[2026 Latest] AI Flow Optimization to Eliminate Congestion in Furniture In-Store Pickups

Click & Collect, where customers pick up items ordered online at a physical store, has become established as a highly convenient shopping experience. However, in furniture retail, the time required to pick large items often leads to "congestion," where customers are left waiting at the counter for extended periods. Based on real-world consulting cases, this article explains the mechanisms of "back-end DX" that use AI location management to eliminate wasted search time and drastically shorten pickup lead times.

1. Quantifying "Walking Loss" in Furniture Picking

Furniture store backyards are vast, and large items like sofas and bed frames tend to be stored in dispersed locations. A common scenario on the floor is staff members searching for items based on intuition, resulting in over 15 minutes spent on a single pick. In our consulting projects, we begin by digitizing "walking distance" and "wait times" into data.

As the graph above shows, in environments where flow optimization is not implemented, staff movement costs eat into profits. This is not merely an operational efficiency issue but a management challenge directly linked to declining customer satisfaction.

2. The Logic of Shortest-Path Flow via AI Location Management

The core of AI location management is not just "recording locations," but "dynamic placement optimization based on shipping frequency and product size." High-turnover small items are automatically assigned to shelves near the pickup counter, while heavy items are allocated to areas considering the turning radius of forklifts. In our support projects, we build systems where AI instantly calculates a "single-stroke" route, showing staff exactly which shelf to go to next on their handheld terminals.

3. Real-Time Integration of In-House EC and Inventory Supporting Click & Collect

To operate Click & Collect smoothly, close integration between the front-end EC site and the back-end Warehouse Management System (WMS) is essential. The moment a customer selects "In-Store Pickup" on the EC site, the AI at the relevant store must incorporate the optimal picking sequence into the reservation list. In our In-House EC Construction & Growth Support, we recommend building these "no-wait mechanisms" from the system design stage. Having store and EC inventory synchronized without even a one-second lag is the foundation of OMO (Online Merges with Offline).

4. Success Steps for Field-Led Back-End DX

Implementing AI is meaningless if it is disconnected from field operations. The key to success lies in how well the AI learns "exception handling" in picking. For example, we digitize site-specific tacit knowledge, such as "this shelf requires using the adjacent aisle when moving large furniture." In our consulting, we track the movements of veteran staff and feed that data back into the algorithm, creating an environment where even new hires can pick at the speed of a veteran.