Clarkston Consulting recently partnered with a client on a P&L profitability simulation project. The client is a family owned food company that was ready to advance their analytical capabilities across the organization. They engaged Clarkston to kickstart their analytics journey. To activate their analytics program, we first developed a clear strategy that defines the overall vision, path, opportunities, and needs of the organization. Then, we guided their team through an entire end-to-end predictive analytics Proof of Value (POV) project to establish a quick win.

The pilot project was to build a customer profitability simulator. There was interest across the finance, sales and marketing, and supply chain teams in understanding the impact of decisions on the whole profit and loss (P&L) sheet. This simulator gave analysts the ability to simulate changes in trade spend or deductions and see their effect on the bottom line. Situation modeling allowed the client to reimagine profitability through customer and product lenses.

Download the Full P&L Profitability Simulation Analytics Tool Case Study

The primary objectives for this team included reimagining customer and product profitability and cost-to-serve through data storytelling and score carding, enabling analysts to view profitability across retail customers and across products, and aligning processes, skills, and toolsets outlined in the strategic roadmap and identify any organizational gaps to address. for the P&L profitability simulation analytics tool project.

This resulted in the team using machine learning to predict cases sold by joining elements form the P&L like trade dollars, returns, and deductions with sales history and master data. The team used the predicted values for cases sold to calculate the full _&L statement, and integrated with Power BI to visualize the outcomes using scorecards. The key benefits for this project included identifying opportunities in product mix and volume/margin analysis across customers by connecting sales and supply chain cost information. This allowed analysts to take action on the optimal scenarios to maximize revenue or margin or minimize costs, depending on the guiding strategy.

Download the full P&L Profitability Simulation Analytics Tool case study here.

Contributions by Maggie Seeds