Answer the question in the picture MGMT2026 Production and Operations ManagementThe number of students enrolled in Spring Valley Elementary school has beensteadily increasing over the past five years. The School Board would like to forecastenrolment for years 6 and 7 in order to better plan capacity. The past five years"111enrolment is indicated in table 3:Table 3YearEnrolment12202453.2564289310Assuming a linear trend, use the tabular method to derive values for:a the slopeb. the interceptc. Forecast period 6 enrolment.ed Kingdom

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Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter9: Decision Making Under Uncertainty

Section: Chapter Questions

Problem 46P

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The Baker Company wants to develop a budget to predict how overhead costs vary with activity levels. Management is trying to decide whether direct labor hours (DLH) or units produced is the better measure of activity for the firm. Monthly data for the preceding 24 months appear in the file P13_40.xlsx. Use regression analysis to determine which measure, DLH or Units (or both), should be used for the budget. How would the regression equation be used to obtain the budget for the firms overhead costs?
The file P13_22.xlsx contains total monthly U.S. retail sales data. While holding out the final six months of observations for validation purposes, use the method of moving averages with a carefully chosen span to forecast U.S. retail sales in the next year. Comment on the performance of your model. What makes this time series more challenging to forecast?
The file P13_42.xlsx contains monthly data on consumer revolving credit (in millions of dollars) through credit unions. a. Use these data to forecast consumer revolving credit through credit unions for the next 12 months. Do it in two ways. First, fit an exponential trend to the series. Second, use Holts method with optimized smoothing constants. b. Which of these two methods appears to provide the best forecasts? Answer by comparing their MAPE values.
The monthly demand for units manufactured by the Acme Rocket Company has been as follows:Month Units Month UnitsMay 100 September 105June 80 October 110July 110 November 125August 115 December 120a. Use the exponential smoothing method to forecast the number of units for June to January. The initial forecast for May was 105 units; α = 0.2.b. Calculate the absolute percentage error for each month from June through December and the MAD and MAPE of forecast error as of the end of December.c. Calculate the tracking signal as of the end of December. What can you say about the performance of your forecasting method?
3) Seasonality: The following data represent dinner sales at a busy restaurant. Use linear regression to predict sales for each day of week 5 and the total sales for week 5. Estimate the percentage of weekly sales that occur over the weekend (include Friday, Saturday, and Sunday). Finally, determine which days of the week are increasing and decreasing in sales, using the slopes of the LR lines. Week Mon Wed Fri Sat Sun Tue 177 170 Thu 190 Total 270 152 180 321 386 166 218 203 402 427 167 333 357 229 3 158 170 170 205 163 173 158 225 349 433 212 a) Graph the seasonal data and attach the graph to this page. b) Determine the slope for each day of the week. Mon Tue Wed Thu Fri Sat Sun Total Slope c) Estimate the percentage of weekend sales in week 5: d) For which day are sales increasing the fastest? e) For which day are sales decreasing the fastest?
b. Kim’s department at a local department store has tracked the sales of a product over thelast nine weeks. The demand can be seen in the table below.Period Demand1 242 233 264 365 266 307 328 269 25i. Forecast demand using exponential smoothing with an alpha of 0.3, and an initialforecast of 30.0 for period 2 to 9.ii. Use a 5-period moving average, forecast the demand up to period 9.iii. Using MAD, determine which forecasting technique is better.
George Kyparisis owns a company that manufac-tures sailboats. Actual demand for George’s sailboats duringeach of the past four seasons was as follows: George has forecasted that annual demand for his sailboats inyear 5 will equal 5,600 sailboats. Based on this data and themultiplicative seasonal model, what will the demand level befor George’s sailboats in the spring of year 5?
The actual demand for the patients at Omaha Emergency Medical Clinic for the first 6 weeks of this year fol- 4.7 lows: ACTUAL NO. OF WEEK PATIENTS 1 65 62 70 4 48 63 52 Clinic administrator Marc Schniederjans wants you to forecast patient demand at the clinic for week 7 by using this data. You decide to use a weighted moving average method to find this fore- cast. Your method uses four actual demand levels, with weights of 0.333 on the present period, 0.25 one period ago, 0.25 two peri- ods ago, and 0.167 three periods ago. a) What is the value of your forecast? X b) If instead the weights were 20, 15, 15, and 10, respectively, how would the forecast change? Explain why. c) What if the weights were 0.40, 0.30, 0.20, and 0.10, respec- tively? Now what is the forecast for week 7?
12-1. The Hartley-Davis motorcycle dealer in the Minneapolis- St. Paul area wants to be able to forecast accurately the de- mand for the Roadhog Super motorcycle during the next month. From sales records, the dealer has accumulated the data in the following table for the past year. Month January February March April Мay June Motorcycle Sales 7 10 8 7 12 July August September 10 11 12 October 10 November December 14 16 a Compute a three-month moving average forecast of demand for April through January (of the next year). b. Compore a five-month moving average forecast for June drough January. c. Compare the two forecasts computed in parts (a) and (b) using MAD. Which one should the dealer use for January of dhe next year?
The following table shows the actual demand observed over the last 11 years: Year Demand 1 7 2 9 3 5 4 4 9 5 5 13 6 6 8 7 12 8 8 13 This exercise contains only parts b, c, and d. b) Using the 3-year moving average, provide the forecast from periods 4 through 12 (round your responses to one decimal place). 7 9 10 Year Forecast 9 9 10 11 11 11 7 12
Month Demand Forecast Error Abs Error alpha 1 20 2 18 3 21 4 25 5 24 6 27 7 22 8 30 9 23 10 20 11 29 12 22 Mean Bias MAD (mean error) Problem 6: Maverick Jeans' demand manager decided to evaluate exponential smoothing. To maintain com- parability, she used the data from problem 6, copied below. 1 Month 2 3 Demand 20 18 21 25 4 5 24 6 7 8 9 10 11 12 27 22 30 23 20 29 22 Develop forecasts for months 5-12. Calculate the Bias and MAD. Use a starting forecast of 20 for month 4 and an alpha value of 0.2. (Note: Adjust all cell values to two decimal points.)
Demand for oil changes at Garcia’s Garage has been as follows:Month Number of Oil ChangesJanuary 41February 46March 57April 52May 59June 51July 60August 62a. Use simple linear regression analysis to develop a forecasting model for monthly demand. In this application, the dependent variable, Y, is monthly demand and the independent variable, X, is the month. For January, letX = 1; for February, let X = 2; and so on.b. Use the model to forecast demand for September, October,and November. Here, X = 9, 10, and 11,respectively.

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