u-Planning
Smart Production Planning
u-Planning is the first smart production planning solution in the industry built using data mining techniques.
Traditional planning relies on step-by-step simulation, which is slow, difficult to maintain, and often inaccurate.
u-Planning uses historical factory data to automatically build models that capture capacity conditions, cycle times, and interactions between different product mixes. Through continuous learning and automatic updates to the cycle time model, the system delivers accurate planning results within a short time. Compared with traditional planning methods, u-Planning offers significantly faster computation speed and much higher prediction accuracy, effectively supporting long-, mid-, and short-term production planning needs while improving on-time delivery.
─ Key Advantages ─
Data-Driven Modeling
Uses data mining to build dynamic models that generate daily cycle time parameters for every product at each workstation, enabling accurate long-, mid-, and short-term planning.
Accurate Forecasting
Predicts future tool loading and product mix based on shipment targets, helping production lines adjust speed and capacity allocation to improve on-time delivery.
Fast Plan Generation
Learns from production data and updates the cycle time model regularly, allowing rapid plan generation without lengthy manual simulation.
Multi-Scenario Simulation
Supports multiple objective settings. With optimization logic and what-if analysis, the system generates the best release plan under balanced objectives to support managerial decision-making.
Benefits
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Improve decision quality and help the factory mitigate risks
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Increase on-time delivery and enhance customer satisfaction
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Optimize capacity planning and improve profitability
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Shorten production cycle time and reduce excess WIP or dead stock caused by poor planning
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Reduce workload for production control teams and free resources for efficiency improvement initiatives
Lengthy Planning Computation
Poor Cycle Time Accuracy
High Maintenance Effort
Traditional planning systems require hours—or even a full day—to generate results.
Relying only on averages leads to large gaps between simulation and reality, reducing the value of planning output.
Planning simulation data is complex and labor-intensive to maintain, and tends to become outdated over time.
Lack of Optimization Capability
Uncertain Capacity Changes
Difficulty Responding to Fast-Changing Demand
Long computation time prevents running many scenarios, making it difficult to identify the best plan for decision-making.
Traditional planning cannot reflect daily tool conditions or product mix changes, resulting in inaccurate capacity and delivery estimates.
Order changes and urgent orders cannot be evaluated quickly, making it hard to update plans or assess delivery impact.
─ Solutions ─
1
Quickly Build
Dynamic Models
Directly connects to factory historical data to automatically build capacity and cycle time models.Learning is fast, continuously updated, and reduces manual maintenance effort.
2
Accurate Cycle Time & Capacity Prediction
Uses data mining and neural-network-based models to achieve much higher accuracy than traditional simulations.Helps factories understand capacity bottlenecks and delivery risks in real time.
3
Automatic Master Production Schedule (MPS) Generation
Supports both forward and backward planning modes.Not only predicts—but also generates—production plans that meet demand, improving on-time delivery and capacity goals.
4
Fast Response to
Order Changes
When new orders, rush orders, or production variations occur, the system quickly recalculates due dates and generates optimized results, minimizing impact on existing commitments.
5
What-If Scenario Simulation
Supports multiple improvement scenarios for evaluating different strategies in advance.Helps managers make faster and lower-risk decisions.
6
Short Deployment & Computation Time
Deployment takes only around two months.Models provide feedback quickly, enabling rapid go-live and immediate benefit realization.