Production Planning & Scheduling
Automatically distribute orders optimally across machines.
AI-driven production scheduling considers capacity, changeover times, priorities and disruptions simultaneously to build realistic, optimized production plans. It reacts to changes faster than manual planning, improving on-time delivery and machine utilization.
- Higher on-time delivery to customers
- Better utilization of costly machine capacity
- Faster response to rush orders and disruptions
equipment utilization
changeover time share
payback period
on-time delivery
Derived from current utilization, changeover share and the cost of late deliveries.
- 01
Capture planning logic
Capture existing rules and constraints together with planners.
- 02
Data integration
Combine orders, capacity and changeover matrix from ERP and MES.
- 03
Build optimization model
Develop an algorithm to generate plans under constraints.
- 04
Test scenarios
Simulate different disruption and priority scenarios.
- 05
Rollout with planners
Gradually integrate suggestions into daily planning.
Steps
Data sources
Stakeholders
From first data access to production – every step delivers a tangible interim result.
ERP order data
Quantities, dates and priorities per order.
MES capacity data
Available machine time and current allocation.
Changeover matrix
Setup times between product variants.
Workforce availability
Shift schedules and qualifications.
Disruption history
Past disruptions used for buffer planning.
Material availability
Stock levels and supplier commitments.
Production planning
Gets solid plan proposals instead of manual spreadsheets.
Sales
Can commit to realistic delivery dates.
Plant management
Sees higher utilization and less idle time.
Shift supervisors
Gets clear, actionable daily plans.
Higher on-time delivery to customers
Better utilization of costly machine capacity
Faster response to rush orders and disruptions
Less manual planning effort
Transparent basis for prioritization decisions
With a solid data foundation this use case gets faster, cheaper and far more stable.
Real-time data alignment between ERP and MES
Fast replanning in response to short-notice changes
Simulation environment for what-if analyses
Seamless integration with existing planning tools
Demand forecasting
More accurate forecasts directly improve planning quality.
Energy optimization
Scheduling can actively avoid peak loads.
Inventory optimization
Coordinated plans reduce safety stock needs.