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SCMG 305: At Minigrip, we specialize in plastic products like snack bags and trash bags, forecasting is a crucial part of our operations. Predicting future demand for these products is essential to maintain efficient inventory levels, minimize production costs, and meet customer requirements. However, there are several ways a forecast can go wrong when using historic data to predict future requirements in this context.

One common challenge we run into is underestimating the level of seasonal variations. Snack bags and trash bags might experience variations in demand during holidays or specific seasons. If historical data does not account for these patterns good enough, it can lead to overstocking or stockouts. Also, consumer preferences and market trends can change over time, and if historical data does not reflect these shifts, forecasts may not align with actual demand.

The statistical methods used to sense, shape, and forecast demand at Minigrip may include time-series analysis techniques such as moving averages or exponential smoothing. These methods are applied to historical sales data, order history, and market indicators to generate forecasts. The choice of method often depends on the product and the time frame.

Time-series data used for forecasting at Minigrip usually encompasses historical sales figures, order data, and inventory levels for the products we make. While this data serves as a good foundation for forecasting, the accuracy of the forecasts may vary. The short-term predictions for regular product lines like trash bags tend to be more accurate due to stable demand patterns, but longer-term forecasts or forecasts for new product variations can be more challenging. We constantly monitor forecast accuracy and adjust forecasting methods based on results. This way we can continually work towards getting more accurate results.