Even before COVID-19 forced retailers in India, and globally, to rethink their operational strategy, retailers were grappling with profitability and growth-related challenges. Then came COVID 19 and the retail universe was faced with several problems, such as:

Massive decrease in footfall
Low return on Stores at central hip locations
Exponential Growth in Online Orders
Ever-increasing Consumers’s Expectations
Low-Profit Margins

Fortunately, there seems to be a silver lining. For several brands, dark stores (also known as fulfillment centers) have become strategically important in matching the ever-shrinking shipping time and superior customer service levels of major retailers like Amazon and Alibaba.

Why is a Dark Store Network Important?

“On Demand Deliveries” can cost over Rs 50 per delivery and is often not feasible. However, if the goods to be delivered were located close to customers the cost would decrease manifold.

The idea behind “dark-stores” was to have mini-warehouses close to customer locations to facilitate deliveries and make them quicker while keeping operational costs low.

To implement the idea appropriately, you would have to first answer the following questions:

What is the consumer demand across the operational area?
Which area is close to several demand hotspots?
Of these areas, which have the lowest rent?
Is the road network good around the location to ensure smooth stocking-up and picking-up of goods?

All the above questions can be answered with DataSutram’s KHOJ AI platform at 100 x 100m granularity.

What is the Consumer Demand across the Operational Area?

Demand for a product varies from one neighborhood to the other. The first step towards predicting demand is to identify the characteristics of a neighborhood that influence demand.

Over 178+ External Location Factors are considered to identify these characteristics.

Using F(X), the influence of each External Factor over Demand is computed.

Consider an FMCG Chain that operates in South Mumbai, the External Factors that hold 88% influence over demand for its products are:

Population Density
Commercial Store Density
Entertainment Centers Nearby

Keeping in mind the identified set of external factors & their importance, the demand across the operational area is predicted.

Demand Map [Gradient: Blue (low) to Red (high)]

Locations with high demand that could potentially become Demand Hotspots are filtered out and pinpointed.

For eg, Nagdevi Nagar is a Demand Hotspot for considered FMCG Chain.

Which area is close to several Demand Hotspots?

To gain maximum returns, each Dark Store must be located such that, it can cater to several demand hotspots. To find such points we apply the following formula on each 100 x 100 m grid.

The Delivery Convenience is expressed as an index in the range from 0 to 10, to give a clear idea of favorable Dark store Locations based on proximity to Hotspots.

Favorable Locations[Gradient: Yellow (low) to Purple (high)]

Which areas have the lowest rent?

To ensure that the setup cost of the Dark Store is low, the affluence of each location is considered. The affluence is calculated based on a number of data points such as rent, the average income of people living there, etc.

Affluence Map: Gradient [blue (low) to red(high)]

Is the road network good around the location to ensure smooth stocking-up and picking-up of goods?

The number of roads, traffic movement, jam factor, etc around a location is analyzed to assess the connectivity.

Taking into consideration the above-mentioned factors and the Catchment Area for each Dark Store, a set of locations is identified to build an efficient Dark Store Network.

This method has proven to: