About the customer
One of India’s leading Direct Selling Companies offering products in the categories of personal care, color cosmetics & skin care, home care, laundry care, agriculture, auto care, nutrition, food & beverage and health & wellness.
Customer Challenges
32 Mn. decision nodes weremanually processed by one dedicated resource to create a replenishment plan based on supplier, product, quantity, location, transportation type & lead time. Manual process led to error, inefficiencies, delays and:
- High Complexity
- Longer TAT to create plans
- No real-time visibility of critical stock
- Managing the ageing of inventory
- Stock out or Overstock at FCs
- Critical Stock prioritization
- Delayed replenishment of promo products
- 70% capacity utilization of truck
- Gap in planning vs execution (warehouse & manpower capacity)
Impact on Business
- Variance in demand vs stock replenishment
- Loss of opportunity to sell
- Financial losses due to ageing issue
- Higher inventory holding cost
- Longer lead time to replenish
- Higher cost of operations
Solutions
- Implemented VERDIS RAP (AI & BI enabled Replenishment Allocation Planning)
- (Automated and optimized the replenishment planning
- Enabled system-based stock analysis
- Automated Dispatch Planning
- Automated Transportation planning
- System based recommendation of transporter and route
- Dashboard view to make informed decision
Key benefits:
- Demand-driven replenishment
- Planning time reduced from 7 days to 10 minutes
- 25 plans creation in 3 hours
- Enabled advance planning option (7 days to 1 month)
- Real-time visibility of stock
- Delivery route optimization (the nearest supply origin)
- Improved speed time to shelf
- Increase productivity & efficiency
- Higher product availability
- Lower cost-to-serve
Value Addition:
- 10 mins to create 1 plan
- 99% stock availability
- 9% accuracy in allocation planning
- 30% reduction in inventory days
Ready to transform your warehousing and fulfilment processes? Contact Us now at [email protected] to Get Started.
