Prescriptive analytics for human resource planning in the professional services industry

In this paper, we examine human resource planning decisions made at firms that sell contract-based consulting projects. High levels of uncertainty in deals and revenue forecasts make it challenging for consulting firms to hire the right people to staff their projects. We present a human resource planning model using concepts from robust optimization to allow companies to dynamically make hiring decisions that maximize profit while remaining as flexible as possible, and demonstrate potential profit improvements through simulation on real data.