AI-Driven Forecasting

AI Use Case Analysis: Danone’s AI-Driven Demand Forecasting

Why: The Need for a New Approach

Danone faced persistent challenges in accurately forecasting demand for its fresh dairy products, which have short shelf lives and highly volatile sales patterns. Frequent promotions, media campaigns, and shifting consumer preferences made traditional forecasting methods unreliable, leading to excess inventory, lost sales, and inefficient cross-functional planning.

What: The AI Tools and Their Purpose

To address these challenges, Danone implemented a machine learning-based demand forecasting system. This AI solution integrated diverse data sources—including historical sales, promotional calendars, marketing events, and external factors like weather and macroeconomic indicators—to generate highly granular forecasts at the product, channel, and store level.

How: The New Workflow

Danone deployed an AI solution that uses machine learning algorithms to analyze complex relationships among various demand drivers. The model focuses on:

• Using ML to assess historical trade promotions and media activities data.

• Automatically adjusting to new data (internal and external) to produce millions of forecasts weekly.

• Coordinate planning and rapid scenario analysis across departments like sales, marketing, supply chain, and finance.

So What: The Takeaways

Danone’s AI-driven approach reduced forecast error by 20%, lost sales by 30%, and product obsolescence by 30%, while improving promotion ROI by 10 points. Cross-functional demand planners saw their workload cut in half, and benefited from faster, more accurate, and more collaborative planning.

Skill Integration: Building a Demand Forecasting AI Workflow

Deploying a specialized AI application for forecasting might be too complex for most people. As would other firms focusing on adopting AI systems, the key is to build upon existing data and platforms, and experiment on creating AI-enhanced workflows.

Instead of a practical guide that might not be appropriate for this scenario, it is better to give conceptual workflows cross-functional workers can build upon.

AI-Driven Demand Forecasting Idea

Step 1: Define Your Forecasting Objective

• Choose a product, service, or business metric you want to forecast (e.g., weekly sales of a product, event attendance, or website traffic).

• Clearly state the business question: “How many units will we need next month?” or “What will demand look like during the next promotion?”

Step 2: Gather and Prepare Data

• Collect historical data: Download past sales, orders, or usage data from your CRM, ERP, or spreadsheets.

• Supplement with external data: Add relevant factors like promotions, holidays, weather, or economic indicators (many public datasets are available).

• Clean and format: Ensure your data is in a tidy table (CSV/Excel), with clear date columns and no missing values.

Step 3: Choose an Accessible AI Tool

• No-code/low-code options:

  • Microsoft Excel with AI plugins (e.g., Forecast Sheet, Power BI’s AI visuals)

  • Google Sheets with Vertex AI Forecast Add-on

  • Tableau with Einstein Discovery

  • Open-source tools: Facebook Prophet (can be run in Google Colab or Jupyter Notebook)

  • Cloud platforms: Amazon Forecast, Google Vertex AI, C3 AI Demand Forecasting

Step 4: Build and Train Your Forecast Model

• Upload your data to the chosen tool.

• Select a forecasting model: Most tools offer automated model selection (e.g., time series, regression, or ML-based).

• Configure model settings: Set the forecast horizon (e.g., next 4 weeks), frequency (daily/weekly), and include any special events or promotions as variables.

• Run the model: Let the AI analyze patterns and generate forecasts.

Step 5: Validate and Interpret Results

• Compare predictions to actuals: Use holdout (test) data to check forecast accuracy.

• Visualize results: Most tools provide charts showing predicted vs. actual demand.

• Analyze drivers: Many platforms offer explanations of key factors influencing the forecast.

Step 6: Scenario Planning and Experimentation

• Simulate “what-if” scenarios: Adjust variables (e.g., add a promotion, change price, simulate a weather event) to see how forecasts change.

• Document insights: Note which factors most impact demand and how AI models respond to different scenarios.

Step 7: Share and Reflect

• Share findings: Present your results and insights to your team or manager as a proof of concept.

• Reflect on impact.

Tools You Can Try Today

Tips for Knowledge Workers

• Start with a small, non-critical dataset to avoid compliance issues.

• Use public or anonymized data if company policy restricts access to internal data.

• Document your process and results to build a business case for future adoption.

• Explore free trials and cloud credits to minimize costs.

• Constant experimentation using various tools and leverage LLMs for practical help.