During the warehouse management process, errors related to operations and delays negatively affect service quality and customer satisfaction. The logistics optimization process is key to predicting demand and selecting a suitable sales destination. If an error occurs in counting the number of pallets or packages that need to be moved on a particular day or the amount of equipment required to handle that move, all subsequent operations will be affected.
If an AI model is used to improve such process, detailed stock movement forecasting and management can be achieved. This can reduce operator errors and turnaround time, and improves overall work efficiency and productivity. In addition, you can analyze and understand when the interruption occurs and the cause of the interruption. This can be of great help in improving the operation of the warehouse, maximizing the warehouse's efficiency in terms of storage management and supply.
|Data||Data type||Content||Use mode|
|Input data||CSV||Demand forecast results, supplier delivery times, quality issues and product line downtime information, etc.||API|
|Output data||CSV||Real-time inventory prediction||API|
|Payment||Subscription method||Attached file upon application|
|Prepaid charge||Online||Customer data required for model creation|
|API Setting||$1,800 ~|