Problem: Component suppliers of the parent company lack information about the number of components used and plan production based on experience, which leads to errors and difficulties in inventory management. Unstable order volumes from the parent company create inventory management issues for suppliers, which can negatively impact their financial situation.
Objective: To create an AI-based forecasting system to predict delivery volumes, optimizing inventory management and productivity.
Offering: Analyze historical data and generate forecasts based on trends, allowing for the prediction of future behavior and optimization of required deliveries.
Solution: Data collection was carried out at the manufacturing plant through an internet data center. The data included variables such as part number, creation time, order volume, planned delivery volume, etc. This data was used to train an AI model to predict delivery volumes, improving inventory management. The AI GEEKS engineers applied the LSTM (Long Short-Term Memory) algorithm for order volume forecasting. This algorithm takes into account long-term dependencies in the data, making it suitable for time-series forecasting tasks. Market Advantage: The AI GEEKS engineering team created a model that predicts the number of ordered parts with an accuracy of 1–2 parts. This means that suppliers can predict in advance how many parts the parent company will order and prepare all materials to optimize inventory management and production.
As a result, the supplier company was prepared when the actual order was placed, avoiding situations like part shortages, insufficient time to complete the order, or overstocking.
Problems arising from the inability to plan production in advance often lead to losses and additional time costs. The use of AI improved efficiency, reduced risks, and optimized the delivery process.