Gnomial Functions Inc. wants to determine the most reliable forecasting technique for determining the sales level for the next eighteen months. The sales trend reflected a consistent increase over the last 18 months with minimal seasonal variation. However, the sales for recent months reflected had more significance on the following months sales (Wisniewski, 2016). Consequently, Gnomial Functions Inc. should use the weighted moving average to forecast the most likely level of sales.
The weighted moving average used four weights to determine the likely level of sales in the next month. These weights ranged from 0.1 to 0.4 for the earliest and most recent months respectively. The most recent data received a higher weight due to its higher relevance to the future level of sales. Consequently, the weighted moving average generates accurate forecast compared to the simple moving averages.
The company can realize even more accurate forecast by adjusting the weights for the moving average. The management should give higher weight to the recent monthly sales to reduce the forecasting error. Specifically, the mean absolute deviation and mean standard error for the initial weights (0.1, 0.2, 0.3, and 0.4) was 6.27 and 47.29. In contrast, when the new weights that give more importance to the recent performance (0, 0, 0.4, and 0.6) are applied, the mean absolute deviation declined to 4.81, and the mean standard error declines to 27.28. This performance is better than the simple moving average.
Gnomial Functions Inc. should use the weighted moving average to determine the likely level of sales in the next year. This method is simple, effective, and reliable. In addition, it can be adjusted to reflect the changes in sales trend by adjusting the weights to give the requisite importance to the recent months. Therefore, the company should use weighted moving average to estimate the likely performance in the coming year.
Reference
Wisniewski, M., 2016. Quantitative methods for decision makers. Pearson.