Real-time Predictive Time-to-Completion for Configure-to-Order Manufacturing
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Solution Overview
Problem
Current manufacturing systems for configure-to-order products rely on historical cycle times, which are averages and do not account for configuration variability, leading to inaccurate prediction of completion times and inefficiencies due to labor-intensive manual solutions.
Innovation Solution
An automated process that generates a self-adjusting predicted completion time specific to each order by analyzing actual cycle times for completed activities and updating the prediction in real-time as manufacturing progresses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If historical cycle times (average times) are used to predict completion times, then the prediction process is simple and automated, but the prediction accuracy is poor due to configuration variability
Solution Approach 1:
The patent segments the manufacturing process into discrete activities (power up, surface scan, configuring, loading software, etc.) and assigns specific cycle time estimates to each activity based on its characteristics and the product configuration. This allows the system to move from a single average historical cycle time to multiple activity-specific time estimates, improving accuracy while maintaining automation.
Solution Approach 2:
The patent applies local quality by providing customized cycle time estimates for each specific manufacturing activity based on the actual product configuration being manufactured. Instead of using a uniform average time for all activities, the system tailors the time estimate to each activity's specific requirements and the particular configuration options selected by the customer.
2Measurement precision
If manual interrogation of each ordered configuration is performed to improve prediction accuracy, then the prediction accuracy improves, but labor intensity and error rates increase
Solution Approach 1:
The patent implements self-service by enabling the system to automatically calculate and update completion time predictions without requiring manual expert analysis. The system uses the ordered configuration data and activity-specific cycle time estimates to compute predictions autonomously, eliminating the need for manual data synthesis and expert intervention while maintaining high accuracy.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously monitors actual cycle times as activities are completed and uses this information to refine and update the completion time prediction. This automated feedback loop improves accuracy over time without requiring manual analysis, replacing expert judgment with systematic data-driven adjustments.
3Measurement precision
If expert skills are used to analyze and project completion times for each configuration, then the prediction accuracy improves, but manufacturing efficiency decreases due to resource diversion
Solution Approach 1:
The patent replaces the mechanical system of expert human analysis with an automated computational system. Instead of relying on experts to manually interrogate configurations and synthesize data, the system uses automated algorithms to calculate completion times based on configuration data and activity estimates, freeing expert skills for other value-added activities and improving overall manufacturing productivity.
Data Source
AI summary
Predicting a completion time for the manufacturing of an ordered configuration of a product based on actual completion times and self adjusting prediction completion times. An order is received to manufacture a particular configuration of a product. Multiple activities are identified that need to be finished in order to complete the manufacturing of the configuration. A completion time that the configuration will be manufactured is predicted based on actual completion times and self-adjusting predicted completion times for the activities.