Production Management System for 4M Loss Factor Determination
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current production management systems face challenges in accurately determining the occurrence and factors of opportunity losses, as they primarily rely on state information from production equipment, making it difficult to identify losses caused by elements other than Machine, such as Man, Material, and Method.
Innovation Solution
A production management support system that analyzes time series data from all 4 Ms (Machine, Man, Material, and Method) to determine loss state combinations and their corresponding factors, using a combination of rule tables and machine learning models to improve accuracy in loss factor identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If state information is obtained only from production equipment (Machine), then the system is simple to operate, but it is difficult to determine the occurrence of loss caused by elements other than Machine
Solution Approach 1:
The system merges data from multiple sources corresponding to the 4 Ms (Machine, Man, Material, Method) into a unified analysis framework. By combining time series data groups from all four elements, the system comprehensively determines loss occurrence and factors, overcoming the limitation of relying solely on Machine data while maintaining systematic operation.
2Device complexity
If state information is obtained only from production equipment, then data collection is simple, but accuracy in determination of the factor of loss is insufficient
Solution Approach 1:
The system combines time series data groups from all 4 Ms (Machine, Man, Material, Method) to comprehensively analyze loss factors. This merging approach enables accurate identification of which specific element caused the loss, such as determining whether a stop was caused by Material shortage rather than Machine failure, thereby improving factor determination accuracy.
Solution Approach 2:
The system segments the analysis by examining each M (Machine, Man, Material, Method) separately through dedicated time series data groups, then integrates them to determine the specific loss factor. This segmentation allows precise identification of the causative element while maintaining systematic data collection.
3Productivity
If only Machine data is analyzed, then the analysis process is simple, but it is difficult to accurately determine the occurrence of loss
Solution Approach 1:
The system merges time series data groups from all 4 Ms to comprehensively determine loss occurrence. By integrating data from Machine, Man, Material, and Method, the system achieves accurate loss detection while maintaining efficient automated processing through unified analysis rules.
Data Source
AI summary
A system performs a loss factor determination which refers to, for each of two or more different Ms from among 4 Ms (Man, Machine, Material, and Method), a time series data group representing a time series of a state of an element belonging to the M, and which determines, for each period between time points, whether or not a state combination in the same period between time points corresponds to one or a plurality of loss state combinations. In each period between time points, the state combination is a combination of two or more states belonging to the period between time points and corresponding to the two or more Ms. The loss state combination is a state combination defined as an opportunity loss. With each loss state combination, a loss factor, which is a factor of an opportunity loss corresponding to the loss state combination, is associated.