A leading SEA ecommerce brand faced a common foe: inaccurate inventory forecasts. Their reliance on manual manual demand forecasting led to data complexities, human errors, and a lack of integration with other systems. This resulted in disruptions throughout their supply chain, creating a constant struggle to maintain optimal inventory levels.
In collaboration with the brand, we implemented an AI-powered demand forecasting model to:
- Predict future customer demand for optimized inventory management.
- Empower the brand to make data-driven decisions regarding their supply chain operations.
What Was Done
We developed a multi-model machine learning approach to the AI demand forecasting model. The AI model was trained on a vast dataset of historical information, including:
- SKU sales data
- Base costs of products
- Seasonality (holidays, promotions & campaigns, and pay days)
The model could predict daily SKU sales for up to 1 year into the future, allowing for significantly improved inventory planning.
AI demand forecasting beats human forecast for all SKU classes.
Benefits Seen
The AI model consistently outperformed human forecasts by up to 50% across all SKU categories. This translated to over $600,000 in monthly inventory cost savings, a testament to the model’s effectiveness. But the benefits went beyond just the bottom line. With accurate forecasts, the brand achieved:
- Reduced stockouts and overstocking through optimized inventory levels.
- Streamlined supply chain: informed decisions on production, procurement, and logistics.
- Enhanced customer satisfaction: Right products, in stock, meeting customer needs.
This case study exemplifies the transformative power of AI-powered demand forecasting. Businesses that embrace high-value AI business cases can unlock a future of efficiency, cost savings, and improved customer satisfaction.
For more data & AI applications, visit us at: https://gradientxgroup.com/ai-applications/
