The last four weekly values of sales were 80, 100, 105, and 90 units, while the last four forecasts were 60, 80, 95, and 75 units. What does this data illustrate?

The data illustrated in this scenario shows a systematic tendency of forecasts to deviate from actual sales values, indicating bias in the forecasting process. Bias occurs when the forecast consistently overestimates or underestimates the actual values over time.

Looking at the sales data (80, 100, 105, 90) compared to the forecasts (60, 80, 95, 75), a pattern emerges where the forecasts are generally lower than the actual sales figures. For instance, the first forecast is 60 while the actual sales is 80, indicating an underestimation. This trend continues with the rest of the values, where the forecasts do not align closely with the actual sales, highlighting a consistent error in one direction.

In this context, the forecasts are failing to accurately capture the trend of increasing sales values and instead remain consistently lower than the actual performances. Bias is particularly concerning in supply chain and operations management because it can lead to inadequate inventory levels or poor resource allocation if decisions are based on forecasts that systematically underestimate demand. Understanding and addressing bias is crucial for improving forecasting accuracy and enhancing overall supply chain performance.