Optimize Project Staffing with a Custom Resource Allocation Algorithm

Many small firms rely on dedicated scheduling tools like Float or Harvest Forecast. These platforms show who is assigned to what project right now, but they are fundamentally static calendars. They cannot analyze historical data to predict that a specific consultant is 90% likely to be pulled into high-priority support work two weeks into a new project, disrupting the entire timeline. The tools are for visualization, not for forecasting.

Consider a 15-person consulting firm staffing a new 3-month engagement. The project manager opens a master spreadsheet, finds an available senior consultant, and assigns them. What the spreadsheet does not show is that this consultant has spent 40% of her time on unscheduled, high-urgency tasks for a key client over the past six months. Two weeks into the new engagement, that key client has a predictable crisis, the consultant is reassigned, and the new project is already 5 days behind schedule.

The structural problem is that off-the-shelf tools are designed for scheduling, not learning. Their data model is a simple record of a person, a project, and a date range. A predictive system needs to model the relationships between a person, a task type, a project archetype, and the variance between estimated and actual hours. An off-the-shelf tool cannot learn that a specific developer consistently underestimates data migration tasks on new client onboarding projects by 25%.

The process would begin with an audit of your historical project data from HubSpot and time tracking data from QuickBooks Time. We would map out project types, team member skills, and task categories. You would receive a data dictionary defining every entity and a report showing the last 12 months of utilization, profitability per project type, and any data quality issues that require cleanup before modeling can begin.

The core of the system would be a Python-based forecasting model using the LightGBM library, hosted on AWS Lambda for pay-per-use cost efficiency. A nightly job would pull new time entries via the QuickBooks API, retrain the model on the latest data, and write updated forecasts to a Supabase database. A FastAPI service would serve this data to a simple dashboard. FastAPI is used for this because its async capabilities can pull data from multiple sources concurrently, keeping the entire data refresh process under 60 seconds.

The delivered system is a web-based dashboard showing each team member's forecasted utilization for the next 8 weeks. It also includes an API endpoint. A project manager can query this endpoint with a new project's skill requirements and receive a ranked list of the best-fit, available consultants. This tool integrates into your existing workflow, augmenting a manager's judgment with data, not replacing it.