Agentic AI Framework for Smart Inventory Replenishment
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866917112334778368 |
|---|---|
| author | Syed, Toqeer Ali Jan, Salman Ali, Gohar Akarma, Ali Ali, Ahmad Mastoi, Qurat-ul-Ain |
| author_facet | Syed, Toqeer Ali Jan, Salman Ali, Gohar Akarma, Ali Ali, Ahmad Mastoi, Qurat-ul-Ain |
| contents | In contemporary retail, the variety of products available (e.g. clothing, groceries, cosmetics, frozen goods) make it difficult to predict the demand, prevent stockouts, and find high-potential products. We suggest an agentic AI model that will be used to monitor the inventory, initiate purchase attempts to the appropriate suppliers, and scan for trending or high-margin products to incorporate. The system applies demand forecasting, supplier selection optimization, multi-agent negotiation and continuous learning. We apply a prototype to a setting in the store of a middle scale mart, test its performance on three conventional and artificial data tables, and compare the results to the base heuristics. Our findings indicate that there is a decrease in stockouts, a reduction of inventory holding costs, and an improvement in product mix turnover. We address constraints, scalability as well as improvement prospect. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_23366 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Agentic AI Framework for Smart Inventory Replenishment Syed, Toqeer Ali Jan, Salman Ali, Gohar Akarma, Ali Ali, Ahmad Mastoi, Qurat-ul-Ain Artificial Intelligence Multiagent Systems In contemporary retail, the variety of products available (e.g. clothing, groceries, cosmetics, frozen goods) make it difficult to predict the demand, prevent stockouts, and find high-potential products. We suggest an agentic AI model that will be used to monitor the inventory, initiate purchase attempts to the appropriate suppliers, and scan for trending or high-margin products to incorporate. The system applies demand forecasting, supplier selection optimization, multi-agent negotiation and continuous learning. We apply a prototype to a setting in the store of a middle scale mart, test its performance on three conventional and artificial data tables, and compare the results to the base heuristics. Our findings indicate that there is a decrease in stockouts, a reduction of inventory holding costs, and an improvement in product mix turnover. We address constraints, scalability as well as improvement prospect. |
| title | Agentic AI Framework for Smart Inventory Replenishment |
| topic | Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2511.23366 |