Dynamic Production Rescheduling via Event-Driven Simulation
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Solution Overview
Problem
In manufacturing, unexpected events such as production delays and equipment failures disrupt production schedules, leading to inefficiencies and difficulties in accurately estimating the impact of rescheduling, which can worsen production delays and hinder real-time evaluation of production efficiency.
Innovation Solution
A data analysis apparatus that includes an event occurrence setting module, an event detection timing setting module, a simulation executing processing module, and a KPI calculating module, which conducts simulations to estimate the effects of events on production schedules and calculates key performance indicators (KPIs) based on the detection timing of these events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If production schedules are created based on standard work times and equipment availability, then production planning efficiency is improved, but the ability to respond to unexpected events (such as equipment failures, material delays, or process interruptions) deteriorates, leading to schedule deviations and reduced production reliability
Solution Approach 1:
The patent implements dynamic rescheduling that automatically adjusts production schedules in real-time based on detected events. The system transitions from static pre-planned schedules to dynamic adaptive scheduling, where the schedule is continuously updated according to actual production status, equipment availability, and event occurrences, thereby maintaining both planning efficiency and schedule reliability
Solution Approach 2:
The system incorporates real-time feedback mechanisms through event detection modules that monitor production processes, equipment status, and material flow. This feedback enables the rescheduling algorithm to respond to actual conditions rather than relying solely on predetermined plans, improving the system's ability to handle unexpected events while maintaining overall productivity
2Device complexity
If rescheduling is performed manually or without automated systems, then system complexity is reduced, but the time required to detect events and implement rescheduling increases, worsening production delays
Solution Approach 1:
The patent implements an automated self-service rescheduling system where the scheduling algorithm autonomously detects events, calculates optimal reschedules, and updates production plans without requiring manual intervention. This self-service capability significantly reduces rescheduling response time while the modular architecture keeps system complexity manageable through automated routine operations
Solution Approach 2:
The system replaces manual mechanical scheduling processes with automated computational algorithms. Event detection, schedule calculation, and rescheduling execution are performed by computer-based systems rather than human operators, dramatically reducing response time while the use of standard algorithms and modular design prevents excessive complexity
3Device complexity
If production events are not detected in real-time, then detection system complexity is reduced, but the accuracy of production status monitoring deteriorates, making it impossible to evaluate rescheduling effects accurately
Solution Approach 1:
The patent implements a multi-functional event detection system that monitors multiple production parameters (equipment status, material flow, process completion, anomalies) through integrated sensors and data collection points. This universal detection approach provides comprehensive production status monitoring while using standardized sensor interfaces and data protocols to manage system complexity
Data Source
AI summary
There is provided a data analysis apparatus, comprising an event occurrence setting module configured to cause a prescribed event to occur in a simulation for a work order that includes a process at which the prescribed event is to occur an event occurrence detection timing setting module configured to store an event occurrence detection timing indicating a time period between an occurrence of an event and detection of the event, a simulation executing processing module configured to execute a simulation when an occurrence of the event is detected, the simulation executing processing module being configured to execute a simulation that reflects an effect on the process when the event is addressed in accordance with the event occurrence detection timing recorded in the storage module, and a KPI calculating module configured to calculate a KPI of the process for the event occurrence detection timing, based on results of the simulation.