Dynamic Pull Planning Engine for Construction Schedules
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Solution Overview
Problem
Existing software technologies for managing construction project schedules fail to address the burden of manually monitoring master schedules for updates and the challenges of accurately determining inter-task dependencies and identifying tasks that can be pulled up during pull planning sessions, leading to potential delays and increased costs.
Innovation Solution
The development of software technology that includes a pull planning software engine applying predictive analytics to master schedules to identify tasks that can be commenced earlier than scheduled, along with a software tool for facilitating user interaction, approval, and coordination of these tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual monitoring of master schedules is performed, then schedule updates can be identified, but labor burden and time consumption increase
Solution Approach 1:
The system automatically monitors master schedules and identifies schedule updates without requiring manual intervention. The computing system continuously tracks schedule changes, task dependencies, and resource allocations, enabling self-service schedule management that reduces manual monitoring time while maintaining reliable update identification.
Solution Approach 2:
Manual mechanical processes of schedule monitoring are replaced with automated computing systems. The system uses software algorithms to process schedule data, identify updates, and determine task dependencies, substituting human labor with automated digital mechanisms that achieve the same functional outcomes more efficiently.
2Measurement precision
If manual determination of inter-task dependencies is performed, then accurate scheduling can be achieved, but complexity and time consumption increase
Solution Approach 1:
Manual analysis of task dependencies is replaced with automated computing algorithms that systematically evaluate relationships between tasks. The system processes schedule data to automatically determine dependencies, reducing the complexity burden on users while maintaining precise dependency identification through computational methods.
Solution Approach 2:
The system continuously monitors schedule updates and feedback loops to dynamically determine task dependencies. By analyzing real-time schedule changes and their impacts on dependent tasks, the system automatically adjusts dependency mappings, ensuring accurate scheduling decisions without manual intervention in the complex analysis process.
3Productivity
If pull planning sessions are conducted manually, then task optimization can be achieved, but productivity and efficiency decrease
Solution Approach 1:
The system performs self-service pull planning by automatically identifying tasks that can be pulled up and optimizing schedules without requiring extensive manual planning sessions. The computing system analyzes schedule data, determines optimal task sequencing, and generates revised schedules that maintain or improve productivity while reducing the time needed for manual planning interventions.
Data Source
AI summary
Techniques for dynamic pull planning involve (i) determining an update to a master schedule for a construction project that comprises tasks having respective scheduled start dates, (ii) executing a machine learning model that has been trained with historical construction project schedule data and thereby identifying candidate tasks each available for commencement earlier than its scheduled start date, (iii) causing a client station to display each identified task, its scheduled start date, a respective new start date, and an impact on the master schedule if the task is commenced on the respective new start date, (iv) receiving user input indicating selection of a given task that is to be commenced earlier than its scheduled start date, (v) and causing transmission of a notification to a party responsible for completing the given task indicating that the given task has been nominated for earlier commencement and requesting approval for the earlier commencement.