ROSSMANN Sales Forecasting

Transforming Retail Operations with Predictive Analytics

SATOM OÜ

About Rossmann & the Dataset

Company Overview

🏢 Founded in 1972 by Dirk Rossmann

🇩🇪 Germany’s 2nd largest drugstore chain

💶 €10+ billion annual turnover

🛒 4,000+ stores across Europe

Dataset

Dataset Component

Details

Key Features

Training Data

1,017,209 sales records

Sales, Customers, Promotions

Test Data

41,088 predictions needed

Store, Date, Holiday info

Store Information

1,115 stores (856 active in test)

Competition, Store type, Assortment

Business Goal

Forecast 48 days of sales

Revenue optimization

The Challenge

Traditional Approach

  • Manual forecasting by store managers
  • Historical averages and gut feelings
  • Reactive inventory management
  • High waste and stockouts
  • Limited visibility across stores

Data-Driven Approach

  • 1+ Million sales records analyzed
  • Machine learning predictions with 88% accuracy
  • Proactive planning for all 856 stores
  • €2.1M annual ROI improvement
  • Real-time insights for better decisions

Bottom Line: We transformed 1,017,209 historical sales records into actionable predictions for smarter operations

Business Impact Overview

Weekly Sales Patterns: Plan Ahead

Action Items: Week 5 requires 25% more inventory and staff than average weeks

Store Performance

Support Where Needed

Action Items: Implement support programs for bottom 10% stores. Potentially, €50M+ annual impact!

Smart Inventory Management

Inventory Strategy: Stock to 80th percentile (€8.2k) for regular days, 90th percentile (€10.8k) for peak periods

Model Accuracy: Trust the Data

In the retail industry, the accuracy of the sales prediction between 80% and 95% is targeted often.

Implementation Roadmap

Implementation Phase

Timeline

Key Actions

Expected ROI

Status

Phase 1: Foundation

Month 1-2

• Deploy forecasting system
• Train 50 key managers
• Establish data pipelines

€0.2M

Ready

Phase 2: Pilot

Month 3-4

• Test with 100 stores
• Refine inventory rules
• Measure initial impact

€0.8M

Planned

Phase 3: Scale

Month 5-8

• Roll out to all 856 stores
• Full integration with POS
• Automated alerts

€2.1M

Planned

Phase 4: Optimize

Month 9-12

• Advanced analytics
• Personalized recommendations
• Continuous improvement

€3.5M+

Planned

Next Steps: Begin Phase 1 implementation next month - €200k immediate ROI expected. - This is the example plan. Any modifications are welcomed.

Key Takeaways for Management

Immediate Actions

  1. Deploy predictive model - 88% accuracy vs 40% historical average
  2. Support bottom 214 stores with targeted programs
  3. Optimize Week 5 operations - €40.4M peak revenue
  4. Implement smart inventory - 80/90 percentile rules

Expected Benefits

  • €2.1M annual ROI from better forecasting
  • 39.9M at-risk revenue now manageable
  • 25% reduction in stockouts and waste
  • Real-time visibility across all 856 stores

Questions & Discussion

Ready to transform your operations with data-driven insights?

“From 1 million data points to €2.1M value - that’s the power of analytics”