mIQ Planner
mIQ Planner goes beyond traditional ERP/MRP tools to optimize production plans using artificial intelligence. It generates proactive production strategies through dynamic order management, bottleneck resolution, and green scheduling.
Capacity Planning with MES Data Using Artificial Intelligence
The system collects machine capacity, shift schedules, and production speeds via MES and processes them in an AI-powered planning engine. This engine distributes production orders to resources in the most appropriate manner, taking into account historical data and current conditions. This balances the capacity utilization of machines and lines, preventing idle time or overload.
What-If Scenarios with Change Impact Analysis
Users can make hypothetical changes to the production plan and see the results in advance. For example, scenarios such as “How would adding overtime affect the production target?” or “How would the production plan change if Machine X were to be out of service tomorrow?” can be created. mIQ Planner calculates outputs such as the percentage of orders to be completed, delays, and costs for these What-if scenarios and presents them to the user. Planners can compare scenarios to determine the most suitable option. See the impact of changes with ‘What-if’ scenarios.
Dynamic Planning Based on Stock and Order Data
The module provides access to order and inventory information within ERP/MRP systems or MES. This allows it to continuously monitor order quantities and current stock levels and plan accordingly. For example, if raw material stock is insufficient, the system automatically issues a warning or revises the production plan based on stock levels. Similarly, when a new customer order is added, the plan is automatically updated and capacity is allocated for the relevant date.
Adapting to Instant Order Changes
Last-minute order changes (cancellations, quantity increases/decreases, delivery date advances, etc.) are quickly reflected in the plan. mIQ Planner allows users to reoptimize the plan with just a few clicks when order delivery dates change or an urgent additional order is added. The system identifies affected production items and machines and provides recommendations for adapting to the new plan or performs automatic re-planning.
Scenario Comparison and Selection of the Best Plan
When a user creates multiple plan scenarios (e.g., normal, overtime, or weekend work), the Planner module compares these scenarios based on specified criteria. These criteria include metrics such as production completion time, total cost, number of delayed orders, and machine utilization rate. With AI support, the system runs a decision support algorithm that optimizes these criteria and suggests the “best plan.” The user can select and approve the suggested plan to implement it.
Green Production Planning (Energy and Carbon Optimization)
mIQ Planner considers production plans not only in terms of time and quantity, but also in terms of energy consumption and carbon footprint. In particular, the estimated energy consumption of different plan scenarios is calculated. For example, if the energy cost is lower during the night shift, the system may suggest shifting production to the night. Similarly, to reduce carbon emissions, processes that consume high energy can be scheduled at alternative times or on different machines. When the user selects the “green planning” mode, the artificial intelligence algorithm combines PMS (Energy Management System) data with production data to present the most sustainable plan.
Predicting Delay Risks with Data
The system uses historical performance data to predict the risk of delays in planned jobs. For example, if a particular machine generally takes 10% longer than planned to produce, the risk of delay for jobs assigned to that machine is flagged during the planning phase. mIQ Planner analyzes cycle times and failure statistics in MES data and issues warnings such as “it will be difficult to meet the target date for this order with this plan.” The planner can then allocate additional resources or revise the delivery date based on these warnings.
Production Line Balance Optimization (Cost, Quality, Time)
The system strives to balance multiple objectives such as minimum cost, high quality, and short production time. mIQ Planner evaluates these factors together. For example, an overtime scenario enables fast production but increases costs; a normal working hours scenario, on the other hand, is lower cost but has a longer delivery time. The system evaluates each scenario in terms of cost, quality, and time using multi-criteria optimization techniques (e.g., weighted scoring or Pareto optimization). This balance is presented in the user interface through graphs or summary scores.
Solving Capacity Bottlenecks with Artificial Intelligence Recommendations
The Planner module identifies potential bottlenecks using the Theory of Constraints approach when creating the production plan. With AI support, it provides suggestions for eliminating these bottlenecks. For example, if a particular work center cannot keep up with the plan due to insufficient capacity, the system will suggest alternatives such as “Perform this operation on an alternative machine” or “Outsource part of this product to a subcontractor.” If increasing capacity is not possible, the system may also suggest postponing low-priority tasks.
Creating a Smart Manufacturing Strategy Instead of a Plan
mIQ Planner does not merely provide the user with a static production plan; it also develops a production strategy. This includes the plan's assumptions, risks, and recommendations. For example, the system may conclude, “Demand will be high over the next 3 months. Recommended strategy: take capacity-enhancing measures (overtime, opening new lines).” With this feature, mIQ Planner provides high-level insights and recommendations, setting it apart from traditional planning software. It creates not just a plan, but an intelligent production strategy.