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In time series data depicting demand which of the following is not considered a component of demand variation? p.504
Variation. Variance is a measure of the degree of error, not a component of demand variation. E.g., Several common terms used to describe the degree of error are standard error, mean squared error (or variance), and mean absolute deviation.
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Which of the following is not one of the basic types of forecasting? p. 486
Force field analysis. Forecasting can be classified into four basic types: qualitative, time series analysis, causal relationships, and simulation.
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In most cases, demand for products or services can be broken down into several components. Which of the following is not considered a component of demand?(p. 486)
Past data. In most cases, demand for products or services can be broken down into six components: average demand for the period, a trend, seasonal elements, cyclical elements, random variation, and autocorrelation.
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In most cases, demand for products or services can be broken into several components. Which of the following is considered a component of demand?(p. 486)
Cyclical elements. In most cases, demand for products or services can be broken down into six components: average demand for the period, a trend, seasonal elements, cyclical elements, random variation, and autocorrelation.
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In most cases, demand for products or services can be broken into several components. Which of the following is considered a component of demand?(p. 486)
Autocorrelation. In most cases, demand for products or services can be broken down into six components: average demand for the period, a trend, seasonal elements, cyclical elements, random variation, and autocorrelation.
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Which of the following forecasting methodologies is considered a qualitative forecasting technique?
Market research. Market research is used mostly for product research in the sense of looking for new product ideas, likes and dislikes about existing products, which competitive products within a particular class are preferred, and so on. Again, the data collection methods are primarily surveys and interviews.
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Which of the following forecasting methodologies is considered a time series forecasting technique? (p.498)
Simple moving average. Simple moving average is the only choice that attempts to predict future values of demand based upon past data.
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Which of the following forecasting methodologies is considered a time series forecasting technique?
(p. 499)
Weighted moving average. Weighted moving average is the only choice that attempts to predict future values of demand based upon past data.
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Which of the following forecasting methodologies is considered a causal forecasting technique?
(p. 486)
Linear regression. Causal forecasting, which we discuss using the linear regression technique, assumes that demand is related to some underlying factor or factors in the environment.
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Which of the following forecasting methods uses executive judgment as its primary component for forecasting?(p. 509)
Panel consensus. In a panel consensus, the idea that two heads are better than one is extrapolated to the idea that a panel of people from a variety of positions can develop a more reliable forecast than a narrower group. Panel forecasts are developed through open meetings with free exchange of ideas from all levels of management and individuals. When decisions in forecasting are at a broader, higher level (as when introducing a new product line or concerning strategic product decisions such as new marketing areas), the term executive judgment is generally used.
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Which of the following forecasting methods is very dependent on selection of the right individuals who will judgmentally be used to actually generate the forecast?(p. 510)
Delphi method. The step-by-step procedure for the Delphi method is: 1. Choose the experts to participate. There should be a variety of knowledgeable people in different areas.
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In business forecasting, what is usually considered a short-term time period?
(p. 488)
Less than 3 months. In business forecasting short term usually refers to under three months.
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In business forecasting, what is usually considered a medium-term time period? (p.488)
Three months to two years. In business forecasting medium term (refers to) three months to two years.
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In business forecasting, what is usually considered a long-term time period?
(p. 488)
Two years or longer. In business forecasting long term (refers to) greater than two years.
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In general, which forecasting time frame compensates most effectively for random variation and short term changes?(p. 488)
Short-term forecasts. In general, the short-term models compensate for random variation and adjust for short-term changes (such as consumers' responses to a new product).
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In general, which forecasting time frame best identifies seasonal effects?
(p. 488)
Medium term forecasts. Medium-term forecasts are useful for capturing seasonal effects.
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In general, which forecasting time frame is best to detect general trends? (p.
Long range forecasts. Long-term models detect general trends and are especially useful in indentifying major turning points
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Which of the following forecasting methods can be used for short-term forecasting?
(p. 488)
Simple exponential smoothing. See exhibit 15.3, page 488.
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Which of the following considerations is not a factor in deciding which forecasting model a firm should choose?(p. 488)
Product. Which forecasting model a firm should choose depends on: (1) Time horizon to forecast; (2) Data availability; (3) Accuracy required; (4) Size of forecasting budget; (5) Availability of qualified personnel.
