Headquartered in Bengaluru, Big Basket is one of the largest online grocery supermarkets in India. It has around 20,000 products from over 1000 brands in its company catalog, which can be accessed through an online platform. This dynamic platform caters to Big Basket’s 10 million customers who can browse through the information to find virtually anything from dairy products to beauty aids on the company’s website. In addition, Big Basket offers its customers ‘slotted’ delivery options wherein they can get their orders delivered within 90 minutes or, latest, on the following day.
Rising consumer demands have posed various challenges to the company, such as, managing voluminous order requests, on-time delivery, and traffic congestion. In addition, the nature of the products and complexities in the delivery of perishable goods are a serious challenge for Big Basket’s delivery chain. Supply of fresh products from the first mile to the last mile, that is, from the warehouse to the customer’s doorstep, is in huge demand and on the rise; it is essential that Big Basket continue to keep us the standards and quality of its products to avoid consumer dissatisfaction.
Location analytics, along with advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Cloud, play a significant role in the supply chain management of the grocery products available at Big Basket. Most supply chain processes involve a spatial component ─ in designing networks of transportation and distribution channels, demand forecasting, and routing. To ensure timely delivery of both perishable and non-perishable products, and overcome several other challenges encountered in the supply chain, Big Basket uses a platform provided by Locus.sh known as ‘Locus Dispatcher’. Bengaluru-based Locus.sh has created this technology platform to automate complex supply chain decisions crucial for business growth. Locus Dispatcher ensures consistency, efficiency, and transparency from first- to last-mile delivery, reduces environmental impact, cuts down operational costs, and streamlines the customer experience.
Locus Dispatcher is a Cloud, AI, ML and GPS-based tool that optimizes and automates routes using various algorithms, and facilitates live monitoring and tracking of delivery vehicles to get accurate, real-time ETAs (Estimated Time of Arrival).
Locus Dispatcher uses a proprietary geocoding engine to avoid misrouting and delays in deliveries. It converts the addresses where deliveries have to be made into accurate and precise geographical coordinates, even if an available address is of poor quality.
ETA is imperative to ensuring timely last-mile deliveries. It is the time taken for a delivery to arrive at its destination. The Locus Dispatcher platform uses historic data and takes into account real-world constraints such as rider breaks, traffic congestion, and the time taken to complete an order, etc. In this way, it manages to circumvent delays in order delivery.
The GPS of the Locus Dispatcher is useful for riders as it helps in planning their real-time routes and ensures they reach within the stipulated timeframe. This is done by calculating and comparing the difference between the planned route and executed route of delivery. Live monitoring of the supply chain processes helps managers keep an eye on compliance rates, which comes in useful when evaluating employee performance and tracking the on-ground operations and logistics of first-mile to last-mile deliveries. Further, it also helps customers track their orders in real-time on their devices.
Location data and analytics with AI solutions have assisted Big Basket in ensuring timely delivery of goods and products to more than 10 million customers. Automated and optimized route plans have improved delivery services according to the customer’s preferred time slots. Given real-time traffic congestion and route restrictions, Locus.sh provided Big Basket with the best fleet combination through its technology platform. Capital utilization and vehicle space utilization resulted in a reduction in shipping and other operational costs, while increasing the number of orders per rider. In addition, successfully predicting the real-time ETA of the orders enhanced consumer experience and achieved 99.5% of service-level agreement (SLA) adherence.
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