ML-BASED RESOURCE PLANNING SOLUTION

A leading Big4 consulting firm needed to modernize their enterprise resource planning process, which relied heavily on manual forecasting and resulted in suboptimal utilization rates. Our team designed and delivered an ML-based solution that transformed resource allocation from a reactive burden into a proactive strategic advantage.

The client's existing resource planning approach created several critical bottlenecks:

• Manual forecasting consumed 15+ hours per week for each resource planner
• Allocation accuracy averaged only 65%, leading to frequent mid-project adjustments
• Limited visibility into capacity prevented leadership from confidently pursuing new opportunities
• Reactive staffing decisions resulted in skill mismatches and project delays

We led this initiative with a product-driven mindset, focusing on sustainable business value rather than just technical delivery:

Discovery & Product Design

• Interviewed stakeholders across project management, resource planning, and executive leadership
• Analyzed 3+ years of historical resource allocation data to identify patterns and predictive signals
• Designed product vision balancing technical feasibility with business impact
• Secured executive buy-in through business-focused presentations that translated ML capabilities into ROI

Proof of Concept

• Led cross-functional team of data scientists, ML engineers, and integration specialists
• Defined clear success criteria for prediction accuracy and integration feasibility
• Coordinated model training and validation using historical project data
• Demonstrated 35% improvement in prediction accuracy, exceeding stakeholder expectations  

Production Development & Integration

• Architected solution to meet enterprise scalability, security, and compliance requirements
• Integrated seamlessly with existing project management, HR, and financial systems
• Designed intuitive interface for non-technical resource planners
• Managed comprehensive testing including functional, performance, and UAT
• Delivered on schedule with zero production incidents

Deployment & Change Management

• Developed comprehensive training materials and conducted user workshops
• Established monitoring dashboards to track model performance and business impact
• Created framework for continuous model improvement as business conditions evolved

• Strategic confidence: Leadership gained real-time capacity visibility, enabling aggressive pursuit of new opportunities

• Operational efficiency: resource planners shifted from manual data crunching to strategic decision-making

• Data-driven culture: organization moved from instinct-based planning to evidence-based forecasting

• Sustained value: solution continues to drive value 2+ years post-deployment with continuous improvements

• Product mindset: Designed for long-term business value, not just technical delivery

• Business-first ML expertise: Combined deep technical knowledge with strategic business acumen

• Stakeholder alignment: Secured executive buy-in through clear ROI communication

• Practical integration: Built solution within existing workflows, minimizing disruption

• End-to-end ownership: From concept through deployment to continuous improvement

Machine Learning & AI • Data & Analytics • Enterprise Integration • Product Ownership