Concept explainers
Case summary/Introduction: TSC is a small business dedicated to sell Photostat copy of original to university students. It also offers a range of service like passport photos, self-service copy machines, packaging and shipping. A university instructor shares his original documents before the semester starts. A manager at TSC is
Characters in the case: TSC company.
Adequate Information: A university instructor shares his original documents before the semester starts. A manager at TSC is forecasting demand of documents. He usually estimated two-third of student’s enrollment.
Interpretation: Number of copies to be produced when course pack is 35 and when estimated enrolment is 275.
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Practical Operations Management
- 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?arrow_forwardThe 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?arrow_forwardThe owner of a restaurant in Bloomington, Indiana, has recorded sales data for the past 19 years. He has also recorded data on potentially relevant variables. The data are listed in the file P13_17.xlsx. a. Estimate a simple regression equation involving annual sales (the dependent variable) and the size of the population residing within 10 miles of the restaurant (the explanatory variable). Interpret R-square for this regression. b. Add another explanatory variableannual advertising expendituresto the regression equation in part a. Estimate and interpret this expanded equation. How does the R-square value for this multiple regression equation compare to that of the simple regression equation estimated in part a? Explain any difference between the two R-square values. How can you use the adjusted R-squares for a comparison of the two equations? c. Add one more explanatory variable to the multiple regression equation estimated in part b. In particular, estimate and interpret the coefficients of a multiple regression equation that includes the previous years advertising expenditure. How does the inclusion of this third explanatory variable affect the R-square, compared to the corresponding values for the equation of part b? Explain any changes in this value. What does the adjusted R-square for the new equation tell you?arrow_forward
- Suppose that a regional express delivery service company wants to estimate the cost of shipping a package (Y) as a function of cargo type, where cargo type includes the following possibilities: fragile, semifragile, and durable. Costs for 15 randomly chosen packages of approximately the same weight and same distance shipped, but of different cargo types, are provided in the file P13_16.xlsx. a. Estimate a regression equation using the given sample data, and interpret the estimated regression coefficients. b. According to the estimated regression equation, which cargo type is the most costly to ship? Which cargo type is the least costly to ship? c. How well does the estimated equation fit the given sample data? How might the fit be improved? d. Given the estimated regression equation, predict the cost of shipping a package with semifragile cargo.arrow_forwardThe file P13_02.xlsx contains five years of monthly data on sales (number of units sold) for a particular company. The company suspects that except for random noise, its sales are growing by a constant percentage each month and will continue to do so for at least the near future. a. Explain briefly whether the plot of the series visually supports the companys suspicion. b. By what percentage are sales increasing each month? c. What is the MAPE for the forecast model in part b? In words, what does it measure? Considering its magnitude, does the model seem to be doing a good job? d. In words, how does the model make forecasts for future months? Specifically, given the forecast value for the last month in the data set, what simple arithmetic could you use to obtain forecasts for the next few months?arrow_forwardThe manager of Carpet City outlet needs to make an accurate forecast of demand for Soft Shag Carpet (its biggest seller). If the manager does not order enough carpet from the carpet mill, customers will buy their carpet from one of Carpet City’s many competitors. The manager has collected the following demand data for the past 8 months: Month Demand for Soft Shag Carpet (1000 yd) 1 8 2 12 3 7 4 9 5 15 6 11 7 10 8 12 Required showing all workings: Compute a 3-month moving average forecast for months 4 through 9. Compute a weighted 3-month moving average forecast for months 4 through 9. Assign weights of 0.55, 0.33 and 0.12 to the months in sequence, starting with the most recent month.arrow_forward
- The demand for Krispee Crunchies, a favorite breakfast cereal of people born in the 1940s, is experiencing a decline. The company wants to monitor demand for this product closely as it nears the end of its life cycle. The following table shows the actual sales history for January–October. Generate forecasts for November–December, using the trend projection with regression method. Looking at the accuracy of its forecasts over the history file, as well as the other statistics provided, how confident are you in these forecasts for November–December? Month Sales Month Sales January Februray March April May June 890,000 800,000 825,000 840,000 730,000 780,000 July August September October November December 710,000 730,000 680,000 670,000arrow_forwardThe following table shows the past two years of quarterly sales information. Assume that there are both trend and seasonal factors and that the seasonal cycle is one year. Use time series decomposition to forecast quarterly sales for the next year. (Do not round intermediate calculations. Round your answers to the nearest whole number.) Note:- Do not provide handwritten solution. Maintain accuracy and quality in your answer. Take care of plagiarism. Answer completely. You will get up vote for sure.arrow_forwardThe actual demand of a product for six months are summarized in the table below: Month (t) Demand Dt 1 80 2 90 3 70 4 100 5 70 6 90 Find three months weighted moving averages by assuming the weights, W1 =1, W2 = 0.4 and W3 = 0.5 ii. Compute the mean forecast error iii. Compute the mean square error (MSE) iv. Compute the mean absolute deviation (MAD). v. Mean absolute percent error (MAPE)arrow_forward
- The managing director of a consulting group has the accompanying monthly data on total overhead costs and professional labor hours to bill to clients. Complete parts a through c Click the icon to view the monthly data. a. Develop a simple linear regression model between billable hours and overhead costs. Overhead Costs 105.790.5+ (47.3714) x Billable Hours (Round the constant to one decimal place as needed. Round the coefficient to four decimal places as needed. Do not include the $ symbol in your answers) b. Interpret the coefficients of your regression model. Specifically, what does the fixed component of the model mean to the consulting firm? Interpret the fixed term, bo, if appropriate. Choose the correct answer below. OA. The value of bo is the predicted overhead costs for 0 billable hours OB. For each increase of 1 unit in billable hours, the predicted overhead costs are estimated to increase by bo C. It is not appropriate to interpret bo. because its value is the predicted…arrow_forwardThe following table shows the three-period moving average and five-period moving average for monthly sales of Budget Furniture's during 2019. Moving averages of Budget Furniture's Time period Months Sales Three-period moving average (rounded off to Five-period moving average four decimals) R'millions 1 Jan 7 5.0000 6.2 February 5.6667 6.6 March 5 7.0000 B 4 April 8.3333 8.2 May 7 9.3333 8.4 June 8.3333 9.6 7 July 12 8.6667 9.6 August 4 A 9.2 September 10 10.6667 10 October 13 10.6667 11 November 9 12 December 10 The seasonal index for the month of February in 2019 is: LOarrow_forwardThe demand for Krispee Crunchies, a favorite breakfast cereal of people born in the 1940s, is experiencing a decline. The company wants to monitor demand for this product closely as it nears the end of its life cycle. The following table shows the actual sales history for January–October. Generateforecasts for November–December, using the trend projection with regression method. Looking at the accuracy of its forecasts over the history file, as well as the other statistics provided, how confident are you in these forecasts for November–December?arrow_forward
- Practical Management ScienceOperations ManagementISBN:9781337406659Author:WINSTON, Wayne L.Publisher:Cengage,