Which one of the following is a sales forecasting technique that can be utilized in preparing the annual profit plan?
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.
The four components of time series data are secular trend, cyclical variation, seasonality, and random variation. The seasonality in the data can be removed by
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.
A forecasting technique that is a combination of the last forecast and the last observed value is called
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.
As part of a risk analysis, an auditor wishes to forecast the percentage growth in next month’s sales for a particular plant using the past 30 months’ sales results. Significant changes in the organization affecting sales volumes were made within the last 9 months. The most effective analysis technique to use would be
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.
What are the four components of a time series?
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.
The moving-average method of forecasting
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.
Violation of which assumption underlying regression analysis is prevalent in time series
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).
Sales of big-screen televisions have grown steadily during the past 5 years. A dealer
predicted that the demand for February would be 148 televisions. Actual demand in
February was 158 televisions. If the smoothing constant (?) is 0.3, the demand forecast for
March, using the exponential smoothing model, will be
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
Sunrise Corporation’s actual sales for May were $22,000,000, a result $600,000 greater
than projected. Actual sales for June totaled $22,500,000. Using exponential smoothing with
a smoothing factor (alpha) of 0.7, Sunrise’s projected sales for July would be
Answer (B) is correct.
The formula for forecasting sales using exponential smoothing is
Ft = (?)xt – 1 + (1 – ?)Ft – 1 , where F = the forecast for a period, t = the
time period, ? = the smoothing factor (0 < ? < 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) = (?) × May’s actual sales + (1 – ?) × May’s projected sales
= (.7) × 22,000,000 + (1 – .7) × (22,000,000 – 600,000)
= 15,400,000 + 6,420,000
Now calculate July’s projected sales as follows:
F(July) = (?) × June’s actual sales + (1 – ?) × June’s projected sales
= (.7) × 22,500,000 + (1 – .7) × (21,820,000)
= 15,750,000 + 6,546,000
A common characteristic of simple regression analysis, learning curve analysis, and time
series analysis is that they all.
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.