Business Forecasting Study Set 1

Business

Quiz 10 :

Managing the Forecasting Process

Quiz 10 :

Managing the Forecasting Process

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Write a short response to each of the following assertions: a. Forecasts are always wrong, so why place any emphasis on demand planning? b. A good forecasting process is too expensive. c. All we need to do is hire a "quantitative type" to do our forecasting.
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Answer will vary. One of the possible answers is given below:
a.
Forecasts are not always right, but they might improve the chances of being close to right about the given process. More significantly, if the forecasts are not accepted to plan the process for the future, then there is a chance of developing own measures by different groups to guide planning, which may end up with probable confusion.
b.
Good forecasting techniques may be expensive. However, without good forecasts, there is a chance of ineffective process; low returns on investments, lack of customer service are some of the impacts. For example, knowledge cannot be obtained if a person thinks that education is expensive. Moreover, containing a good set of forecasts is similar to walking looking ahead rather than looking at shoes.
c.
The principal importance in forecasting is looking for a patterns business environment at over time. There are several types of forecasting techniques. The quantitative technique is one of them. Not only the quantitative skill is required, and also required a business understanding, the forecasting environment, and a good communication skills, so that the management is satisfied with the forecasts.

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BOUNDARY ELECTRONICS Boundary Electronics is a large supplier of electronic products for home use. Among its largest sellers are home video recorders and satellite television systems. Because the company's business has grown so rapidly, Guy Preston, Boundary's president, is concerned that a change in market conditions could alter its sales pattern. In asking his managers about the future of the company, Guy has discovered two things. First, most of his managers are too busy thinking about the day-to-day problems of meeting growing demand to give much thought to the long-range future. Second, their opinions vary considerably from quite optimistic to quite pessimistic. As president of the company, Guy feels he has an obligation to seriously consider the future environment of his company. After thinking about this matter, Guy plans a Saturday retreat for the six members of his top management team. He rents a meeting room in a local hotel and arranges for lunch and coffee breaks for the day. When the team meets Saturday morning, he introduces the topic of the day and then instructs each person to prepare a one- or two-page description of the company's operating environment over the next 20 years for each of the following situations: 1. The company's environment will continue essentially as it is now. Products demanded by the market will be modifications of current products, and no new technology will intervene. 2. Major technological changes will render the company's current line of products obsolete. New products will have to be developed to meet the leisure demands of the American population. 3. Between these two extremes, what is the most likely scenario for the company's operating environment? Guy allows one hour for each team member to develop the scenarios for each of these three situations. During this hour, Guy thinks about the rest of the day and what will happen. He hopes that there will be some provocative ideas developed by his managers and that subsequent discussions will prove lively and interesting. In addition to gaining ideas for his own use, Guy hopes that the day's exercise will help his managers look beyond the company's immediate problems and opportunities and give them a more long-range view of the company. What process do you think Guy should use after the hour's writing activities have been completed?
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Explanation:
From the given case, it is clear that Guy instructs his managers to describe the operating environment of the company over the next 20 years for the given three situations. In other words, the managers were made to think the position of the company in a long range than to think in a short range. This may lead to a discussion rather than to shorten their plans, which may occur due to the daily pressure of business.
Here, the managers would develop descriptions (written scenarios) about the three situations for the future. Then, Guy would have a group discussion to discuss about the managers' opinions. Moreover, different opinions might come out which may lead to the second round of discussion. In other words, the managers might be asked to write scenarios based on the discussion.

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BOUNDARY ELECTRONICS Boundary Electronics is a large supplier of electronic products for home use. Among its largest sellers are home video recorders and satellite television systems. Because the company's business has grown so rapidly, Guy Preston, Boundary's president, is concerned that a change in market conditions could alter its sales pattern. In asking his managers about the future of the company, Guy has discovered two things. First, most of his managers are too busy thinking about the day-to-day problems of meeting growing demand to give much thought to the long-range future. Second, their opinions vary considerably from quite optimistic to quite pessimistic. As president of the company, Guy feels he has an obligation to seriously consider the future environment of his company. After thinking about this matter, Guy plans a Saturday retreat for the six members of his top management team. He rents a meeting room in a local hotel and arranges for lunch and coffee breaks for the day. When the team meets Saturday morning, he introduces the topic of the day and then instructs each person to prepare a one- or two-page description of the company's operating environment over the next 20 years for each of the following situations: 1. The company's environment will continue essentially as it is now. Products demanded by the market will be modifications of current products, and no new technology will intervene. 2. Major technological changes will render the company's current line of products obsolete. New products will have to be developed to meet the leisure demands of the American population. 3. Between these two extremes, what is the most likely scenario for the company's operating environment? Guy allows one hour for each team member to develop the scenarios for each of these three situations. During this hour, Guy thinks about the rest of the day and what will happen. He hopes that there will be some provocative ideas developed by his managers and that subsequent discussions will prove lively and interesting. In addition to gaining ideas for his own use, Guy hopes that the day's exercise will help his managers look beyond the company's immediate problems and opportunities and give them a more long-range view of the company. Is there some other approach that Guy might have tried, given his objectives?
