Analysis and Forecasting Techniques Paper 5

Answer (B) is correct. Exponential smoothing is a sales forecasting technique used to level or smooth variations encountered in a forecast. It also adapts the forecast to changes as they occur. The simplest form of smoothing is the moving average, in which each forecast is based on a fixed number of prior observations. Exponential smoothing is similar to the moving average, but the term "exponential" means that greater weight is placed on the most recent data, with the weights of all data falling off exponentially as the data age.

Answer (C) is correct. Time series analysis relies on past experience. Changes in the value of a variable may have several possible components including secular trends, cyclical variation, seasonality, and random variation. Seasonal variations are common in many businesses. A variety of methods exist for including seasonal variations in a forecasting model, but most methods use a seasonal index. Alternatively, seasonal variations can be removed from data by using a weighted average of several time periods instead of data from individual periods.

Answer (D) is correct. Exponential smoothing is a widespread technique for making projections because it requires less data be kept on hand than the moving average methods. The technique involves weighting the actual result for the previous period by a smoothing factor, weighting the forecast for the previous period by the smoothing factor’s complement, and combining the two.

Answer (B) is correct. Under exponential smoothing, each forecast equals the sum of the last observation times the smoothing constant, plus the last forecast times one minus the constant. Thus, exponential means that greater weight is placed on the most recent data, with the weights of all data falling off exponentially as the data age. This feature is important because of the organizational changes that affected sales volume.

Answer (A) is correct. Time series analysis or trend analysis relies on past experience. Changes in the value of a variable (e.g., unit sales of a product) may have several possible components. In time series analysis, the dependent variable is regressed on time (the independent variable). The secular trend is the long-term change that occurs in a series. It is represented by a straight line or curve on a graph. Seasonal variations are common in many businesses. A variety of methods include seasonal variations in a forecasting model, but most methods adjust data by a seasonal index. Cyclical fluctuations are variations in the level of activity in business periods. Whereas some of these fluctuations are beyond the control of the firm, they need to be considered in forecasting. They are usually incorporated as index numbers. Irregular or random variations are any variations not included in the three categories above. Business can be affected by random happenings, e.g., weather, strikes, or fires.

Answer (D) is correct. The simple moving-average method is a smoothing technique that uses the experience of the past N periods (through time period t) to forecast a value for the next period. Thus, the average includes each new observation and discards the oldest observation. The forecast formula for the next period (for time period t+1) is the sum of the last N observations divided by N.

Answer (B) is correct. Time series analysis is a regression model in which the independent variable is time. In time series analysis, the value of the next time period is frequently dependent on the value of the time period before that. Hence, the error terms are usually correlated or dependent on the prior period; i.e., they are characterized by autocorrelation (serial correlation).

Answer (B) is correct. Exponential smoothing is a widespread technique for making projections because it requires less data be kept on hand than the moving average methods. Mathematically, a forecast is arrived at with exponential smoothing according to the following formula: Forecast = (Smoothing factor × Previous month result) + (Smoothing factor complement × Previous month forecast) = (0.3 × 158) + (0.7 × 148) = 47.4 + 103.6 = 151

Answer (B) is correct. The formula for forecasting sales using exponential smoothing is Ft = (α)xt – 1 + (1 – α)Ft – 1 , where F = the forecast for a period, t = the time period, α = the smoothing factor (0 < α < 1), and x = the actual result for a period. Since June’s forecasted sales are not known, first solve for this as follows: F(June) = (α) × May’s actual sales + (1 – α) × May’s projected sales = (.7) × 22,000,000 + (1 – .7) × (22,000,000 – 600,000) = 15,400,000 + 6,420,000 = $21,820,000 Now calculate July’s projected sales as follows: F(July) = (α) × June’s actual sales + (1 – α) × June’s projected sales = (.7) × 22,500,000 + (1 – .7) × (21,820,000) = 15,750,000 + 6,546,000 = $22,296,000

Answer (B) is correct. All of these methods use past data to estimate future value. The simple regression model assumes that past relationships can be validly projected into the future, the learning curve analysis reflects the increased rate at which people perform tasks as they gain experience, and the time series analysis projects future trends based on past experience.