By Troy Prothero, Senior Vice President, Product Management, Supply Chain Solutions, Symphony RetailAI
Q-commerce is an emerging category of ecommerce where providers aim to deliver items, usually grocery goods, extremely quickly, with some promising deliveries in 10 minutes or less. These companies operate out of “dark stores”, much like the “ghost kitchen” concept of restaurants where a restaurant only exists in delivery apps but does not have a physical store that customers can visit. Dark stores are usually nothing more than a hyperlocal fulfilment centre that houses and processes orders for couriers to pick up and deliver.
Would-be q-commerce operators or retailers interested in exploring this channel should first retrain their brains on some widely held beliefs for demand forecasting for the typical retail setting. For instance, with a dark store, the visual considerations of planning service and minimum stock levels are moot. The operator can theoretically optimize those levels with unprecedented precision since what truly matters is ensuring the right products are on hand to fulfill orders, regardless of quantity.
Retrain the brain
One time-tested concept that still applies here is the old shelf planning equation of looking at the difference between merchandise arriving at a loading dock, what eventually sells through at the register, and planning the next order accordingly. Modern demand and shelf planning for a dark store can take that idea and strengthen it with AI to make it even more powerful and agile to respond to fast-changing shopper and market behaviours.
Dark stores are a perfect use case for AI-enabled demand forecasting: it’s not wildly different from demand forecasting for brick and mortar and other types of omnichannel forecast applications, where AI-enabled solutions already deliver proven value. Therefore, those who have data sets from brick and mortar or other omnichannel applications are already a step ahead. AI powered demand forecasting for dark stores also comes with the bonus that it does not need to account for merchandising and other foot-traffic considerations. That means that AI-enabled demand forecasting for this channel is likely to produce highly reliable results.
That’s because AI models are trained to focus solely on the inputs related to moving items in and out of the shelf without needing to consider much else. It’s an opportunity for these operators to home in on all the pros of demand forecasting with few cons.
Prepare for tomorrow today
Operating under the q-commerce business model requires dynamic supply chain and demand planning, making it nearly impossible to succeed solely through outdated best practices. That’s why q-commerce operators need to update yesterday’s lessons for today to rethink how they approach demand and shelf forecasting to execute plans as fast as deliveries can be made.