Business Case Story flow for Data Science Managers

When a project sponsor assigns a budget for a data science solution, they’re effectively betting that your solution will generate value — let’s say, “X” dollars. Later, they’ll ask if “X” dollars have been realized. A business case formalizes this, highlighting how the solution will improve decision-making and generate value beyond the current state. It also shows alternatives, their limitations, and confidence in outcomes. As a data science manager it’s important you familiarize yourself with the structure of a business case and how it ties back to the solution you want to build

Financial Outcomes Sponsors Look For:

1. EBITDA Impact (Profitability):

• Example: A Pricing Optimization model can increase prices by 5%, resulting in a $1.5 million EBITDA increase, assuming a minimal loss in sales volume.
• Verbalization: If prices increase by 5%, and we lose only 1% of sales, EBITDA increases by 15%.

1. Working Capital Reduction (Cash Flow Improvement):

• Example: Demand Forecasting improvements reduce inventory by $1 million, freeing up cash from $5M to $4M, improving liquidity.
• Verbalization: By reducing excess stock through more accurate forecasting, $1 million in working capital is freed for other business operations.

1. Revenue Growth (Top-line Growth):

• Example: A Lead Generation model improves conversion rates, leading to an extra $2 million in annual revenue.
• Verbalization: The new model helps convert 4 additional customers, bringing $2 million in revenue growth

To make this real lets use an example as it relates to a Demand Forecasting Problem

Key Financial Metrics Example:

• Current EBITDA: $15 million
• Inventory Value: $12 million
• Annual Holding Costs: $3 million (25% of inventory value)
• Working Capital: $25 million

Opportunity Assessment:

• Reducing inventory from $12M to $10M would lead to:
• EBITDA increase: From $15M to $15.5M (due to $500K reduction in holding costs)
• Inventory Value: $10M
• Holding Costs: Decrease to $2.5M annually
• Working Capital: Reduced from $25M to $23M

This opportunity arises because current inventory levels are high, despite 98% service levels (order fill rate) and minimal stockout costs (<$50K annually). Additionally, inventory turnover is low (3 vs. industry benchmark of 6), and days of inventory on hand are high (120 vs. 60).

Demand Forecasting Insights:

• The model shows a 30% WAPE and 12% positive bias (indicating over-forecasting).
• SKUs with accurate forecasts show better inventory turnover and lower days on hand.
• A tighter procurement process ensures safety stock buffers supply variance, meaning further reductions must come from improved demand forecasts.

Pilot Approach:

We recommend a 3–month pilot targeting a 10% WAPE improvement across key customers which could reduce inventory by 250K immediately. If scalable, we believe within a year we can generate annual savings of 500K through a demand forecast with a 20% WAPE. Post this, with another 500K possible through adjusted safety stock calculations.