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A company wants to forecast demand using the simple moving average. If the company uses four prior yearly sales values (i.e., year 2008 = 100, year 2009 = 120, year 2010 = 140, and year 2011 = 210), which of the following is the simple moving average forecast for year 2012?(p. 498)
142.5 Using equation 15.5 (page 498) Forecast for 2012 = (100 + 120 + 140 + 210)/4 = 570/4 = 142.5
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A company wants to forecast demand using the simple moving average. If the company uses three prior yearly sales values (i.e., year 2009 = 130, year 2010 = 110, and year 2011 = 160), which of the following is the simple moving average forecast for year 2012?(p. 498)
133.3 Using equation 15.5 (page 498) Forecast for 2012 = (130 + 110 + 160)/3 = 400/4 = 133.3
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A company wants to forecast demand using the weighted moving average. If the company uses two prior yearly sales values (i.e., year 2011 = 110 and year 2012 = 130), and we want to weight year 2011 at 10% and year 2012 at 90%, which of the following is the weighted moving average forecast for year 2013?(p. 500)
128 Using equation 15.6 (page 500) Forecast for 2013 = (110x0.1) + (130x0.9) = 11 + 117 = 128
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A company wants to forecast demand using the weighted moving average. If the company uses three prior yearly sales values (i.e., year 2010 = 160, year 2011 = 140 and year 2012 = 170), and we want to weight year 2010 at 30%, year 2011 at 30% and year 2012 at 40%, which of the following is the weighted moving average forecast for year 2013?(p. 500)
158 Using equation 15.6 (page 500) Forecast for 2013 = (160x0.3) + (140x0.3) + (170x0.4) = 158
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Which two of the following are among the major reasons that exponential smoothing has become well accepted as a forecasting technique?(p. 501)
- Accuracy. Exponential smoothing techniques have become well accepted for six major reasons:
- 1. Exponential models are surprisingly accurate.
- 2. Formulating an exponential model is relatively easy.
- 3. The user can understand how the model works.
- 4. Little computation is required to use the model.
- 5. Computer storage requirements are small because of the limited use of historical data.
- 6. Tests for accuracy as to how well the model is performing are easy to compute.
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The exponential smoothing method requires which of the following data to forecast the future?
(p. 501)
The most recent forecast. In the exponential smoothing method, only three pieces of data are needed to forecast the future: the most recent forecast, the actual demand that occurred for that forecast period, and a smoothing constant alpha.
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Given a prior forecast demand value of 230, a related actual demand value of 250, and a smoothing constant alpha of 0.1, what is the exponential smoothing forecast value for the following period?
232Using equation 15.7, Forecast = 230 + 0.1 x (250 - 230) = 232
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If a firm produced a standard item with relatively stable demand, the smoothing constant alpha used in an exponential smoothing forecasting model would tend to be in which of the following ranges?(p. 501)
5% to 10% If a firm produced a standard item with relatively stable demand, the reaction rate to differences between actual and forecast demand would tend to be small, perhaps just 5 or 10 percentage points.
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If a firm produced a product that is experiencing growth in demand, the smoothing constant alpha used in an exponential smoothing forecasting model would tend to be which of the following?(p. 501)
The more rapid the growth, the higher the percentage.If a firm were experiencing growth, it would be desirable to have a higher reaction rate, perhaps 15 to 30 percentage points, to give greater importance to recent growth experience. The more rapid the growth, the higher the reaction rate should be.
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Given a prior forecast demand value of 1,100, a related actual demand value of 1,000, and a smoothing constant alpha of 0.3, what is the exponential smoothing forecast value?
1030 Using equation 15.7, Forecast = 1100 + 0.3 x (1100 - 1000) = 1030
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A company wants to generate a forecast for unit demand for year 2012 using exponential smoothing. The actual demand in year 2011 was 120. The forecast demand in year 2011 was 110. Using this data and a smoothing constant alpha of 0.1, which of the following is the resulting year 2012 forecast value?
111 Using equation 15.7, Forecast = 110 + 0.1 x (120 - 110) = 111
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As a consultant you have been asked to generate a unit demand forecast for a product for year 2012 using exponential smoothing. The actual demand in year 2011 was 750. The forecast demand in year 2011 was 960. Using this data and a smoothing constant alpha of 0.3, which of the following is the resulting year 2012 forecast value?
897 Using equation 15.7, Forecast = 960 + 0.3 x (960 - 750) = 897
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Which of the following is a possible source of bias error in forecasting?
(p. 504)
A. Failing to include the right variables
B. Using the wrong forecasting method
C. Employing less sophisticated analysts than necessary
D. Using incorrect data
E. Using standard deviation rather than MAD
Failing to include the right variables. Bias errors occur when a consistent mistake is made. Sources of bias include the failure to include the right variables; the use of the wrong relationships among variables; employing of the wrong trend line; a mistaken shift in the seasonal demand from where it normally occurs; and the existence of some undetected secular trend.
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Which of the following are used to describe the degree of error?
(p. 504
Mean absolute deviation. Several common terms used to describe the degree of error are standard error, mean squared error (or variance), and mean absolute deviation.
