Construction Work Order Automation via Machine Learning
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Solution Overview
Problem
Current work order management systems in the construction industry are inefficient, prone to human errors, and require excessive manual monitoring and processing, leading to increased costs and overlooked orders due to the inability to accurately track materials, jobs, and payments.
Innovation Solution
A work order generation system utilizing data analytics and machine learning to aggregate data from multiple construction jobs, creating a database that automates the production, scheduling, and installation processes, with a template and information policy to validate and process message data, reducing the need for manual intervention and minimizing errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual work order processing is used, then flexibility in handling individual cases is maintained, but time consumption and human errors increase significantly
Solution Approach 1:
The patent replaces the mechanical manual system with an automated computer-based system that collects data from APIs, processes work orders, and generates reports automatically. This substitution eliminates manual data collection and input, directly resolving the contradiction between reliability and time consumption.
Solution Approach 2:
The system enables self-service by automatically gathering data from multiple sources, processing work order information, and generating outputs without requiring manual intervention. The automated workflow allows the system to serve itself, eliminating the need for human operators to collect and input data manually.
2Reliability
If more users are assigned to manually monitor and process work orders, then coverage and monitoring capability improve, but operational costs and complexity increase
Solution Approach 1:
The patent merges multiple functions (data collection, processing, tracking, and reporting) into a single integrated automated system. This consolidation eliminates the need for multiple users to perform separate manual tasks, resolving the contradiction between reliability and system complexity.
Solution Approach 2:
The automated system performs multiple functions simultaneously - collecting data from various APIs, processing work order information, tracking materials and jobs, and generating reports. This multi-functionality replaces the need for multiple specialized users, reducing operational complexity while maintaining comprehensive monitoring capability.
3Productivity
If manual work order management is used, then system simplicity is maintained, but productivity and speed of processing decrease
Solution Approach 1:
The system performs preliminary actions by pre-configuring API connections, establishing data collection parameters, and setting up automated processing workflows before actual work order processing begins. This preparation enables high-speed automated processing without requiring complex manual setup for each individual work order.
Solution Approach 2:
The patent replaces manual mechanical processing with automated computer-based processing that operates at higher speeds. The automated system can collect, process, and generate work order information much faster than manual operations, achieving improved productivity despite the added automation infrastructure.
4Measurement precision
If automated data collection from multiple sources is implemented, then data accuracy and completeness improve, but system integration complexity increases
Solution Approach 1:
The patent uses an intermediary automated system that connects to multiple data sources through standardized API interfaces. This intermediary layer handles the complexity of integrating different data sources, ensuring accurate and complete data collection while shielding users from the underlying integration complexity.
Data Source
AI summary
Systems and methods for a work order generation system for a construction project using data analytics and machine learning to aggregate data from multiple construction jobs of the same user and creating a database to automate the production, scheduling, and installation process. The work order system has a template, an information policy, and a work order manager. The information policy is configured to identify message data for a construction project including construction model data associated with a construction model and builder data. The message data is placed into the template using an information policy. A work order for performing a group of tasks for the construction project from the template uses a work order policy, enabling performing tasks for the construction project having a designated stock keeping unit for each construction model retained in a location specific database.