In my plant operations, an unexpected production surge due to a marketing campaign exposed gaps in our inventory control, causing delays and disruptions. Learning from this, I integrated sales and marketing data, including predictive lead conversion estimates, into our demand forecasts to better anticipate spikes. I implemented an e-procurement system to categorize vendors by capacity, quality, pricing, and location, enabling dynamic sourcing. An inventory management system provided real-time stock tracking and automated reordering, streamlining procurement and supplier coordination. These changes aligned inventory control with demand forecasting, marketing, & supplier management, ensuring uninterrupted production and reducing errors.

To accurately forecast demand, you can: utilize historical sales data, incorporate external factors like market trends and economic conditions, leverage advanced forecasting methods like statistical modeling or machine learning, involve multiple departments in the forecasting process, regularly monitor and adjust forecasts based on new information, and choose the appropriate forecasting technique based on your product and market dynamics; key aspects include selecting the right data points, analyzing trends, and using error metrics to assess forecast accuracy.