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SprintAI is building intelligent software to power fulfilment and inventory placement decisions for retail (and brand) stores which are doubling as fulfillment centers.

Built by engineers and data scientists from Amazon, Flipkart (Now Walmart) and Grofers (Instacart of India), we are bringing the best in class data science and optimization algorithms to power truly omni-channel retail experience with most efficient utilization of all assets (inventory, labor, shipping infrastructure).

This helps retailers operate with higher efficiency (higher product availability, lower logistics & labor costs) and offer personalized delivery promise to customers (leading to higher conversion). Based on some of our early results (on ground in India) and opportunity assessment (in US), retailers are loosing up-to 2-4% full price revenue and have 10-12% higher logistic costs because of inefficiencies.

As brand stores double up as warehouses to offer pickup and delivery (faster, cheaper) to customers, they face new type of risks and opportunities which are not catered by legacy systems. Here are a few distinct gaps:

  1. Availability Risk: While fulfilling orders from store, there is lag between order time and pick-up time, which introduces risk that items may not be found to fulfill an order.
  2. Better Delivery Promise: Rather than offering a standard delivery time (often 3-7 day window) across all pin-codes (in large zones), retailers can learn from previous orders and offer personalized (and more precise) delivery promise to customers (at each pin-code, carrier) and increase cart conversions.
  3. Fulfillment location optimization: Neither ERPs (catering to offline store planning), nor Order Management Systems (catering to online orders) have a holistic picture of the business. While fulfilling demand for online orders, this leads to local (channel, store-wise) optimizations driven via simple rules, which are often sub-optimal. For Example, it may not always be profitable to fulfill a customer order form the nearest store (default rule used by most organizations), if there is another store which has comparable (or just higher) delivery cost but has excess stock which will probably go into mark-down (if its unsold).
  4. Optimal Stock Placement: Traditional stock placement (allocations, replenishment, inter-store balancing) also needs to change as per the real demand from a region (catered by a store) across channels. There is a cyclical problem in current systems, due to which they end up over-stocking a store which is fulfilling orders to a far off location because that's how stock placement algorithm works in traditional ERPs (send more product where there is more fulfilled demand).

In essence, we need to re-imagine and re-write the intelligence layer powering 'all' product movements. As SprintAI, we are building this intelligent software integrated with Order Management Systems and ERPs, powered by AI (for probabilistic demand forecasting) and real time optimization algorithms.

The sub-components (Individual Product Offerings) of the problems we plan to solve include:

  1. Dynamic Safety Stock (to increase availability across online and offline channels, yet keep order fill rates high)
  2. Optimized Fulfillment Location (Real time API for each order by optimizing between labor, logistics and potential mark-down costs)
  3. Personalized delivery promise (by pin-code)
  4. Inventory Placement - Allocations, Replenishment, Re-Balancing