The slide's title is "Demand Forecasting and Financial Impact at Unilever," highlighting the company's focus on predictive analytics and their economic implications. Its subtitle emphasizes AI-driven optimization to address the bullwhip effect, aiming for a €3.3 billion reduction in waste.

Demand Forecasting and Financial Impact at Unilever

AI-Driven Optimization: Tackling Bullwhip Effect for €3.3B Waste Reduction

Source: Unilever Executive Presentation

The presentation agenda outlines key topics starting with the Bullwhip Effect and €3.3B annual waste in Unilever's supply chains, followed by financial impacts including €1.96B cash release potential from safety stock optimization. It then covers the Ice Cream category's demand forecasting successes, case studies like Sweden accuracy improvements and Amazon Prime Day collaborations, and concludes with AI ROI demonstrations and key takeaways.

Presentation Agenda

1. Bullwhip Effect & Waste

Exploring supply chain volatility and €3.3B annual waste in Unilever categories.

2. Financial Impacts

Analyzing €1.96B cash release potential through safety stock optimization.

3. Ice Cream Focus

Highlighting star performance in demand forecasting for Ice Cream category.

4. Case Studies

Sweden accuracy improvements and Amazon Prime Day collaboration successes.

5. AI ROI & Conclusion

Demonstrating AI forecasting ROI and key takeaways for Unilever.

This section header slide introduces "The Bullwhip Effect" as section 02, highlighting how demand variability intensifies across supply chain stages. It focuses on Unilever's product categories, including ice cream, foods, beauty, and personal care, to illustrate the phenomenon.

The Bullwhip Effect in Supply Chains

02

The Bullwhip Effect

Amplifying Demand Variability Across Unilever's Categories: Ice Cream, Foods, Beauty & Personal Care

The Bullwhip Effect occurs when order batching amplifies small demand fluctuations upstream in the supply chain, while promotions distort signals and create artificial spikes. This leads to stockouts, excess inventory, and higher costs, as seen in Unilever's exposure across categories, particularly Ice Cream.

Understanding the Bullwhip Effect

• Order batching amplifies small demand fluctuations upstream
• Promotions distort signals, creating artificial spikes
• Leads to stockouts, excess inventory, and higher costs
• Unilever exposed across categories, notably Ice Cream

Demand forecasting inefficiencies result in €3.3 billion in annual waste across the sector. Ice cream accounts for the largest share at 25%, followed by the foods category at 30%.

Financial Toll: €3.3B in Waste

• €3.3B: Annual Waste

From demand forecasting inefficiencies

• 25%: Ice Cream Share

Largest category contributor

• 30%: Foods Category

Second highest waste area

This section header slide, titled "Ice Cream: The Star Performer" and numbered 05, highlights ice cream as a standout product in the context of market analysis. Its subtitle emphasizes strategies for leveraging high volatility and optimization to address seasonal demand fluctuations.

Ice Cream: The Star Performer

05

Ice Cream: The Star Performer

Unlocking High Volatility and Optimization in Seasonal Demand

The Ice Cream Category Deep Dive slide highlights key challenges, including highly seasonal demand with summer peaks and promotion-driven spikes that amplify the Bullwhip Effect, leading to €3.3B in waste and difficulties in safety stock optimization. On the opportunities side, it discusses how AI forecasting could improve accuracy from 75% to 95%, releasing €1.96B in cash through better inventory management, supported by a 20% accuracy gain in a Sweden case study and real-time demand insights from Amazon Prime Day collaborations.

Ice Cream Category Deep Dive

In 2019, before AI adoption, Sweden's ice cream demand forecasting achieved 75% accuracy despite Bullwhip Effect challenges. From 2020 onward, Unilever launched an AI-based system that boosted accuracy to 92% by 2021, reduced waste by 15%, and contributed to a €1.96B cash release across categories.

Sweden Ice Cream Case Study

2019: Pre-AI Forecasting Era Achieved 75% accuracy in demand forecasting for Swedish ice cream sales amid Bullwhip Effect challenges. 2020: AI Implementation Launch Deployed AI-based forecasting system to optimize safety stock and reduce waste in Unilever's ice cream category. 2021: Post-Implementation Success Boosted accuracy to 92%, cut waste by 15%, contributing to €1.96B cash release across categories.

The Amazon Prime Day Success slide highlights a collaborative forecasting effort that achieved 95% accuracy in predicting demand spikes for the Unilever Ice Cream category. It also optimized inventory to reduce stockouts by 40% during the event peak, unlocking €15M in working capital efficiency.

Amazon Prime Day Success

!Image

• Collaborative forecasting achieved 95% demand spike accuracy
• Optimized inventory for Unilever Ice Cream category
• Reduced stockouts by 40% during event peak
• Unlocked €15M in working capital efficiency

Source: Image from Wikipedia article "Amazon Prime"

The slide highlights a €1.96 billion cash release achieved through safety stock optimization, with the ice cream category contributing a major €500 million in savings. Additionally, it notes €3.3 billion in annual waste avoided by reducing the bullwhip effect's impact.

Safety Stock Optimization: €1.96B Cash Release

• €1.96B: Total Cash Release

From safety stock optimization

• €500M: Ice Cream Savings

Category's major contribution

• €3.3B: Annual Waste Avoided

Reducing Bullwhip Effect impact Source: Unilever Internal Data

The slide highlights the impressive ROI from AI-based forecasting, achieving a 20x return through demand optimization, with a specific example of a €10M investment yielding €200M in annual savings. It also notes a 30% reduction in forecasting errors across categories like Ice Cream, Foods, Beauty, and Personal Care, enabled by scalable implementation.

ROI of AI-Based Forecasting

• Achieved 20x ROI via AI-driven demand forecasting optimization
• Reduced forecasting errors by 30% across categories
• Scalable implementation for Ice Cream, Foods, Beauty, and Personal Care
• Example: €10M investment yields €200M annual savings (20x return)

Unilever should leverage AI-powered forecasting to unlock €1.96 billion in value by optimizing safety stock and mitigating the Bullwhip Effect. The company plans to expand the successful Ice Cream pilot to Foods, Beauty & Personal Care, and other categories for enhanced ROI and operational excellence, under the subtitle "Innovate Boldly, Lead the Future," featuring Steve Jobs' quote on innovation.

Next Steps for Unilever

Leverage AI-powered forecasting to unlock €1.96B in value through safety stock optimization and Bullwhip Effect mitigation.

Expand the successful Ice Cream pilot across Foods, Beauty & Personal Care, and other categories to drive ROI and operational excellence.

"Innovation is the ability to see change as an opportunity - not a threat." - Steve Jobs

Innovate Boldly, Lead the Future.