Background

Notino, a leading online beauty retailer in Europe, relied heavily on its internal purchasing tool to maintain optimal stock levels. This system was crucial for balancing inventory across various product lines and managing seasonal demand fluctuations.

The Challenge

Despite the sophisticated tool, there were unexplained inconsistencies in stock levels, particularly during high-demand periods like Black Friday and the post-holiday season. These inconsistencies led to either stockouts during peak times or excess inventory during slower periods.

My Approach

1. Gaining Access:
   • Despite initial resistance from the development team, I persistently advocated for access to the ordering algorithm, emphasizing the potential business impact of understanding it thoroughly.
2. Algorithm Analysis:
   • Once granted access, I conducted a meticulous examination of the inventory ordering formula.
3. Identifying the Flaw:
   • Through careful analysis, I discovered a critical error in the algorithm’s logic: it was calculating order quantities based on current demand rather than projected demand at the time of delivery.
4. Impact Assessment:
   • I modeled the implications of this flaw, focusing on two key scenarios: a) Black Friday orders with a 2-month lead time b) Holiday season orders for the first quarter
5. Documentation and Reporting:
   • Prepared a comprehensive report detailing the flaw, its implications, and potential solutions.

Key Findings

1. Algorithm Flaw:
   • The inventory ordering system was using current demand data instead of projected future demand at the expected delivery date.
2. Black Friday Scenario:
   • Orders placed two months before Black Friday were significantly underestimated, leading to potential stockouts during the crucial sales period.
3. Post-Holiday Scenario:
   • Orders placed during the holiday season for Q1 delivery were overestimated, potentially resulting in excess inventory and subsequent discounting.
4. Financial Implications:
   • Potential for significant revenue loss due to stockouts during high-demand periods.
   • Risk of margin erosion from discounting excess inventory during slower periods.
5. Customer Satisfaction Risk:
   • Stockouts during key shopping events like Black Friday could lead to decreased customer satisfaction and lost sales.

Results and Potential Impact

1. Awareness Creation:
   • Brought a critical system flaw to the attention of senior management.
2. Potential for Improved Inventory Management:
   • If implemented, the fix would lead to more accurate stock levels aligned with actual demand patterns.
3. Financial Benefits:
   • Potential for increased sales during peak periods by avoiding stockouts.
   • Reduction in excess inventory, minimizing the need for heavy discounting.
4. Enhanced Forecasting:
   • The discovery paved the way for more sophisticated demand forecasting methodologies.
5. Process Improvement:
   • Highlighted the need for regular audits of critical business algorithms.

Key Learnings

1. Persistence Pays Off: Overcoming initial resistance to access crucial information can lead to significant discoveries.
2. Holistic Understanding: Sometimes, solving inventory problems requires looking beyond just the numbers to understand the underlying logic.
3. Anticipatory Analysis: In e-commerce, it’s crucial to consider future demand patterns, not just current trends.
4. Cross-functional Collaboration: Bridging the gap between technical teams (dev) and business operations is essential for identifying and solving complex issues.
5. Continuous Improvement: Even established systems need regular review and potential updates to remain effective.