# Service Management

## Quiz 14 :Forecasting Demand for Services

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The demand for a certain drug in a hospital has been increasing. For the past six months, the following demand has been observed: Use a three-month moving average to make a forecast for July.
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Moving Averages Method uses the average of the most recent data values in the time series as the forecast for the next period.
Consider the demand data given:
The three-month moving average forecast for July is obtained by averaging the demand from April to June.
Thus, the three-month moving average forecast for July is
.

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What changes in ?, ?, and ? would you recommend to improve the performance of the trendline seasonal adjustment forecast shown in Figure 11.4?
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Trendline seasonal adjustment is an extension to the exponential smoothing forecast method that includes both trend and seasonal adjustments to forecast the data.
Alpha, beta, and gamma are smoothing constants used to make accurate forecasts by adjusting both trend and seasonality. All the three smoothing constant values are usually assigned between 0.1 and 0.5. The higher the alpha, beta, and gamma values, lesser the improvement will be to get more better results. Hence, the smoothing constants should be kept as low as possible. In the given case, by reducing the values alpha, beta, and gamma to 0.1 a better performance can be seen.
Hence, by reducing the smoothing constants alpha, beta, and gamma to 0.1 would improve the performance of the trendline seasonal adjustment forecast.

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Suggest a number of independent variables for a regression model to predict the potential sales volume of a given location for a retail store (e.g., a video rental store).
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•Number of VCR's per household
•Level of education
•Number of children per household
•Residential population density

