An FMCG e-commerce starts up operating in a major Indian city built its model around low inventory and local delivery partnerships, promising same day or next day delivery. But order patterns weren’t steady they shifted by time of day, day of week, and zone making it hard to staff delivery teams efficiently by hand.
• High delivery costs from inefficient staffing against unpredictable demand
• Missed deliveries when staffing didn’t match where and when orders actually landed
• A highly analytical staffing problem, hard to solve through manual planning
• Short delivery timelines leaving no room for staffing missteps
Aiwozo built an AI model to read ordering patterns by zone and time, turning that into staffing plans that matched actual demand rather than a flat schedule.
Pattern analysis agents studied ordering data across the city, dividing it into delivery zones and identifying how demand shifted for example, office areas seeing more weekday orders, residential areas picking up on weekends.
Staffing agents used those patterns to generate staffing plans for the week, aligning delivery staff schedules including days off with the zones and times where order volume was actually low, rather than applying the same staffing level every day.
Key business and operational challenges that limited performance, scalability, and governance before automation was introduced.
