Production Loss Analysis Using Shared 4M Resource Correlation
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Solution Overview
Problem
Existing production loss detection systems struggle to identify causes of downtime across multiple machines, workers, and shared resources, limiting the ability to implement effective countermeasures and enhance productivity.
Innovation Solution
A production information processing apparatus that analyzes time-series 4M (Machine, Man, Material, Method) data using a production loss analysis model to classify occurrence factors of production losses across machines sharing resources, generating loss occurrence factor information and suggesting improvement measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If enormous calculation resources are used to analyze 4M data for detecting production loss, then production loss detection capability is improved, but system complexity and resource consumption increase
Solution Approach 1:
The patent segments the production loss analysis into two distinct stages: (1) machine-level production loss identification using individual machine 4M data, and (2) resource-level loss occurrence factor classification by combining multiple machine data through shared resource relationships. This segmentation reduces the complexity of analyzing all machines simultaneously while maintaining comprehensive detection capability.
Solution Approach 2:
The patent introduces shared resource information (workers, materials, methods) as an intermediary that connects multiple machines. By first identifying production losses at each machine level, then using shared resource relationships to classify loss occurrence factors across machines, the system avoids the complexity of direct multi-machine simultaneous analysis while achieving resource-level insights.
2Measurement precision
If production loss analysis is performed at individual machine level, then machine-specific loss identification is improved, but ability to identify resource-related losses across multiple machines deteriorates
Solution Approach 1:
The patent merges production loss information from multiple machines by combining their 4M data through shared resource relationships. After identifying machine-level production losses, the system combines data from machines sharing common resources (workers, materials, methods) to classify loss occurrence factors at the resource level, thereby recovering resource-related loss information that would be invisible at the individual machine level.
Solution Approach 2:
The patent implements a feedback mechanism where shared resource information serves as a bridge between machine-level analyses. The system uses shared resource relationships to feed back and correlate production loss data across machines, enabling the classification of loss occurrence factors by resource while maintaining the benefits of machine-level precision.
3Productivity
If shared resources are analyzed across multiple machines, then resource-level productivity enhancement is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the data processing into two manageable stages: first processing individual machine 4M data to identify machine-level production losses, then processing shared resource relationships to classify loss occurrence factors. This segmentation reduces data processing complexity compared to simultaneously analyzing all machines and resources together, while still enabling resource-level productivity enhancement.
Solution Approach 2:
The patent performs preliminary action by first identifying production losses at each machine level before combining data across machines. This preliminary machine-level analysis simplifies the subsequent resource-level classification by providing pre-processed, machine-specific loss information that can be efficiently combined through shared resource relationships, reducing overall data processing complexity.
Data Source
AI summary
In a production information processing apparatus, a storage device stores a resource shared among a plurality of machines belonging to a predetermined manufacturing area, 4M data information that is time-series data of operating states per unit time of the machines and the resource related to the machines, and a production loss analysis model that defines a criterion for determining a production loss from a combination of the operating states per unit time in the 4M data information, and a processor identifies a production loss of each of the machines to generate production loss information, and combines the production loss information of one of the machines and 4M data information of another of the machines that is different from the machine and shares the resource, using the production loss information and the shared resource, to classify an occurrence factor of the production loss and generate loss occurrence factor information.