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Answer:

Answer will vary. One of the possible answers given below:
From the given case, it is clear that Guy's retreat is to help his managers to look ahead of company's problems and opportunities which would give a long-range view. After the first round of discussion on the written sceneries, there is a possibility that the managers are still struck up with the day-to-day business and are not fully involved in long-range thinking. Therefore, the results of the first round of discussion can summarized and provided to managers for further consideration. After the second round, members are given the opportunity to change prior opinions based on provided feedback. The empirical studies support that accuracy tends to increase and the decision tend to be more accurate than unstructured planning. By doing this process, long-range thinking can be promoted.

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BOUNDARY ELECTRONICS Boundary Electronics is a large supplier of electronic products for home use. Among its largest sellers are home video recorders and satellite television systems. Because the company's business has grown so rapidly, Guy Preston, Boundary's president, is concerned that a change in market conditions could alter its sales pattern. In asking his managers about the future of the company, Guy has discovered two things. First, most of his managers are too busy thinking about the day-to-day problems of meeting growing demand to give much thought to the long-range future. Second, their opinions vary considerably from quite optimistic to quite pessimistic. As president of the company, Guy feels he has an obligation to seriously consider the future environment of his company. After thinking about this matter, Guy plans a Saturday retreat for the six members of his top management team. He rents a meeting room in a local hotel and arranges for lunch and coffee breaks for the day. When the team meets Saturday morning, he introduces the topic of the day and then instructs each person to prepare a one- or two-page description of the company's operating environment over the next 20 years for each of the following situations: 1. The company's environment will continue essentially as it is now. Products demanded by the market will be modifications of current products, and no new technology will intervene. 2. Major technological changes will render the company's current line of products obsolete. New products will have to be developed to meet the leisure demands of the American population. 3. Between these two extremes, what is the most likely scenario for the company's operating environment? Guy allows one hour for each team member to develop the scenarios for each of these three situations. During this hour, Guy thinks about the rest of the day and what will happen. He hopes that there will be some provocative ideas developed by his managers and that subsequent discussions will prove lively and interesting. In addition to gaining ideas for his own use, Guy hopes that the day's exercise will help his managers look beyond the company's immediate problems and opportunities and give them a more long-range view of the company. Do you think Guy will accomplish his objectives with the Saturday meeting?
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BUSBY ASSOCIATES Jill Tilly was a recent graduate of a university business school when she took a job with Busby Associates, a large exporter of farm equipment. Busby's president noticed a forecasting course on Jill's resume during the hiring process and decided to start Jill's employment with a forecasting project that had been discussed many times by Busby's top managers. Busby's president believed there was a strong relationship between the company's export sales and the national figures for exports. The national figures were readily available from government sources, so Jill's project was to forecast a good, representative export variable. If this effort was successful, Busby's president believed the company would have a powerful tool for forecasting its own export sales. At the time, Jill located the most recent copy of the Survey of Current Business in a local library and recorded the quarterly figures for consumer goods exports in billions of dollars. She believed this variable was a good representative of total national exports. Anticipating the possibility of forecasting using regression analysis, she also recorded values for other variables she thought might possibly correlate well with this dependent variable. She ended up with values for four variables for 14 quarters. She then computed three additional variables from her dependent variable values: change in Y, percent change in Y , and Y lagged one period. By the time she began to think about various ways to forecast her variable, she had collected the data shown in Table 11-2. Jill keyed her data into a computer program that performed regression analysis and computed the correlation matrix for her seven variables. After examining this matrix, she chose three regressions with a single predictor variable and six regressions with two predictor variables. She then ran these regressions and chose the one she considered best: It used one predictor ( Y lagged one period) with the following results: img Jill examined the residual autocorrelations and found them to be small. She concluded she did not need to correct for any autocorrelation in her regression. Jill believed that she had found a good predictor variable ( Y lagged one period). img a Estimated. Variable key: 1: Consumer goods, exports, billions of dollars 2: Gross personal savings, billions of dollars 3: National income retail trade, billions of dollars 4: Fixed weight price indexes for national defense purchases, military equipment, 1982 = 100 5: Change in dependent variable from previous period 6: Percent change in dependent variable from previous period 7: Dependent variable lagged one period Source for variables 1 through 4: Survey of Current Business (U.S. Department of Commerce) 70 (7) (July 1990). Jill realized that her sample size was rather small: 13 quarters. She returned to the Survey of Current Business to collect more data points and was disappointed to find that during the years in which she was interested the definition of her dependent variable changed, resulting in an inconsistent time series. That is, the series took a jump upward halfway through the period that she was studying. Jill pointed out this problem to her boss, and it was agreed that total merchandise exports could be used as the dependent variable instead of consumer goods exports. Jill found that this variable remained consistent through several issues of the Survey of Current Business and that several years of data could be collected. She collected the data shown in Table 11-3, lagged the data one period, and again ran a regression analysis using Y lagged one period as the predictor variable. This time she again found good statistics in her regression printout, except for the lag 1 residual autocorrelation. That value,.47, was significantly different from zero at the 5% level. Jill was unsure what to do. She tried additional runs, including the period number and the change in Y as additional predictor variables. But after examining the residuals, she was unable to conclude that the autocorrelation had been removed. Jill decided to look at other forecasting techniques to forecast her new dependent variable: total merchandise exports. She used the time series data shown in the Y column of Table 11-3. The time series plot of total merchandise exports for the years 1984 through the second quarter of 1990 is shown in Figure 11-4. After studying Figure 11-4, Jill decided to use only the last 16 data points in her forecasting effort. She reasoned that beginning with period 9 the series had shown a relatively steady increase, whereas before that period it had exhibited an increase and a decline. An examination of her data revealed no seasonality, so Jill decided to try two smoothing methods: Simple exponential smoothing Holt's linear exponential smoothing, which can accommodate a trend in the data For simple exponential smoothing, Jill restricted the smoothing constant, a, to lie between 0 and 1 and found the best a to be very close to 1. The Minitab program selected the optimal values for the Holt smoothing constants, a and b. Error measures and the choices for the smoothing constants are shown in Table 11-4. Given the results shown in Table 11-4, Jill decided Holt's linear smoothing offered the most promise and used this procedure to generate forecasts for the next four quarters. Jill noted that the optimum smoothing constant using simple exponential smoothing was almost 1.00 (.99). Apparently, in order to track through the data in this fashion, the program was basically using each data value to predict the next. This is equivalent to using a simple naive method to forecast-that is, a model that says consecutive differences are random. img FIGURE 11-4 Plot of Quarterly Data Values: Total Merchandise Exports, First Quarter of 1984 to Second Quarter of 1990 ($ billions) img Jill realized that with each passing quarter a new actual value of total merchandise exports would be available and that the forecasts for future periods could be updated. img Using Holt's smoothing method, the forecasts for the next four quarters beyond the end of her data are img Jill then met with her boss to discuss her results. She indicated that she thought she had a good way to forecast the national variable, total merchandise exports, using exponential smoothing with trend adjustments. Her boss asked her to explain this method, which she did. Her next assignment was to use actual data to verify the hunch of Busby's president: that Busby's exports were well correlated with national exports. If she could establish this linkage, Busby would have a good forecasting method for its exports and could use the forecasts to plan future operations. Jill did not consider combining the forecasts generated by the two methods she analyzed. How would she go about doing so? What would be the advantages and disadvantages of such action?
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BUSBY ASSOCIATES Jill Tilly was a recent graduate of a university business school when she took a job with Busby Associates, a large exporter of farm equipment. Busby's president noticed a forecasting course on Jill's resume during the hiring process and decided to start Jill's employment with a forecasting project that had been discussed many times by Busby's top managers. Busby's president believed there was a strong relationship between the company's export sales and the national figures for exports. The national figures were readily available from government sources, so Jill's project was to forecast a good, representative export variable. If this effort was successful, Busby's president believed the company would have a powerful tool for forecasting its own export sales. At the time, Jill located the most recent copy of the Survey of Current Business in a local library and recorded the quarterly figures for consumer goods exports in billions of dollars. She believed this variable was a good representative of total national exports. Anticipating the possibility of forecasting using regression analysis, she also recorded values for other variables she thought might possibly correlate well with this dependent variable. She ended up with values for four variables for 14 quarters. She then computed three additional variables from her dependent variable values: change in Y, percent change in Y , and Y lagged one period. By the time she began to think about various ways to forecast her variable, she had collected the data shown in Table 11-2. Jill keyed her data into a computer program that performed regression analysis and computed the correlation matrix for her seven variables. After examining this matrix, she chose three regressions with a single predictor variable and six regressions with two predictor