International Research Journal of Education and Technology

Data-driven approaches to production planning and inventory control are becoming essential in today’s highly competitive and complex manufacturing environment. By leveraging vast amounts of data generated from supply chains, customer behaviors, and internal operations, companies can gain valuable insights to streamline processes, optimize resources, and reduce costs. The integration of advanced analytics, machine learning, and artificial intelligence (AI) allows businesses to make informed decisions based on real-time data, leading to improved forecasting, inventory management, and production scheduling. These techniques not only enhance operational efficiency but also increase responsiveness to market changes, providing companies with a competitive advantage.

One of the most significant contributions of data-driven methods is in the area of demand forecasting. Traditionally, forecasting relied on historical sales data and expert intuition, which often led to inaccuracies and overstock or stockouts. However, with the implementation of machine learning models and AI-driven tools, businesses can now predict demand more accurately by analyzing various factors such as seasonal trends, market conditions, customer preferences, and even external events like economic shifts. This enhanced demand forecasting allows manufacturers to better align their production schedules with market needs, reducing waste and optimizing resource utilization.

Data-driven approaches also play a crucial role in inventory control by enabling dynamic and responsive inventory management systems. Instead of relying on static reorder points and safety stock levels, companies can now use real-time data analytics to monitor inventory levels continuously and adjust them based on fluctuating demand. Algorithms can predict when stock levels are likely to fall below optimal thresholds and automatically trigger replenishment orders, ensuring that production lines remain operational without the risk of overstocking. Furthermore, predictive analytics can help companies identify slow-moving or obsolete inventory, enabling them to make data-driven decisions about markdowns, promotions, or discontinuations.

Key Words:  Data-driven approaches, Production planning, Inventory control, Demand forecasting