Final answer:
Exponential smoothing forecasts cannot be computed without actual sales data. A regression model was provided but is not applicable for quarterly predictions with exponential smoothing, which requires recent sales figures and the previous period's forecast.
Step-by-step explanation:
To compute the forecast for future quarters using exponential smoothing, we need the actual sales data for previous periods (which we do not have in this scenario). However, typically the formula for exponential smoothing is Ft+1 = α × At + (1 - α) × Ft, where Ft+1 is the forecast for the next period, α is the smoothing factor, At is the actual sales for the current period, and Ft is the previous period's forecast. Since the smoothing factor is given as 0.6, the formula would heavily weight the most recent sales figure. This technique is a simple yet powerful tool for sales forecasting and inventory control in businesses.
Without actual sales figures, we cannot provide an answer to predict sales for 4Q 2022 or any of the quarters in 2023. Also, the regression model provided for the electronics retailer cannot be used in this context as it gives a linear equation for predicting sales growth daily rather than a quarterly basis. For the regression model, we would simply substitute x with 60 or 90 to predict sales for days 60 or 90, respectively.