C. what customers' future plans are
D. whether customers are satisfied or dissatisfied with their performance in the past
E. what the salesperson's appropriate sales quota should be
Knowledge about what customers are likely to do is much more valuable than information regarding what customers plan or want to do.
AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-06 Describe four qualitative forecasting techniques. Level of Difficulty: 2 Medium Topic: Qualitative Forecasts
Essay Questions
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140. Develop a forecast for the next period, given the data below, using a three-period moving average.
Feedback: Average demand from periods 3 through 5.
AACSB: Analytic Blooms: Apply Learning Objective: 03-08 Prepare a moving average forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data
141. Consider the data below:
Using exponential smoothing with alpha = .2, and assuming the forecast for period 11 was 80, what would the forecast for period 14 be?
Feedback: The forecast error in period 13 (2.84) is multiplied by the smoothing constant. This is then added to the period 13 forecast to get the period 14 forecast.
AACSB: Analytic Blooms: Apply Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data
142. A manager is using exponential smoothing to predict merchandise returns at a suburban branch of a department store chain. Given a previous forecast of 140 items, an actual number of returns of 148 items, and a smoothing constant equal to .15, what is the forecast for the next period?
Feedback: The forecast error in the previous period is multiplied by the smoothing constant. This is then added to the previous period's forecast to get the upcoming period's forecast.
AACSB: Analytic Blooms: Apply Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data
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143. A manager is using the equation below to forecast quarterly demand for a product: Yt = 6,000 + 80t where t = 0 at Q2 of last year
Quarter relatives are Q1 = .6, Q2 = .9, Q3 = 1.3, and Q4 = 1.2.
What forecasts are appropriate for the last quarter of this year and the first quarter of next year?
For Q4 of this year t = 6 For Q1 of next year t = 7
Feedback: Adjust deseasonalized forecasts by the quarterly seasonal relatives.
AACSB: Analytic Blooms: Apply Learning Objective: 03-11 Prepare a linear trend forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data
144. Over the past five years, a firm's sales have averaged 250 units in the first quarter of each year, 100 units in the second quarter, 150 units in the third quarter, and 300 units in the fourth quarter. What are appropriate quarter relatives for this firm's sales? Hint: Only minimal computations are necessary.
145. A manager has been using a certain technique to forecast demand for gallons of ice cream for the past six periods. Actual and predicted amounts are shown below. Would a naive forecast have produced better results?
Current method: MAD = 3.67; MSE = 16.8; 2s control limits ± 8.2 (OK) Naive method: MAD = 4.40; MSE = 30.0; 2s control limits ± 11.0 (OK) Feedback: Either MSE or MAD should be computed for both forecasts and
compared. The demand data are stable. Therefore, the most recent value of the series is a reasonable forecast for the next period of time, justifying the naive approach. The current method is slightly superior both in terms of MAD and MSE. Either method would be considered in control.
AACSB: Analytic Blooms: Apply Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 3 Hard Topic: Forecast Accuracy
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146. A new car dealer has been using exponential smoothing with an alpha of .2 to forecast weekly new car sales. Given the data below, would a naive forecast have provided greater accuracy? Explain. Assume an initial exponential forecast of 60 units in period 2 (i.e., no forecast for period 1).
Exponential method: MAD = 1.70; MSE = 6.34 Naive method: MAD = 3.00; MSE = 15.25
Feedback: The exponential forecast method appears to be superior because both MAD and MSE are lower when it is used.
AACSB: Analytic Blooms: Apply Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 3 Hard Topic: Forecast Accuracy
147. A CPA firm has been using the following equation to predict annual demand for tax audits: Yt = 55 + 4t. Demand for the past few years is shown below. Is the forecast performing as well as it might? Explain.
MSE = 11/6 and s = = 3.41. Even with ± 2s limits (6.82), all values are within the limits. It seems, then, that only random variation is present, so one might say that the forecast is working. One might also observe that the first three errors are negative and the last three are positive. Although six observations constitute a relatively small sample, it may be that the errors are cycling, and this would be a matter to investigate with additional data.
Feedback: Either a tracking signal or a control chart is called for. To conduct these assessments, it is necessary to generate the forecasts so that errors can be determined.
AACSB: Analytic Blooms: Apply Learning Objective: 03-15 Construct control charts and use them to monitor forecast errors. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data
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148. Given the data below, develop a forecast for period 6 using a four-period weighted moving average and weights of .4, .3, .2 and .1.
.4(17) + .3(19) + .2(18) + .1(20) = 18.1
Feedback: Multiply demand observed in periods 2 through 5 by the appropriate weight, then sum these products.
AACSB: Analytic Blooms: Apply Learning Objective: 03-09 Prepare a weighted-average forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data
149. Use linear regression to develop a predictive model for demand for burial vaults based on sales of caskets.
Feedback: Least-squares estimation leads to this regression equation.
AACSB: Analytic Blooms: Apply Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 3 Hard Topic: Associative Forecasting Techniques
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150. Given the following data, develop a linear regression model for y as a function of x.
Feedback: Least squares estimation leads to this regression equation.