variables. She then ran these regressions and chose the one she considered best: It used one predictor ( Y lagged one period) with the following results: img Jill examined the residual autocorrelations and found them to be small. She concluded she did not need to correct for any autocorrelation in her regression. Jill believed that she had found a good predictor variable ( Y lagged one period). img a Estimated. Variable key: 1: Consumer goods, exports, billions of dollars 2: Gross personal savings, billions of dollars 3: National income retail trade, billions of dollars 4: Fixed weight price indexes for national defense purchases, military equipment, 1982 = 100 5: Change in dependent variable from previous period 6: Percent change in dependent variable from previous period 7: Dependent variable lagged one period Source for variables 1 through 4: Survey of Current Business (U.S. Department of Commerce) 70 (7) (July 1990). Jill realized that her sample size was rather small: 13 quarters. She returned to the Survey of Current Business to collect more data points and was disappointed to find that during the years in which she was interested the definition of her dependent variable changed, resulting in an inconsistent time series. That is, the series took a jump upward halfway through the period that she was studying. Jill pointed out this problem to her boss, and it was agreed that total merchandise exports could be used as the dependent variable instead of consumer goods exports. Jill found that this variable remained consistent through several issues of the Survey of Current Business and that several years of data could be collected. She collected the data shown in Table 11-3, lagged the data one period, and again ran a regression analysis using Y lagged one period as the predictor variable. This time she again found good statistics in her regression printout, except for the lag 1 residual autocorrelation. That value,.47, was significantly different from zero at the 5% level. Jill was unsure what to do. She tried additional runs, including the period number and the change in Y as additional predictor variables. But after examining the residuals, she was unable to conclude that the autocorrelation had been removed. Jill decided to look at other forecasting techniques to forecast her new dependent variable: total merchandise exports. She used the time series data shown in the Y column of Table 11-3. The time series plot of total merchandise exports for the years 1984 through the second quarter of 1990 is shown in Figure 11-4. After studying Figure 11-4, Jill decided to use only the last 16 data points in her forecasting effort. She reasoned that beginning with period 9 the series had shown a relatively steady increase, whereas before that period it had exhibited an increase and a decline. An examination of her data revealed no seasonality, so Jill decided to try two smoothing methods: Simple exponential smoothing Holt's linear exponential smoothing, which can accommodate a trend in the data For simple exponential smoothing, Jill restricted the smoothing constant, a, to lie between 0 and 1 and found the best a to be very close to 1. The Minitab program selected the optimal values for the Holt smoothing constants, a and b. Error measures and the choices for the smoothing constants are shown in Table 11-4. Given the results shown in Table 11-4, Jill decided Holt's linear smoothing offered the most promise and used this procedure to generate forecasts for the next four quarters. Jill noted that the optimum smoothing constant using simple exponential smoothing was almost 1.00 (.99). Apparently, in order to track through the data in this fashion, the program was basically using each data value to predict the next. This is equivalent to using a simple naive method to forecast-that is, a model that says consecutive differences are random. img FIGURE 11-4 Plot of Quarterly Data Values: Total Merchandise Exports, First Quarter of 1984 to Second Quarter of 1990 ($ billions) img Jill realized that with each passing quarter a new actual value of total merchandise exports would be available and that the forecasts for future periods could be updated. img Using Holt's smoothing method, the forecasts for the next four quarters beyond the end of her data are img Jill then met with her boss to discuss her results. She indicated that she thought she had a good way to forecast the national variable, total merchandise exports, using exponential smoothing with trend adjustments. Her boss asked her to explain this method, which she did. Her next assignment was to use actual data to verify the hunch of Busby's president: that Busby's exports were well correlated with national exports. If she could establish this linkage, Busby would have a good forecasting method for its exports and could use the forecasts to plan future operations. The optimum smoothing constants used by Holt's linear exponential smoothing were ? = 1.77 and ? =.14. As new data come in over the next few quarters, Jill should probably rerun her data to see if these values change. How often do you think she should do this?
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BUSBY ASSOCIATES Jill Tilly was a recent graduate of a university business school when she took a job with Busby Associates, a large exporter of farm equipment. Busby's president noticed a forecasting course on Jill's resume during the hiring process and decided to start Jill's employment with a forecasting project that had been discussed many times by Busby's top managers. Busby's president believed there was a strong relationship between the company's export sales and the national figures for exports. The national figures were readily available from government sources, so Jill's project was to forecast a good, representative export variable. If this effort was successful, Busby's president believed the company would have a powerful tool for forecasting its own export sales. At the time, Jill located the most recent copy of the Survey of Current Business in a local library and recorded the quarterly figures for consumer goods exports in billions of dollars. She believed this variable was a good representative of total national exports. Anticipating the possibility of forecasting using regression analysis, she also recorded values for other variables she thought might possibly correlate well with this dependent variable. She ended up with values for four variables for 14 quarters. She then computed three additional variables from her dependent variable values: change in Y, percent change in Y, and Y lagged one period. By the time she began to think about various ways to forecast her variable, she had collected the data shown in Table 11-2. Jill keyed her data into a computer program that performed regression analysis and computed the correlation matrix for her seven variables. After examining this matrix, she chose three regressions with a single predictor variable and six regressions with two predictor variables. She then ran these regressions and chose the one she considered best: It used one predictor ( Y lagged one period) with the following results: img Jill examined the residual autocorrelations and found them to be small. She concluded she did not need to correct for any autocorrelation in her regression. Jill believed that she had found a good predictor variable ( Y lagged one period). img a Estimated. Variable key: 1: Consumer goods, exports, billions of dollars 2: Gross personal savings, billions of dollars 3: National income retail trade, billions of dollars 4: Fixed weight price indexes for national defense purchases, military equipment, 1982 = 100 5: Change in dependent variable from previous period 6: Percent change in dependent variable from previous period 7: Dependent variable lagged one period Source for variables 1 through 4: Survey of Current Business (U.S. Department of Commerce) 70 (7) (July 1990). Jill realized that her sample size was rather small: 13 quarters. She returned to the Survey of Current Business to collect more data points and was disappointed to find that during the years in which she was interested the definition of her dependent variable changed, resulting in an inconsistent time series. That is, the series took a jump upward halfway through the period that she was studying. Jill pointed out this problem to her boss, and it was agreed that total merchandise exports could be used as the dependent variable instead of consumer goods exports. Jill found that this variable remained consistent through several issues of the Survey of Current Business and that several years of data could be collected. She collected the data shown in Table 11-3, lagged the data one period, and again ran a regression analysis using Y lagged one period as the predictor variable. This time she again found good statistics in her regression printout, except for the lag 1 residual autocorrelation. That value,.47, was significantly different from zero at the 5% level. Jill was unsure what to do. She tried additional runs, including the period number and the change in Y as additional predictor variables. But after examining the residuals, she was unable to conclude that the autocorrelation had been removed. Jill decided to look at other forecasting techniques to forecast her new dependent variable: total merchandise exports. She used the time series data shown in the Y column of Table 11-3. The time series plot of total merchandise exports for the years 1984 through the second quarter of 1990 is shown in Figure 11-4. After studying Figure 11-4, Jill decided to use only the last 16 data points in her forecasting effort. She reasoned that beginning with period 9 the series had shown a relatively steady increase, whereas before that period it had exhibited an increase and a decline. An examination of her data revealed no seasonality, so Jill decided to try two smoothing methods: Simple exponential smoothing Holt's linear exponential smoothing, which can accommodate a trend in the data For simple exponential smoothing, Jill restricted the smoothing constant, a, to lie between 0 and 1 and found the best a to be very close to 1. The Minitab program selected the optimal values for the Holt smoothing constants, a and b. Error measures and the choices for the smoothing constants are shown in Table 11-4. Given the results shown in Table 11-4, Jill decided Holt's linear smoothing offered the most promise and used this procedure to generate forecasts for the next four quarters. Jill noted that the optimum smoothing constant using simple exponential smoothing was almost 1.00 (.99). Apparently, in order to track through the data in this fashion, the program was basically using each data value to predict the next. This is equivalent to using a simple naive method to forecast-that is, a model that says consecutive differences are random. img FIGURE 11-4 Plot of Quarterly Data Values: Total Merchandise Exports, First Quarter of 1984 to Second Quarter of 1990 ($ billions) img Jill realized that with each passing quarter a new actual value of total merchandise exports would be available and that the forecasts for future periods could be updated. img Using Holt's smoothing method, the forecasts for the next four quarters beyond the end of her data are img Jill then met with her boss to discuss her results. She indicated that she thought she had a good way to forecast the national variable, total merchandise exports, using exponential smoothing with trend adjustments. Her boss asked her to explain this method, which she did. Her next assignment was to use actual data to verify the hunch of Busby's president: that Busby's exports were well correlated with national exports. If she could establish this linkage, Busby would have a good forecasting method for its exports and could use the forecasts to plan future operations. It's possible that the choice of forecasting method could shift to another technique as new quarterly data are added to the database. Should Jill rerun her entire analysis once in a while to check this? If so, how often should she do this?
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BUSBY ASSOCIATES Jill Tilly was a recent graduate of a university business school when she took a job with Busby Associates, a large exporter of farm equipment. Busby's president noticed a forecasting course on Jill's resume during the hiring process and decided to start Jill's employment with a forecasting project that had been discussed many times by Busby's top managers. Busby's president believed there was a strong relationship between the company's export sales and the national figures for exports. The national figures were readily available from government sources, so Jill's project was to forecast a good, representative export variable. If this effort was successful, Busby's president believed the company would have a powerful tool for forecasting its own export sales. At the time, Jill located the most recent copy of the Survey of Current Business in a local library and recorded the quarterly figures for consumer goods exports in billions of dollars. She believed this variable was a good representative of total national exports. Anticipating the possibility of forecasting using regression analysis, she also recorded values for other variables she thought might possibly correlate well with this dependent variable. She ended up with values for four variables for 14 quarters. She then computed three additional variables from her dependent variable values: change in Y, percent change in Y, and Y lagged one period. By the time she began to think about various ways to forecast her variable, she had collected the data shown in Table 11-2. Jill keyed her data into a computer program that performed regression analysis and computed the correlation matrix for her seven variables. After examining this matrix, she chose three regressions with a single predictor variable and six regressions with two predictor variables. She then ran these regressions and chose the one she considered best: It used one predictor ( Y lagged one period) with the following results: img Jill examined the residual autocorrelations and found them to be small. She concluded she did not need to correct for any autocorrelation in her regression. Jill believed that she had found a good predictor variable ( Y lagged one period). img a Estimated. Variable key: 1: Consumer goods, exports, billions of dollars 2: Gross personal savings, billions of dollars 3: National income retail trade, billions of dollars 4: Fixed weight price indexes for national defense purchases, military equipment, 1982 = 100 5: Change in dependent variable from previous period 6: Percent change in dependent variable from previous period 7: Dependent variable lagged one period Source for variables 1 through 4: Survey of Current Business (U.S. Department of Commerce) 70 (7) (July 1990). Jill realized that her sample size was rather small: 13 quarters. She returned to the Survey of Current Business to collect more data points and was disappointed to find that during the years in which she was interested the definition of her dependent variable changed, resulting in an inconsistent time series. That is, the series took a jump upward halfway through the period that she was studying. Jill pointed out this problem to her boss, and it was agreed that total merchandise exports could be used as the dependent variable instead of consumer goods exports. Jill found that this variable remained consistent through several issues of the Survey of Current Business and that several years of data could be collected. She collected the data shown in Table 11-3, lagged the data one period, and again ran a regression analysis using Y lagged one period as the predictor variable. This time she again found good statistics in her regression printout, except for the lag 1 residual autocorrelation. That value,.47, was significantly different from zero at the 5% level. Jill was unsure what to do. She tried additional runs, including the period number and the change in Y as additional predictor variables. But after examining the residuals, she was unable to conclude that the autocorrelation had been removed. Jill decided to look at other forecasting techniques to forecast her new dependent variable: total merchandise exports. She used the time series data shown in the Y column of Table 11-3. The time series plot of total merchandise exports for the years 1984 through the second quarter of 1990 is shown in Figure 11-4. After studying Figure 11-4, Jill decided to use only the last 16 data points in her forecasting effort. She reasoned that beginning with period 9 the series had shown a relatively steady increase, whereas before that period it had exhibited an increase and a decline. An examination of her data revealed no seasonality, so Jill decided to try two smoothing methods: Simple exponential smoothing Holt's linear exponential smoothing, which can accommodate a trend in the data For simple exponential smoothing, Jill restricted the smoothing constant, a, to lie between 0 and 1 and found the best a to be very close to 1. The Minitab program selected the optimal values for the Holt smoothing constants, a and b. Error measures and the choices for the smoothing constants are shown in Table 11-4. Given the results shown in Table 11-4, Jill decided Holt's linear smoothing offered the most promise and used this procedure to generate forecasts for the next four quarters. Jill noted that the optimum smoothing constant using simple exponential smoothing was almost 1.00 (.99). Apparently, in order to track through the data in this fashion, the program was basically using each data value to predict the next. This is equivalent to using a simple naive method to forecast-that is, a model that says consecutive differences are random. img FIGURE 11-4 Plot of Quarterly Data Values: Total Merchandise Exports, First Quarter of 1984 to Second Quarter of 1990 ($ billions) img Jill realized that with each passing quarter a new actual value of total merchandise exports would be available and that the forecasts for future periods could be updated. img Using Holt's smoothing method, the forecasts for the next four quarters beyond the end of her data are img Jill then met with her boss to discuss her results. She indicated that she thought she had a good way to forecast the national variable, total merchandise exports, using exponential smoothing with trend adjustments. Her boss asked her to explain this method, which she did. Her next assignment was to use actual data to verify the hunch of Busby's president: that Busby's exports were well correlated with national exports. If she could establish this linkage, Busby would have a good forecasting method for its exports and could use the forecasts to plan future operations. A colleague of Jill's suggested she try the Box-Jenkins ARIMA methodology. What advice do you have for Jill if she decides to try Box-Jenkins? Can you identify a tentative ARIMA model?
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CONSUMER CREDIT COUNSELING The Consumer Credit Counseling (CCC) operation was described in Cases 1-2 and 3-3. The executive director, Marv Harnishfeger, concluded that the most important variable that CCC needed to forecast was the number of new clients that would be seen for the rest of 1993. Marv provided Dorothy Mercer with monthly data for the number of new clients seen by CCC for the period from January 1985 through March 1993 (see Case 3-3). Dorothy, with your help, has tried several ways to forecast the most important variable. These efforts are outlined in Cases 4-3, 5-3, 6-5, 8-5, and 9-3. Having completed these forecasting attempts, Dorothy decides it is time to summarize these efforts and to attempt to arrive at a method for forecasting the rest of the year. Assume that Dorothy assigns you to help her with this forecasting problem. Write a report that recommends a course of action. Keep in mind that Marv must develop forecasts for the number of clients seen that are as accurate as possible and that he can use in the everyday decision making for the organization. Be specific about what you are recommending to Dorothy and Marv. Remember to consider the issues discussed in this chapter, such as cost.
