Optimized delivery staffing and on-time goods delivery
Artificial Intelligence (AI) for analyzing the data on ordering patterns
and creating optimized staffing plans and routes for the delivery team
Process
The client is a start-up focused on FMCG e-commerce. The client is currently operating from one major city in India. The business model is to maintain low inventory, and distribute products through local partnerships. The client was looking at providing same day or next day delivery of products ordered to ensure good customer experience. The pattern of orders was not consistent through the day or through the week. The challenge was to have optimized use of delivery staff to ensure deliveries being on time, as well as the best utilization of the staff. Keeping in mind the short delivery timeline, it was a difficult objective to achieve through manual process.
Solution
We looked at Artificial Intelligence (AI) for analyzing the data on ordering patterns and creating optimized staffing plans. An AI model was created to look at ordering patterns and the city was divided into different zones to identify delivery zones. An example of zone was that office areas were more common delivery zones during weekdays, while residential areas were more common delivery locations over weekends. The AI model then created staffing plans for the week to ensure that delivery staff offs got aligned as per low order delivery days.
Challenges Addressed
Highly analytical in nature
Creating staffing plans required analyzing a lot of factors like ordering patterns, delivery timelines, locations, and staff availability.
Short delivery timeline
The key objective was to keep a short delivery time from the time the order got placed by the customer.
High delivery cost
Keeping staff to manage deliveries resulted in high costs to ensure that no orders missed delivery timelines.
Missed deliveries
Missing delivery timelines resulted in orders being cancelled and poor customer experience.
Outcome
- 30% more orders covered by same staff requirement.
- Increase in staff utilization by 45%.