Spend less time combining ERP exports, production files, and spreadsheet versions.
Identify BOM, material, capacity, routing, and schedule gaps before production starts.
Review the effect of breakdowns, urgent orders, quality issues, and slow output faster.
Compare required load with available machine, line, shift, and tool time.
See where actual production differs from the approved schedule.
Show the orders, rules, constraints, and assumptions behind each option.
Use AI to prepare choices while authorized people approve major changes.
Record plan versions, exceptions, recommendations, approvals, and actions.
Results depend on BOM accuracy, routing data, cycle times, capacity settings, production reporting, and planning rules.
Mimasa AI adds a governed intelligence and workflow layer across existing systems.
Prepared planning data stays reusable through intelligent data snapshots and no-code data transformation.
| Existing System | Main Role | How Mimasa AI Can Support It |
|---|---|---|
| ERP | Orders, master data, purchasing, and production records | Connect planning context and send approved actions |
| MRP | Material requirement calculations | Explain BOM-related gaps and connect them with schedule decisions |
| MES | Shop-floor execution and production status | Compare actual output and events with the plan |
| APS | Detailed scheduling and constraints | Add cross-system context, plain-language analysis, and approvals |
| CMMS | Maintenance plans and equipment work | Show maintenance impact on available production capacity |
| Quality System | Holds, rejection, and release status | Include quality impact in plan-versus-actual review |
| Spreadsheets | Local plans and adjustments | Reduce manual consolidation and control plan versions |
Integration depends on available APIs, databases, files, and permissions. Mimasa AI does not need to replace the planning and execution systems already in use.
Authorized users can ask questions across governed production data, and publish the result through visualisation and dashboards or insights, analysis and reporting.
Answers can show the source data and assumptions. Critical actions still follow defined approval steps handled by AI agents.
Build, compare, release, and revise production plans with clearer context.
Review BOM requirements and material readiness for scheduled work.
See capacity risks, delayed orders, and open planning exceptions.
Compare shift progress with planned output and explain shop-floor exceptions.
Show planned maintenance and equipment availability to production planners.
Connect holds, rejections, and release status with affected orders and plans.
Review delivery risk using the latest approved production plan.
Connect systems, govern planning data, and control workflow access.
Bring orders, BOMs, capacity, schedules, maintenance, quality, and actual output together.
Use AI to find schedule gaps, compare options, and prepare the next action.
Show which rule, order, resource, or constraint shaped the result.
Route important production changes to authorized planners.
Add intelligence across ERP, MRP, MES, APS, maintenance, quality, databases, and files.
Begin with BOM readiness, capacity load, shift monitoring, or plan-versus-actual control.
Production plans can reveal customer orders, capacity, process details, product structures, and plant performance.
The final setup depends on integrations, plant networks, data policy, and response-time needs.
A strong first use case has:
