VERA FEBRIANI

Lead end-to-end demand planning and supply chain forecasting for large-scale portfolio covering 20,000+ active SKUs and 50,000+ total SKUs.

Key Achievements:

• Contributed to 60% YoY revenue growth across spare parts portfolio by improving forecast accuracy and inventory alignment.
• Optimized working capital exposure by balancing service level targets and inventory investment.
• Strengthened S&OP process by conducting structured forecast reviews with sales, operations, and warehouse teams to align projections with execution.
• Implemented data-driven forecasting approach leveraging SQL and Python to enhance demand visibility and decision accuracy.
• Monitored forecast accuracy and demand trends to minimize stock imbalances and improve operational efficiency.

Lead commercial planning and e-commerce performance optimization across marketplace platforms, integrating demand planning with revenue strategy.

Key Achievements:

• Increased ROAS by 15% (from 20 to 23) through campaign performance analysis and data-driven advertising optimization.
• Designed and executed peak-season promotional strategies, increasing traffic and driving incremental revenue growth.
• Optimized e-commerce fulfillment strategy by aligning forecast projections with inventory allocation, reducing stock imbalance while maintaining customer satisfaction.
• Led and developed a team of 2 e-commerce specialists, driving structured campaign optimization and improving digital sales performance across marketplace platforms.
• Analyzed consumer behavior and channel performance to refine promotional mix and margin optimization strategy.

Managed inventory control and demand analysis to support nationwide automotive spare parts distribution.

Key Achievements:

• Increased sales revenue by 100% over 2 years through forecast model improvement and optimized replenishment strategy.
• Maintained healthy inventory-to-sales ratio at 0.8, ensuring sustainable growth without excess working capital exposure.
• Developed structured demand forecasting models to support inventory control and stock planning decisions.
• Monitored daily, weekly, and monthly inventory performance to identify trends, risks, and productivity gaps.
• Recommended process improvements to prevent stockouts and overstock situations, improving service level consistency.