AACSB: Analytic Blooms: Apply Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 2 Medium Topic: Associative Forecasting Techniques
151. Given the following data, develop a linear regression model for y as a function of x.
Feedback: Least squares estimation leads to this regression equation.
AACSB: Analytic Blooms: Apply Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 3 Hard Topic: Associative Forecasting Techniques
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152. Develop a linear trend equation for the data on bread deliveries shown below. Forecast deliveries for period 11 through 14.
Yt = 518.2 + 52.164t r = +.935
Feedback: Formulate the regression equation using least squares estimation, then apply the result to periods 11 through 14.
AACSB: Analytic Blooms: Apply Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data
153. Demand for the last four months was:
A) Predict demand for July using each of these methods: 1) a three-period moving average
2) exponential smoothing with alpha equal to .20 (use a naive forecast for April for your first forecast)
B) If the naive approach had been used to predict demand for April through June, what would MAD have been for those months?
Feedback: The naive approach leads to absolute forecast errors of two units in each period.
AACSB: Analytic Blooms: Apply Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data
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154. A manager wants to choose one of two forecasting alternatives. Each alternative was tested using historical data. The resulting forecast errors for the two are shown in the table. Analyze the data and recommend a course of action to the manager.
Although Alternative 1 has the smaller MSE, it appears to be cycling and steady; Alternative 2 errors after the first three periods are small or zero. For the last six periods, Alternative 2 was much better, suggesting that approach would be better. Feedback: Although Alternative 1 has the smaller MSE, it appears to be cycling and steady; Alternative 2 errors after the first three periods are small or zero. For the last six periods, Alternative 2 was much better, suggesting that approach would be better.
AACSB: Analytic Blooms: Apply Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 3 Hard Topic: Forecast Accuracy
155. A manager uses this equation to predict demand: Yt = 20 + 4t. Over the past eight periods, demand has been as follows. Are the results acceptable? Explain.
s = 2.10; 2s control limits are ± 4.20. Although all values are within control limits, the errors may be exhibiting cyclical patterns, which would suggest
nonrandomness.
Feedback: s = 2.10; 2s control limits are ± 4.20. Although all values are within control limits, the errors may be exhibiting cyclical patterns, which would suggest nonrandomness.
AACSB: Analytic Blooms: Apply Learning Objective: 03-15 Construct control charts and use them to monitor forecast errors. Level of Difficulty: 2 Medium Topic: Approaches to Forecasting
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156. Data on demand over the last few years are available as follows:
What would this year's forecast be if we were using the naive approach?
49
Feedback: This year's forecast would be last year's demand.
AACSB: Analytic Blooms: Apply Learning Objective: 03-07 Use a naive method to make a forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data
157. Data on demand over the last few years are available as follows:
What is this year's forecast using a four-year simple moving average?
45.5
Feedback: Average the four most recent periods of demand.
AACSB: Analytic Blooms: Apply Learning Objective: 03-08 Prepare a moving average forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data
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158. Data on demand over the last few years are available as follows:
What is this year's forecast using exponential smoothing with alpha = .25, if last year's smoothed forecast was 45?
45.8
Feedback: Multiply last year's forecast error by the smoothing constant. Add the resulting product to last year's forecast to get this year's forecast.
AACSB: Analytic Blooms: Apply Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data
159. Data on demand over the last few years are available as follows:
What are this and next year's forecasts using the least squares trend line for these data?
62; 69
Feedback: Treat the earliest period as period 0 in formulating least squares coefficients, then proceed.
AACSB: Analytic Blooms: Apply Learning Objective: 03-11 Prepare a linear trend forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data
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160. Data on demand over the last few years are available as follows:
What is this year's forecast using trend-adjusted (double) smoothing with alpha = . 2 and beta = .1, if the forecast for last year was 56, the forecast for two years ago was 46, and the trend estimate for last year's forecast was 7?
61.76
Feedback: Smooth both the trend and the forecasts using the appropriate smoothing coefficients.
AACSB: Analytic Blooms: Apply Learning Objective: 03-12 Prepare a trend-adjusted exponential smoothing forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data
161. Data on the last three years of demand are available as follows:
What is the centered moving average for spring two years ago?
29
Feedback: First average the four periods beginning fall three years ago. Then average the four periods beginning spring two years ago. Then average these two averages.
AACSB: Analytic Blooms: Apply Learning Objective: 03-13 Compute and use seasonal relatives. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data
162. Data on the last three years of demand are available as follows:
What is the spring's seasonal relative?
Spring = 0.91
Feedback: Divide data points by centered moving averages where moving averages are available. Average the resulting values across the seasons to get the seasonal relatives.
AACSB: Analytic Blooms: Apply Learning Objective: 03-13 Compute and use seasonal relatives. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data
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163. Data on the last three years of demand are available as follows:
What is the linear regression trend line for these data (t = 0 for spring, three years ago)?
y = 17 + 2.33t
Feedback: Used deseasonalized data points to formulate least squares coefficients.
AACSB: Analytic Blooms: Apply Learning Objective: 03-12 Prepare a trend-adjusted exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data
164. Data on the last three years of demand are available as follows:
What is this year's seasonally adjusted forecast for each season?
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