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TUX The owner of several Mr. Tux rental outlets, John Mosby, has tried several ways to forecast his most important variable, monthly dollar sales. His efforts are outlined in Cases 1-1, 2-2, 3-2, 4-2, 5-2, 6-4, 8-4, and 9-2. Having completed these forecasting attempts, John decides it is time to summarize his efforts and attempt to arrive at a method for forecasting the future. He realizes that he should update both his data and his method of forecasting at some time in the future, but he needs to choose a way of forecasting the next few months right away. To begin, John summarizes the results of the methods he has tried so far: • Case 2-2: Using the annual average to forecast future annual sales might work, but since John noticed an upward trend, he needs a sound way of extending these averages into the future. Also, John is quite concerned with the seasonal effect, since he knows his sales vary considerably by month. His efforts using annual averages are not fruitful. • Case 3-2: John's use of the Minitab program established that both a trend and a seasonal effect existed in his data. Although he knew these elements were there before he started, he was pleased to see that the results from his computer program established them statistically. The program also indicated that several autocorrelation coefficients were outside sampling error limits, indicating to John that both trend and seasonal effects needed to be reflected in his final forecasting model. • Case 4-2: When John used exponential smoothing, including methods that took account of trend and seasonal factors, the resulting error measurements were unsatisfactory. He realized that these measurements, such as the average error and average percentage error, resulted from predicting past values of his variable. But since they were so high, he didn't want to use these techniques to predict the unknown future. • Case 5-2: John finally got some encouraging results using the decomposition method to construct a trend line, seasonal indexes, and a cyclical component for his data. He was able to show his banker the seasonal indexes and make desired arrangements on his loan payments. He also generated forecasts for the next few months by reassembling his estimated components. However, John was somewhat disturbed by the wide range of his projections. • Case 6-4: Simple regression analysis was the next technique John tried, using the time period as the independent or predictor variable. He reasoned that this variable would account for the trend factor he knew was in his data. This method did not account for the seasonality in sales, and the r 2 value of 56% was unsatisfactory. • Case 8-4: John next tried a multiple regression using both a time period number to reflect trend and a series of dummy variables to account for the seasonal effect (months). His R 2 value of 88% was a considerable improvement over his simple regression, but the forecast error for the last 12 months of his data, as measured by the mean absolute percentage error ( MAPE ), of 21% was unacceptable. With this kind of error, John decided not to use multiple regression. He also tried a seasonal autoregressive model, which resulted in an R 2 value of 91%. John was quite pleased with this result. • Case 9-2: The Box-Jenkins ARIMA methodology bothered John from the outset because he did not totally understand it. He recognized, however, that his seasonal autoregressive model was a particular ARIMA model. He wanted to know if he could improve on this model. He knew he would have to explain whatever forecasts he came up with to investors and bankers in his attempt to gain capital for expansion, so he wanted a forecasting method that was both accurate and understandable. In thinking over these efforts, John realized that time was running out and that he must generate forecasts of his monthly revenues soon. He had limited time to try modifications to the methods he used and could think about combining two or more methods. But he did not have time to acquire new software and try completely different methods. As he wondered what he should do, he looked at one of his favorite sayings posted on his office wall: "Let's do something, even if it's not quite right." Assume you have been hired to help John Mosby with his forecasting problem. Write him a memo that summarizes his efforts to date and that recommends a course of action to him. Keep in mind that John must quickly develop forecasts for monthly sales that are as accurate as possible and that he can use in discussions with investors. Be very specific about what you are recommending to the owner of Mr. Tux.
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ALOMEGA FOOD STORES Example 1.1 described how Julie Ruth, the president of Alomega Food Stores, collected monthly sales data for her company along with several other variables she thought might be related to sales. Cases 2-3, 3-4, 5-6, 8-7, and 10-2 described Julie's efforts to use various Minitab procedures to make meaningful forecasts of monthly sales. Julie knew that her technical staff were using the same data to generate good forecasts but didn't know how they were coming along. Besides, she really wanted to come up with a good forecasting method on her own. She knew that as the first woman president of Alomega, she had jumped over several potential candidates for the job and that there might be some resentment among her management team. She was especially sensitive to the negative comments made by Jackson Tilson, her production manager, during a recent meeting (see the end of Example 1.1). In reviewing her efforts, Julie decided to discard the simple regression analysis she performed as summarized in Case 2-3. She was left with a choice among the decomposition analysis described in Case 5-6, the multiple regression described in Case 8-7, and a combination of methods described in Case 10-2. Suppose you were recently hired by Alomega Food Stores and assigned to assist Julie in developing an effective forecasting method for monthly sales. After reviewing Julie's efforts to date, which of the methods she tried would you recommend to her?
