Onit’s solution that uses Machine Learning for inventory monitoring and procurement process optimization.
directly through a browser
FIELDS OF APPLICATION
What advantages does the IProQ solution provide?
Effective inventory monitoring, distinguishable by warehouse or total, based on the schedule of incoming supplies, commitments related to production plans and customer orders received but yet to be planned.
The system calculates the requirement forecast and thus the minimum monthly stock thresholds, using machine learning techniques.
IProQ can suggest the quantity to order and the ideal date for the arrival of the goods based on the lead times guaranteed by the suppliers, and supports management of the new order.
FORECAST WITH ARTIFICIAL INTELLIGENCE ALGORITHMS
The calculation function for forecasting through Machine Learning techniquesis controlled by a scheduler and the result of the processing can be effectively controlled with a graph that allows a partial overlap between the history and the forecast.
SUPPORT FOR CREATING VENDOR ORDERS
The choice of supplier and the guided creation of the vendor order are managed according to a series of criteria (optimal quantity, compatible lead time, best price, pre-existence of contracts to be respected) and supported by a tool for sending emails from templates (e.g., quotation requests) and include the option of sending the draft order to the ERP.
The system uses graphic visualizations that guarantee immediacy, thanks to the scheduling and categorization of warehouse stocks by Equivalence Groups.
IProQ does not require clients to install applications and can be used through the browser on any type of device (tablet or PC), allowing operations both from the office, on the move, or from home.
The IProQ solution allows effective and efficient monitoring of raw material stocks (and enables updates almost in real-time). It also allows you to optimize and support procurement process activities through AI algorithms, thus significantly reducing the occurrence of stock-outs and downtimes.