AI/ML-Based Effort Estimation Solution for Project Team

Our AI/ML-based solution addresses the challenge of accurately estimating task effort for project managers. By leveraging regression models, NLP transformers, and neural networks, it considers factors like team skills, experience, industry standards, and task criticality.

The solution integrates with existing project management tools, providing real-time, precise effort estimations. Utilizing historical project data, employee profiles, and industry benchmarks, our system ensures data-driven, accurate predictions. This enhances project planning, execution, and resource allocation, improving productivity and successful project outcomes across various industries.

Industry : Manufacturing

Manpower : 50+

Location : India

Quality Index = 4.9

C S Index = 4.6

AI Models and Techniques Used:

01. Supervised Learning Models

Regression Models (e.g., Linear Regression, Ridge Regression): To predict continuous effort estimation values.

Tree-based Models (e.g., Random Forest, Gradient Boosting): To handle complex interactions between multiple factors affecting effort estimation.

02. Natural Language Processing (NLP)

Transformers (e.g., BERT, GPT): For analyzing and understanding task descriptions and mapping them to effort estimation metrics.

03. Neural Networks

Feedforward Neural Networks: For combining various inputs such as skills, experience, and task criticality to produce accurate effort estimations.

Recurrent Neural Networks (RNNs): To consider sequential dependencies in task assignments and project timelines.

04. Cloud-Based Infrastructure

Ensures scalability and accessibility.

Technical Solutions

Data Preprocessing Pipelines: Clean, normalize, and preprocess data to ensure high-quality inputs for the AI models.
Model Training and Evaluation Frameworks: Utilize frameworks such as TensorFlow and PyTorch to develop, train, and evaluate the performance of AI models.
API Integration: Seamlessly integrate the AI models into existing project management tools through robust APIs, allowing project managers to receive real-time effort estimations.
User-Friendly Dashboard: An intuitive dashboard for project managers to input task details and receive effort estimations, along with detailed insights and suggestions for resource allocation.

Data Sources

Historical Project Data: Past project records, including task descriptions, assigned personnel, and actual efforts expended.
Employee Profiles: Information on team members' skills, experience, and past performance metrics.
Industry Standards and Benchmarks: Data from industry reports and standards to calibrate effort estimations against recognized benchmarks.
Company-Specific Standards: Internal documentation and guidelines that outline task complexities, criticality levels, and standard procedures.