The growth of Artificial Intelligence (AI) has been rapidly accelerating in recent months, and its influence is spreading to new areas of our daily lives each day. In the fast-paced world of business, where efficiency and accuracy are key components requiring constant improvement, it seems natural to explore the possibilities of using AI in inventory management, where keeping track of products, supplies, and demand can be a complex and time-consuming task.
Recognizing the potential of AI in the effort to improve the efficiency and accuracy of its inventory management process, Profittools started to incorporate AI and machine learning into some of its models, which track product performance, detect pattern changes, and make personalized adjustments to align with overall inventory goals.
- Method Selection. The module is designed to select the optimal replenishment method for each item, considering its sales history and adjustable parameters. The objective is to ensure the most efficient stock behavior based on the specific sales patterns of the item. Profittools offers a range of diverse functional models, as well as the ability to create custom algorithms that meet the specific requirements of individual clients.
- Calibration. This simulation module is developed to evaluate the most appropriate parameters of the selected method and to ensure that the inventory is kept at the lowest possible level while avoiding lost sales. Through multiple simulations, it identifies and automatically readjusts its parameter sets to ensure optimal inventory performance.
- Stock Availability. The purpose of this module is to adjust replenishment levels and selected parameters in response to unexpected stock shortages caused by supply disruptions. It constantly monitors product availability and adjusts safety stock levels to meet demand.
- Stock Level: This module aims to identify and eliminate any unusual sales trends that may affect demand calculations. By continuously monitoring sales patterns, it detects discrepancies and automatically updates demand projections to prevent accidental overstocking and maintain accurate inventory levels.
The use of AI in inventory management holds the potential for significant benefits, but it also comes with its own set of challenges. Each client has unique criteria for what constitutes a desirable outcome, requiring AI models to fit their specific needs. This increases the complexity of the machine learning algorithms, which must be adaptable to meet the varying demands of each customer. However, equipped with a sophisticated and flexible mathematical engine, Profittools is dedicated to improving its automation models to offer the best experience for our customers.