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ALOMEGA FOOD STORES Example 1.1 described how Julie Ruth, the president of Alomega Food Stores, collected monthly sales data for her company along with several other variables she thought might be related to sales. Cases 2-3, 3-4, 5-6, 8-7, and 10-2 described Julie's efforts to use various Minitab procedures to make meaningful forecasts of monthly sales. Julie knew that her technical staff were using the same data to generate good forecasts but didn't know how they were coming along. Besides, she really wanted to come up with a good forecasting method on her own. She knew that as the first woman president of Alomega, she had jumped over several potential candidates for the job and that there might be some resentment among her management team. She was especially sensitive to the negative comments made by Jackson Tilson, her production manager, during a recent meeting (see the end of Example 1.1). In reviewing her efforts, Julie decided to discard the simple regression analysis she performed as summarized in Case 2-3. She was left with a choice among the decomposition analysis described in Case 5-6, the multiple regression described in Case 8-7, and a combination of methods described in Case 10-2. Based on your choice in Question 1, write a detailed memo to Julie outlining your reasons for your choice, and indicate the extent to which you think this forecasting method would be effective.
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ALOMEGA FOOD STORES Example 1.1 described how Julie Ruth, the president of Alomega Food Stores, collected monthly sales data for her company along with several other variables she thought might be related to sales. Cases 2-3, 3-4, 5-6, 8-7, and 10-2 described Julie's efforts to use various Minitab procedures to make meaningful forecasts of monthly sales. Julie knew that her technical staff were using the same data to generate good forecasts but didn't know how they were coming along. Besides, she really wanted to come up with a good forecasting method on her own. She knew that as the first woman president of Alomega, she had jumped over several potential candidates for the job and that there might be some resentment among her management team. She was especially sensitive to the negative comments made by Jackson Tilson, her production manager, during a recent meeting (see the end of Example 1.1). In reviewing her efforts, Julie decided to discard the simple regression analysis she performed as summarized in Case 2-3. She was left with a choice among the decomposition analysis described in Case 5-6, the multiple regression described in Case 8-7, and a combination of methods described in Case 10-2. In addition to the methods Julie was considering, what other forecasting procedures would you suggest? Use Minitab or another forecasting software package to try other methods and compare them with your choice in Question 1.
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ALOMEGA FOOD STORES Example 1.1 described how Julie Ruth, the president of Alomega Food Stores, collected monthly sales data for her company along with several other variables she thought might be related to sales. Cases 2-3, 3-4, 5-6, 8-7, and 10-2 described Julie's efforts to use various Minitab procedures to make meaningful forecasts of monthly sales. Julie knew that her technical staff were using the same data to generate good forecasts but didn't know how they were coming along. Besides, she really wanted to come up with a good forecasting method on her own. She knew that as the first woman president of Alomega, she had jumped over several potential candidates for the job and that there might be some resentment among her management team. She was especially sensitive to the negative comments made by Jackson Tilson, her production manager, during a recent meeting (see the end of Example 1.1). In reviewing her efforts, Julie decided to discard the simple regression analysis she performed as summarized in Case 2-3. She was left with a choice among the decomposition analysis described in Case 5-6, the multiple regression described in Case 8-7, and a combination of methods described in Case 10-2. In addition to the methods Julie was considering, what other forecasting procedures would you suggest? Use Minitab or another forecasting software package to try other methods and compare them with your choice in Question 1. Should Julie combine forecasts?
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SOUTHWEST MEDICAL CENTER Mary Beasley is not very happy. She has tried several time series methods to develop forecasts for the monthly totals of billable visits to Medical Oncology (see Cases 4-7, 5-8, 8-9, and 9-10). She has had the greatest success with an ARIMA model, although the residual autocorrelations as judged by the Ljung-Box Q statistic (see Equation 9.5) seem to be collectively large. Examining a plot of her series (see Figure 4-23), Mary notices that the behavior of total visits for fiscal years 1997-1998, 1998-1999, and 1999-2000-three years in the middle of her data-is different from that for the remaining years. Monthly total visits are generally larger and more volatile for these middle fiscal years. It's as if this piece of the time series has been "lifted up" from the rest of the series. Mary is not sure why that should be the case and wonders if maybe there was a change in procedure, or some other intervention, that was responsible for changing the character of the time series during these middle years. You have been hired to help Mary with her forecasting problem. Write her a memo that summarizes her efforts to date and that recommends a course of action to her. Be sure your memo addresses the following issues: Should Mary accept the forecasts from one of her current procedures? If so, how should she "sell" the forecasts to management? Given the somewhat inconsistent character of her time series, should Mary search for another forecasting technique? Should she use one of her current methods but with a modified data set?
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