Name
Abstract | ||
|---|---|---|
Improving the availability of products in a store in order to avoid the OOS (out-of-stock) problem is a crucial topic nowadays. The reduction of OOS events leads to a series of consequences, including, an increase in customer satisfaction and loyalty to the store and brand, the production of positive advertising with a consequent growth in sales, and finally an increase in profitability and sales for a specific category. In this context, we propose the Pallet Integrity system for the automatic and real-time detection of OOS on promo pallets and promo forecasting using computer vision. The system uses two cameras placed in top-view configuration; one equipped with a depth sensor used to determines the number of pieces on the pallet and the other, a very high resolution web-cam, that is used for the facing recognition. The computer vision depth process takes place on edge, while the product recognition and promo OOS alarms runs on the fog, with a processing unit per store; the multi-promo forecasting service and the data aggregation and visualization is on cloud. The system was extensively tested on different real stores worldwide with accurate OOS detection and forecasting results. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-30754-7_30 | ICIAP Workshops |
Field | DocType | Volume |
Computer vision,Customer satisfaction,Visualization,Computer science,Loyalty,Internet of Things,Pallet,Profitability index,Artificial intelligence,Data aggregator,Database,Cloud computing | Conference | 11808 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Raffaele Vaira | 1 | 0 | 0.34 |
| Rocco Pietrini | 2 | 1 | 3.74 |
| Roberto Pierdicca | 3 | 0 | 0.68 |
| Primo Zingaretti | 4 | 289 | 44.00 |
| Adriano Mancini | 5 | 202 | 28.13 |
| Emanuele Frontoni | 6 | 248 | 47.04 |