How Zepto's Data Science Engine Powers 10-Minute Deliveries
Ten-minute grocery drops feel like a trick, yet Zepto, the Indian quick-commerce startup, runs it with a lot of data work. They handle more than 4,000 orders a day in each dark store - a number that would crumble without a solid system. At the core is a home-grown data engine that tries to guess what shoppers will need, and, crucially, when and where they’ll want it.
It sifts through millions of signals - from the weather and local events to how fast items are selling right now - and the forecasts are surprisingly spot-on. The real headache isn’t just picking the right SKU; it’s making sure that SKU sits in the right micro-warehouse the instant an order hits the app. That tight dance of supply and demand is what lets Zepto keep its ten-minute pledge.
As they put it, the goal is to have the “right things” in the store at the right time. If we think about our own closets, we’d be buried under clothes without that kind of planning. So after the demand model runs, the next job is to move inventory around in each Zepto store.
All this allows Zepto to stock up its stores with the “Right things” at the right time. Much like our wardrobe, Zepto’s warehouses would be overflowing with inventory if not planned properly. That is why, after the demand forecast is done, the next step is to manage the inventory present in a Zepto warehouse at the given moment.
Using demand forecasting, Zepto can figure out what products it needs to store, but not all of those products find space in a given warehouse. How does Zepto decide which and how many of the products it can store in a given warehouse? To solve this problem, Zepto relies on one of the most popular algorithms in computer science and operations research: 0/1 Knapsack Problem: The algorithm is used to maximise the total “value” of the stocked items in a store while ensuring that the total space occupied by a product stock stays within the shelf capacity.
The algorithmic optimization of its store settings sets Zepto’s stores apart from the usual retail outlets, where the item assortment is guided purely by “gut feeling”. It helps to curate a given Zepto store at any point in time, with fast-moving, high-demand, and profitable items, while excluding the slow-moving products.
What will really matter for Zepto is how its model holds up once it moves out of the packed city cores and into places where demand is more erratic. The 10-minute tagline grabs attention, sure, but the real work happens behind the scenes, a data-driven engine that juggles hyper-local demand forecasts, inventory shuffling and route tweaks in near-real time. That kind of plumbing hints at a day when “instant delivery” could stop being a gimmick for a handful of neighborhoods and become something you can count on.
Right now the hurdle isn’t just proving the idea works; it’s keeping that precision when you spread it across an entire country, where shopping habits and traffic patterns shift from town to town. If Zepto pulls it off, it won’t merely be dropping groceries at doors - it will be sketching a new playbook for real-time logistics that may push e-commerce expectations farther than we’ve seen so far.
Resources
- Kafka, ClickHouse, and the Real-Time Tech | Point Break - AIM Network (Point Break)
- How is Zepto Able to Deliver Groceries in 10 Minutes - AppsRhino
- Zepto Business Model Breakdown: How Dark Stores and Data Drive 10-Minute Delivery - On-Demand App
- How Zepto's Brand Engine Keeps Its 10-Minute Promise Alive - Inc42
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Common Questions Answered
How does Zepto's data science engine enable 10-minute deliveries?
Zepto's proprietary data science engine enables 10-minute deliveries by processing over 4,000 orders per dark store daily and analyzing millions of data points to predict what customers will want, and when and where they will want it. This sophisticated backbone allows for hyperlocal demand forecasting and dynamic inventory routing, which are essential for maintaining the speed promise.
What role does demand forecasting play in managing Zepto's warehouse inventory?
Demand forecasting is critical for inventory management as it allows Zepto to stock its dark stores with the right products at the right time to avoid overflowing warehouses. After forecasting demand, the system determines which specific products need to be stored, but not all forecasted items can fit, requiring precise inventory management at any given moment.
What is the main challenge for Zepto's model as it scales beyond urban centers?
The primary challenge for Zepto's model during expansion is operating in areas with less predictable demand patterns compared to dense urban centers. The underlying data science engine must adapt to ensure that instant delivery remains a reliable and efficient service, not just a novelty, as the company grows.