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In September 2013, there were 1,035 checking-account customers at a neighbourhood bank. The forecast for September, which was made in August, was for 1,065 checking-account customers. Use an ? of 0.1 to update the forecast for October.
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Gnomial Functions, Inc. (GFI), is a medium-sized consulting firm in San Francisco that specializes in developing various forecasts of product demand, sales, consumption, or other information for its clients. To a lesser degree, it also has developed ongoing models for internal use by its clients. When contacted by a potential client, GFI usually establishes a basic work agreement with the firm's top management that sets out the general goals of the end product, primary contact personnel in both firms, and an outline of the project's overall scope (including any necessary time constraints for intermediate and final completion and a rough price estimate for the contract). Following this step, a team of GFI personnel is assembled to determine the most appropriate forecasting technique and develop a more detailed work program to be used as the basis for final contract negotiations. This team, which can vary in size according to the scope of the project and the client's needs, will perform the tasks that are established by the work program in conjunction with any personnel from the client firm who would be included in the team. Recently, GFI was contacted by a rapidly growing regional firm that manufactures, sells, and installs active solar waterheating equipment for commercial and residential applications. DynaSol Industries has seen its sales increase by more than 200 percent during the past 18 months, and it wishes to obtain a reliable estimate of its sales during the next 18 months. The company management expects that sales should increase substantially because of competing energy costs, tax-credit availability, and fundamental shifts in the attitudes of the regional population toward so-called exotic solar systems. The company also faces increasing competition within this burgeoning market. This situation requires major strategic decisions concerning the company's future. When GFI was contacted, DynaSol almost had reached the manufacturing capacity of its present facility, and if it wishes to continue growing with the market, it must expand either by relocating to a new facility entirely or by developing a second manufacturing location. Each involves certain known costs, and each has its advantages and disadvantages. The major unknown factors as far as management is concerned are growth of the overall market for this type of product and how large a market share the company would be able to capture. Table 14.12 contains the preliminary information available to GFI on DynaSol's past sales. Given the information available and your knowledge of different forecasting techniques, develop a recommendation for utilizing a specific forecasting technique in the subsequent study. The final contract negotiations are pending, and so it is essential that you account for the advantages and disadvantages of your preferred technique as they would apply to the problem at hand and point out any additional information you would like to have.
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Gnomial Functions, Inc. (GFI), is a medium-sized consulting firm in San Francisco that specializes in developing various forecasts of product demand, sales, consumption, or other information for its clients. To a lesser degree, it also has developed ongoing models for internal use by its clients. When contacted by a potential client, GFI usually establishes a basic work agreement with the firm's top management that sets out the general goals of the end product, primary contact personnel in both firms, and an outline of the project's overall scope (including any necessary time constraints for intermediate and final completion and a rough price estimate for the contract). Following this step, a team of GFI personnel is assembled to determine the most appropriate forecasting technique and develop a more detailed work program to be used as the basis for final contract negotiations. This team, which can vary in size according to the scope of the project and the client's needs, will perform the tasks that are established by the work program in conjunction with any personnel from the client firm who would be included in the team. Recently, GFI was contacted by a rapidly growing regional firm that manufactures, sells, and installs active solar waterheating equipment for commercial and residential applications. DynaSol Industries has seen its sales increase by more than 200 percent during the past 18 months, and it wishes to obtain a reliable estimate of its sales during the next 18 months. The company management expects that sales should increase substantially because of competing energy costs, tax-credit availability, and fundamental shifts in the attitudes of the regional population toward so-called exotic solar systems. The company also faces increasing competition within this burgeoning market. This situation requires major strategic decisions concerning the company's future. When GFI was contacted, DynaSol almost had reached the manufacturing capacity of its present facility, and if it wishes to continue growing with the market, it must expand either by relocating to a new facility entirely or by developing a second manufacturing location. Each involves certain known costs, and each has its advantages and disadvantages. The major unknown factors as far as management is concerned are growth of the overall market for this type of product and how large a market share the company would be able to capture. Table 14.12 contains the preliminary information available to GFI on DynaSol's past sales. Assume that you are a member of DynaSol's small marketing department and that the contract negotiations with GFI have fallen through irrevocably. The company's top management has decided to use your expertise to develop a forecast for the next six months (and, perhaps, for the sixmonth period following that one as well), because it must have some information on which to base a decision about expanding its operations. Develop such a forecast, and for the benefit of top management, note any reservations or qualifications you feel are vital to its understanding and use of the information.
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During the noon hour this past Wednesday at a fast-food restaurant, 72 hamburgers were sold. The smoothed value calculated the week before was 67. Update the forecast for next Wednesday using simple exponential smoothing and an ? of 0.1.
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Prepare a spreadsheet model for the ferry passenger data in Table 17.5 , and recalculate the forecasts using an ? of 0.3 and a of 0.2. Has this change in the smoothing constants improved the MAD?
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Oak Hollow Medical Evaluation Center is a nonprofit agency noffering multidisciplinary diagnostic services to study children with disabilities or developmental delays. The center can test each patient for physical, psychological, or social problems. Fees for services are based on an ability-to-pay schedule. The evaluation center exists in a highly competitive environment. Many public-spirited organizations are competing for shrinking funds, and many groups such as private physicians, private and school psychologists, and social service organizations also are "competing" for the same patients. As a result of this situation, the center finds itself in an increasingly vulnerable financial position. Mr. Abel, the director of the center, is becoming increasingly concerned with the center's ability to attract adequate funding and serve community needs. Mr. Abel now must develop an accurate estimate of the future patient load, staffing requirements, and operating expenses as part of his effort to attract funding. To this end, the director has approached an operations management professor at the local university for assistance in preparing a patient, staffing, and budget forecast for the coming year. The professor has asked you to aid her in this project. Tables 14.8 through 14.11 give you some pertinent information. Given the information available and your knowledge of different forecasting techniques, recommend a specific forecasting technique for this study. Consider the advantages and disadvantages of your preferred technique, and identify what additional information, if any, Mr. Abel would need.
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For the data in exercise 17.4, use an ? of 0.1 to make a forecast for July.
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Prepare a spreadsheet model for the ferry passenger data in Table 17.6 , and recalculate the forecasts using an ? of 0.3, a ? of 0.1, and a of 0.2. Has this change in the smoothing constants improved the MAD?
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For the data in exercise 17.2, update the fast-food restaurant forecast if a trend value of 1.4 was calculated for the previous week. Use a ? of 0.3 to update the trend for this week, and determine the forecast for next Wednesday using exponential smoothing with trend adjustment.
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For the data in exercise 17.4, use an ? of 0.1 and a ? of 0.2 to make a forecast for July and August. Calculate the MAD for your January through June forecasts.
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Oak Hollow Medical Evaluation Center is a nonprofit agency noffering multidisciplinary diagnostic services to study children with disabilities or developmental delays. The center can test each patient for physical, psychological, or social problems. Fees for services are based on an ability-to-pay schedule. The evaluation center exists in a highly competitive environment. Many public-spirited organizations are competing for shrinking funds, and many groups such as private physicians, private and school psychologists, and social service organizations also are "competing" for the same patients. As a result of this situation, the center finds itself in an increasingly vulnerable financial position. Mr. Abel, the director of the center, is becoming increasingly concerned with the center's ability to attract adequate funding and serve community needs. Mr. Abel now must develop an accurate estimate of the future patient load, staffing requirements, and operating expenses as part of his effort to attract funding. To this end, the director has approached an operations management professor at the local university for assistance in preparing a patient, staffing, and budget forecast for the coming year. The professor has asked you to aid her in this project. Tables 14.8 through 14.11 give you some pertinent information. Develop forecasts for patient, staffing, and budget levels for next year.
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What characteristics of service organizations make forecast accuracy important?
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The number of customers at a bank is likely to vary by the hour of the day and by the dayof the month. What are the implications of this for choosing a forecasting model ?
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Prepare a spreadsheet model for the Saturday hotel occupancy data in Table 17.3, and recalculate the forecasts using an ? of 0.3. What is the new MAD?
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Why is the N-period moving-average model still in common use if the simple exponential smoothing model has superior qualities?
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