MES Timestamp Collection via Token-Based Meta-Status Model
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Solution Overview
Problem
Current MES systems face challenges in accurately calculating Overall Labor Effectiveness (OLE) and Overall Equipment Effectiveness (OEE) indicators due to unreliable and incomplete input data, leading to inefficiencies in identifying loss causes in complex manufacturing environments, where manual and machine operations are intertwined.
Innovation Solution
A method and system for collecting time-stamps of working-statuses of machines and operators using a token-based meta-status model, where meta-statuses (operating and booked) determine data ownership, ensuring synchronized and redundant-free data collection without the need for double-entry or post-acquisition processing, allowing real-time and automatic data acquisition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional MES systems collect time-stamps from both operator and machine independently, then data collection coverage is comprehensive, but data reliability and accuracy deteriorate due to overlapping and redundant entries
Solution Approach 1:
The patent introduces a token as an intermediary entity that mediates between the operator and machine data collection processes. The token acts as a mediator that transfers ownership of data collection responsibility, ensuring that only one source (either operator or machine) provides time-stamp data at any given moment, thereby eliminating redundant and overlapping entries while maintaining comprehensive coverage.
Solution Approach 2:
The system implements self-service mechanisms where the token automatically transfers ownership between operator and machine based on meta-status transitions. This self-managing approach ensures that data collection responsibility dynamically shifts without human intervention, preventing duplicate data entry while maintaining continuous and reliable monitoring of manufacturing operations.
2Measurement precision
If manual data entry is required for OLE and OEE calculation, then data accuracy can be verified, but productivity and time efficiency deteriorate
Solution Approach 1:
The system enables automatic data collection where machines and operators self-report their status data to the MES system without requiring manual verification. The token-based mechanism ensures that the appropriate source automatically provides accurate data based on the current meta-status, eliminating the need for manual data entry while maintaining data accuracy through the structured ownership transfer process.
Solution Approach 2:
The system implements feedback mechanisms where the MES system continuously monitors meta-status transitions and automatically triggers data collection from the appropriate source. This closed-loop feedback ensures that accurate data is collected in real-time based on actual operational states, eliminating the need for manual verification while maintaining measurement precision.
3Reliability
If the system distinguishes between operating and booked meta-statuses, then data ownership clarity improves, but system complexity increases
Solution Approach 1:
The patent segments the machine status into two distinct meta-statuses: operating and booked. This segmentation provides clear boundaries for data ownership - when the machine is in operating meta-status, the machine owns the data; when in booked meta-status, the operator owns the data. This simple binary segmentation clarifies data ownership without requiring complex multi-level classifications.
Solution Approach 2:
The system dynamically adjusts data ownership based on the current meta-status. The token automatically transfers between operator and machine according to meta-status transitions, creating a flexible and adaptive data collection system. This dynamic approach simplifies the overall system by using a single status-based rule rather than multiple complex data collection pathways.
4Measurement precision
If post-acquisition data processing is performed to correct inaccuracies, then data quality improves, but time loss and processing overhead increase
Solution Approach 1:
The system performs preliminary action by establishing clear data ownership rules before data collection begins. The token-based mechanism pre-defines which source (operator or machine) should provide data based on meta-status, preventing inaccuracies and redundancies at the source rather than requiring corrective processing afterward. This proactive approach ensures data quality is maintained throughout the collection process without needing post-acquisition correction.
Data Source
AI summary
A method and a system collect via a MES, time-stamps of working-statuses of machines and operators, called also actors, for a calculation of a time-dependant component of OLE and OEE indicators in a manufacturing task. The method includes providing a token for assigning to an actor the responsibility of data provision, and defining two meta-statuses for a machine in a task, called operating and booked meta-status respectively. Each meta-status groups a set of machine statuses. A machine is defined to be in an operating meta-status when the machine is in a status engaged in the task and it is able to know and notify its own status. A machine is defined to be in a booked meta-status when the machine is in a status engaged in the task and it is unable to notify its own status.