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A company has actual unit demand for three consecutive years of 124, 126, and 135. The respective forecasts for the same three years are 120, 120, and 130. Which of the following is the resulting MAD value that can be computed from this data?(p. 504)
5. Using equation 15.11 on page 504, MAD = ABS((124 - 120) + (126 - 120) + (135 - 130))/3 = 15/3 = 5
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A company has actual unit demand for four consecutive years of 100, 105, 135, and 150. The respective forecasts were 120 for all four years. Which of the following is the resulting MAD value that can be computed from this data?(p. 504)
20. Using equation 15.11 on page 504, MAD = ABS((100 - 120) + (105 - 120) + (135 - 120) + (150 - 120))/4 = 80/4 = 20
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If you were selecting from a variety of forecasting models based on MAD, which of the following MAD values from the same data would reflect the most accurate model?(p. 505)
0.2 MAPE gauges the error relative to the average demand. For example, if the MAD is 10 units and average demand is 20 units, the error is large and significant, but relatively insignificant on an average demand of 1,000 units. Since the same data is being used in the question, MAPE would be least when MAD was smallest. Therefore A is the correct answer.
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A company has calculated its running sum of forecast errors to be 500 and its mean absolute deviation is exactly 35. Which of the following is the company's tracking signal?(p. 506)
About 14.3 Using equation 15.13 (page 506) the tracking signal is RSFE/MAD = 500/35 = 14.29.
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A company has a MAD of 10. Its wants to have a 99.7 percent control limits on its forecasting system. It's most recent tracking signal value is 3.1. What can the company conclude from this information?(p. 505- 506)
The forecasting model is operating acceptably. Tracking Signal = RSFE/MAD hence, 3.1 = RSFE/10 or RSFE =3.1 x 10 = 31. MAD = 10, SD = 1.25 x MAD = 12.5. Since 99.7 percent corresponds to 3 standard deviations from the mean, RSFE would have to be higher than 3 x 12.5 or 37.5 for the forecasting model to be out of control.
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You are hired as a consultant to advise a small firm on forecasting methodology. Based on your research you find the company has a MAD of 3. Its wants to have a 99.7 percent control limits on its forecasting system. It's most recent tracking signal value is 15. What should be your report to the company?(p. 505- 506)
The forecasting model is out of control and needs to be corrected. Tracking Signal = RSFE/MAD hence, 15 = RSFE/3 or RSFE = 15 x 3 = 45. MAD = 3, SD = 1.25 x MAD = 3.75. Since 99.7 percent corresponds to 3 standard deviations from the mean, (3 x 3.75 = 11.25). Since RSFE is 45, the forecasting model is out of control.
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Which of the following is the portion of observations you would expect to see lying within a plus or minus 3 MAD range?(p. 505)
98.36 % 3 MAD x 0.8 = 2.4 Standard Deviations (page 505). From Appendix D, 2.4 standard deviations includes = 0.4918 of the area x 2 = 0.9836 or 98.36%
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Which of the following is the portion of observations you would expect to see lying within a plus or minus 2 MAD range?(p. 505)
89.04
2 MAD x 0.8 = 1.6 Standard Deviations (page 505). From Appendix D, 1.6 standard deviations includes = 0.4452 of the area x 2 = 0.8904 or 89.04%
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If the intercept value of a linear regression model is 40, the slope value is 40, and the value of X is 40, which of the following is the resulting forecast value using this model?(p. 489)
1640 The linear regression line is of the form Y = a + bX, where Y is the value of the dependent variable that we are solving for, a is the Y intercept, b is the slope, and X is the independent variable. Hence, Y = 40 + 40 x 40 = 1,640.
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A company hires you to develop a linear regression forecasting model. Based on the company's historical sales information, you determine the intercept value of the model to be 1,200. You also find the slope value is minus 50. If after developing the model you are given a value of X = 10, which of the following is the resulting forecast value using this model?(p. 489)
700 The linear regression line is of the form Y = a + bX, where Y is the value of the dependent variable that we are solving for, a is the Y intercept, b is the slope, and X is the independent variable. Hence, Y = 1,200 + ( - 50) x 10 = 700.
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Heavy sales of umbrellas during a rain storm is an example of which of the following?
(p. 507)
A casual relationship. We can expect that an extended period of rain will increase sales of umbrellas and raincoats. The rain causes the sale of rain gear. This is a causal relationship, where one occurrence causes another.
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You are using an exponential smoothing model for forecasting. The running sum of the forecast error statistics (RSFE) are calculated each time a forecast is generated. You find the last RSFE to be 34. Originally the forecasting model used was selected because it's relatively low MAD of 0.4. To determine when it is time to re-evaluate the usefulness of the exponential smoothing model you compute tracking signals. Which of the following is the resulting tracking signal?(p. 506)
85 Using equation 15.13, page 506, TS = RSFE/MAD = 34/0.4 = 85.
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