Modular Service Zone Preparation for Predictive Repair Scheduling
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Solution Overview
Problem
The manual arrangement of tools, machines, and parts at service centers for maintenance and repairs is time-consuming and inefficient, requiring significant employee effort and leading to prolonged service times and frequent rearrangements of service zones for different types of physical assets.
Innovation Solution
An AI-enabled system using digital twin models and classification algorithms predictsively automates the configuration of modular service zones, optimizing the arrangement of tools, machines, and parts based on the maintenance profiles of physical assets, minimizing waiting times and maximizing the reuse of service zones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual arrangement of tools, machines, and parts is used at service centers, then employees can flexibly adapt to different service needs, but the process becomes time-consuming and inefficient with prolonged service times
Solution Approach 1:
The system performs preliminary actions by automatically arranging tools, machines, and parts in advance based on predicted service requirements. The proactive preparation system analyzes service requests and pre-configures service zones before assets arrive, eliminating the time-consuming manual arrangement process while maintaining flexibility through dynamic reconfiguration capabilities.
Solution Approach 2:
The patent replaces the manual mechanical arrangement system with an automated intelligent system. AI algorithms and robotic systems substitute human employees in the physical arrangement of equipment, achieving faster and more precise configuration while reducing service preparation time significantly.
2Adaptability or versatility
If service zones are frequently rearranged for different types of physical assets, then service versatility is maintained, but employee effort increases and efficiency decreases
Solution Approach 1:
The service zones are designed with dynamic characteristics, allowing automatic reconfiguration through robotic systems rather than manual intervention. The zones can adapt their layout and equipment arrangement dynamically based on real-time service requirements, maintaining versatility while improving efficiency through automated, rapid repositioning capabilities.
Solution Approach 2:
The system changes key parameters such as equipment position, service zone configuration, and resource allocation automatically based on asset type and service requirements. This parametric control enables versatile adaptation to different service scenarios while maintaining high productivity through systematic, algorithm-driven optimization.
3Manufacturing precision
If manual preparation of service zones is performed, then equipment can be arranged according to specific needs, but significant employee effort is required
Solution Approach 1:
The service center system performs self-service through automated intelligent algorithms that analyze service requests, determine optimal equipment arrangements, and execute the configuration automatically. The system serves itself by making decisions and performing actions without human intervention, achieving precise equipment arrangement while eliminating employee effort complexity.
Solution Approach 2:
An intermediary intelligent system acts as a mediator between service requests and physical equipment arrangement. This intermediary layer processes information, makes decisions, and coordinates robotic systems to achieve precise equipment positioning without direct human involvement in the physical arrangement process.
4Speed
If service zones are pre-configured for specific asset types, then service speed increases, but the ability to handle diverse asset types decreases
Solution Approach 1:
The service zones are designed with universal characteristics, where a single zone can be rapidly reconfigured to handle multiple asset types through automated robotic systems. The multi-functionality is achieved by having a core set of equipment that can be dynamically arranged and supplemented based on specific service needs, maintaining both speed and versatility.
Data Source
AI summary
Systems, methods and/or computer program products predictively automating configurations of modular service zones servicing physical assets, maximizing reuse of service zone(s) and optimizing time for servicing a plurality of physical assets. Digital twin models of physical assets are classified, and arranged into workflows for the service zones, sequencing services performed on physical assets arriving at service centers and preparing service zones based on types of services requested, the estimated time of arrival and similarities between classifications of different digital twins of physical assets. Based on sequences of the workflow, arrival times of physical assets and overlap between parts, tools, machines, etc., within various service zones, service center coordinates robotic systems to arrange service zones in a manner that minimizes waiting time between services, maximizes the number of physical assets repaired within a period of time and reduces rearrangement of service zones between the services provided to different physical assets.