Planning Logic Evaluation Using Process Analysis and Test Data
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Solution Overview
Problem
The introduction of production schedulers is hindered by the need for extensive know-how in setting various items such as dispatching rules, resource selection criteria, and lot size settings, which requires long-term work and expertise, impeding their widespread adoption.
Innovation Solution
A planning logic evaluation support system equipped with a process characteristic analysis unit, productivity reduction factor extraction unit, and test production data generation unit to facilitate the extraction of key parameters affecting productivity and generate targeted production data for testing, enabling efficient evaluation of planning logics without relying on expert know-how.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If production schedulers are introduced with extensive know-how requirements for setting dispatching rules, resource selection criteria, and lot size settings, then the system can achieve optimal production scheduling, but the introduction process becomes complex and time-consuming, requiring long-term work and expert knowledge
Solution Approach 1:
The system performs self-diagnosis by automatically analyzing production data to identify bottlenecks and productivity reduction factors without requiring expert intervention. The scheduler autonomously generates improvement plans and test cases based on the extracted process characteristics and productivity factors, eliminating the need for extensive manual configuration and expert know-how while maintaining scheduling optimality.
2Reliability
If production schedulers are introduced with extensive know-how requirements for setting various parameters, then the system can achieve optimal production scheduling, but the introduction time is prolonged and widespread adoption is impeded
Solution Approach 1:
The system performs preliminary analysis of production data to extract process characteristics and identify productivity reduction factors before generating scheduling plans. By pre-processing and pre-analyzing the production data to understand bottlenecks and characteristics, the system eliminates the need for time-consuming manual setup and expert configuration during the introduction phase, thereby reducing introduction time while maintaining scheduling quality.
Solution Approach 2:
The scheduler autonomously generates test cases and improvement plans based on automatic analysis of production data, eliminating the need for expert intervention and manual configuration. This self-service capability significantly reduces the time required for system introduction and deployment while maintaining optimal scheduling performance.
3Manufacturing precision
If expert know-how is required for setting modeling and planning logic parameters, then accurate production scheduling can be achieved, but the ease of operation is reduced and only experts can perform the setting tasks
Solution Approach 1:
The system automatically extracts process characteristics and productivity reduction factors from production data, and autonomously generates scheduling plans and test cases without requiring expert configuration. This self-service mechanism maintains high scheduling accuracy by using data-driven analysis while making the system accessible to non-experts, thereby significantly improving ease of operation.
Solution Approach 2:
The system replaces manual expert judgment and configuration with automated data analysis and algorithmic decision-making. By substituting the mechanical process of expert setting with automated extraction and generation processes, the system maintains scheduling accuracy while eliminating the need for expert knowledge, thereby improving ease of operation for non-expert users.
Data Source
AI summary
A problem that is addressed by the invention as claimed in the application concerned resides in providing a tool for facilitating the introduction of a scheduler. One aspect of the present invention is a planning logic evaluation support system equipped with a control unit, a storage unit, an input unit, and an output unit and comprising a process characteristic analysis unit to extract process characteristics from master data; a productivity reduction factor extraction unit to extract noticeable characteristic parameters that have an effect on productivity based on the process characteristics; and a test production data generation unit to generate production data for testing in which the noticeable characteristic parameters are dispersed in a certain scope.