Agile Delivery Platform Optimizing Release Planning
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Solution Overview
Problem
Existing agile delivery management systems lack the capability to intuitively plan software releases considering various parameters such as scope, timelines, and available resources, making it difficult for large enterprises to scale agile practices effectively.
Innovation Solution
A system and method for managing agile delivery that includes an input module for providing visions and team velocities, a vision board for displaying goals with key result metrics, a machine learning module for optimizing team resource allocation, and an automated deployment pipeline for managing releases, enabling optimal release planning and delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If enterprises adopt agile practices across the organization to deliver solutions faster with better quality, then the feedback loop is shortened and delivery speed improves, but large organizations struggle to scale agile at the enterprise level
Solution Approach 1:
The system segments the enterprise into multiple autonomous agile teams, each capable of independent delivery. The enterprise-level system orchestrates these teams through standardized interfaces (APIs, webhooks) while maintaining their autonomy, allowing scaling without proportionally increasing complexity.
Solution Approach 2:
The patent introduces an enterprise agile management platform as an intermediary layer between individual agile teams and enterprise leadership. This mediator provides centralized visibility, coordination, and resource allocation while preserving team autonomy, enabling scalable agile implementation across large organizations.
2Ease of operation
If enterprises implement agile delivery management systems to plan releases considering scope, timelines, and resources, then release planning capability improves, but existing systems lack intuitive planning tools and robust feature sets
Solution Approach 1:
The system provides a unified enterprise agile management platform that performs multiple functions: release planning, team velocity tracking, resource allocation, dependency management, and automated deployment. This multi-functional approach consolidates various planning needs into a single intuitive interface, improving ease of operation while maintaining comprehensive functionality.
Solution Approach 2:
The system dynamically adjusts planning parameters such as team velocity, sprint duration, and resource allocation based on historical data and current project needs. This adaptive parameter adjustment enables intuitive release planning that automatically optimizes scope, timelines, and resources without requiring complex manual configuration.
3Productivity
If machine learning methods are used to match teams with requirements for optimal release, then resource allocation optimization improves, but the system requires sophisticated algorithms and data processing
Solution Approach 1:
The system implements self-service machine learning where the algorithm automatically learns from historical team performance data and continuously optimizes resource allocation without requiring manual intervention. The system self-adjusts matching parameters based on accumulated data, improving resource allocation efficiency while keeping the user interface simple and intuitive.
Solution Approach 2:
The patent incorporates continuous feedback loops where the machine learning system monitors actual team performance against predictions, learns from discrepancies, and refines its allocation recommendations. This feedback mechanism enables the system to improve resource allocation efficiency over time while maintaining relatively simple underlying algorithms that adapt rather than require constant complexity increases.
Data Source
AI summary
In an agile delivery program, hundreds of team members are organised into multiple agile teams work in a synchronized manner, to build a product. Normally, they are geographically distributed teams need to have the infrastructure and tools, to manage their agile delivery seamlessly. In addition to team, a product backlog, and sprint duration also need to be managed the simultaneously. A system and method for managing a program in an agile delivery environment using a single integrated platform has been described. The method involves providing a set of visions to each of the members working in the program. The set of goals, the teams and sprint duration are then provided. The profiling and machine learning techniques are then performed on these parameters to generate an optimal release plan for the release of the product.