The warehouse motors the win in the hyper-competitive reality of quick commerce (q-commerce), where it takes only several minutes to have the product delivered to the consumer.
The traditional inventory approaches cannot be used to sustain this speed any longer. Radio Frequency Identification (RFID) and Artificial Intelligence (AI) integration are the ultimate solution to high-speed fulfillment by 2026. Both of them convert raw movement data into predictive intelligence, so that, as a package, Quick will never cost Accuracy.
How do RFID and AI collaborate to create predictive warehouse models?
RFID retail and AI complement each other, starting with the data stream. RFID hardware tags and readers-monitors are monitors that record all the movements of items in real-time, without the need to have a direct line of access. This creates huge amounts of event data.
This data is fed into machine learning models, also referred to as AI algorithms, which are designed to detect concealed patterns that a person would not notice. As an example, the AI can deduce what items are trending at a particular time because of the frequency and time of RFID pings on individual SKUs.
This would enable the system to make a shift towards reactive restocking to predictive replenishment when moving the stock to the edge of the warehouse, even prior to the order being placed.
Can AI-driven RFID analytics optimize the "Dark Store" layout?
Q-commerce typically makes use of dark stores, small and local fulfilment centres that are optimised towards speed. AI is used to analyze RFID location data to generate heat maps for picker actions and the popularity of items.
In case the AI identifies that people often buy specific products in combination, it will propose to co-locate the latter to optimize the layout. Moreover, it anticipates the bottlenecks relating to areas in which RFID-tagged picking carts are often stalled.
Reconfiguration of the virtual and the physical layout of a warehouse on these predictive insights can decrease the picking time to less than 20 per cent, a crucial factor in the sub-15-minute delivery battle.
How does the integration prevent "phantom inventory" and stockouts?
"Phantom inventory," a system whereby it believes there is a stock item in place when it is not, is a nightmare of q-commerce. RFID gives the actual truth, and AI offers the detection of anomalies.
Should the RFID system ping a tag in a "mispicked" bin or an area restricted to unauthorized access, the AI triggers an immediate discrepancy notification. More to the point, predictive analytics predict stockout risks, matching the current inventory values with the external factors, such as weather, local events, or social media trends.
In the case when it is expected that a new spurt of demand will occur and ice cream on a hot Friday night, the AI will give an automated order hours before the shelf will be empty.
Conclusion
RFID combined with artificial intelligence is the intelligence and the brain of the new high-speed commerce warehouse. RFID software solutions are the high-fidelity data, and AI is the foresight to operate on it. With the use of such technologies, the participants of q-commerce will also be able to attain 99% accuracy in the inventory and predictive fulfillment, making the logistical issue of delivering the goods in no more than an hour a viable and profitable reality.
