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ASSIGNMENT MATERIAL 417 needed each week for the club to break even, but first he must estimate the club’s fixed and variable costs. He has collected the following data over the club’s first season of operation: Week Number of Orders per Week Weekly Total Costs 1 415 $26,900 2 435 27,200 3 285 24,700 4 325 25,200 5 450 27,995 6 360 25,900 7 420 27,000 8 460 28,315 9 380 26,425 10 350 25,750 1. Plot the relationship between number of orders per week and weekly total costs. 2. Estimate the cost equation using the high-low method, and draw this line on your graph. 3. Tom uses his computer to calculate the following regression formula: Weekly total costs = $18,791 + ($19.97 * Number of orders per week) Draw the regression line on your graph. Use your graph to evaluate the regression line using the cri- teria of economic plausibility, goodness of fit, and significance of the independent variable. Is the cost function estimated using the high-low method a close approximation of the cost function estimated using the regression method? Explain briefly. 4. Did Market Thyme break even this season? Remember that each of the families paid a seasonal mem- bership fee of $100. 5. Assume that 500 families join the club next year and that prices and costs do not change. How many orders, on average, must Market Thyme receive each of 10 weeks next season to break even? 10-37 High-low method; regression analysis. (CIMA, adapted) Catherine McCarthy, sales manager of Baxter Arenas, is checking to see if there is any relationship between promotional costs and ticket rev- enues at the sports stadium. She obtains the following data for the past 9 months: Month Ticket Revenues Promotional Costs April $200,000 $52,000 May 270,000 65,000 June 320,000 80,000 July 480,000 90,000 August 430,000 100,000 September 450,000 110,000 October 540,000 120,000 November 670,000 180,000 December 751,000 197,000 She estimates the following regression equation: Ticket revenues = $65,583 + ($3.54 * Promotional costs) 1. Plot the relationship between promotional costs and ticket revenues. Also draw the regression line and evaluate it using the criteria of economic plausibility, goodness of fit, and slope of the regression line. 2. Use the high-low method to compute the function relating promotional costs and revenues. 3. Using (a) the regression equation and (b) the high-low equation, what is the increase in revenues for each $10,000 spent on promotional costs within the relevant range? Which method should Catherine use to predict the effect of promotional costs on ticket revenues? Explain briefly. 10-38 Regression, activity-based costing, choosing cost drivers. Sleep Late, a large hotel chain, has been using activity-based costing to determine the cost of a night’s stay at their hotels. One of the activi- ties, “Inspection,” occurs after a customer has checked out of a hotel room. Sleep Late inspects every 10th room and has been using “number of rooms inspected” as the cost driver for inspection costs. A significant component of inspection costs is the cost of the supplies used in each inspection. Required Required

418 CHAPTER 10 DETERMINING HOW COSTS BEHAVE Mary Adams, the chief inspector, is wondering whether inspection labor-hours might be a better cost driver for inspection costs. Mary gathers information for weekly inspection costs, rooms inspected, and inspection labor-hours as follows: Week Rooms Inspected Inspection Labor-Hours Inspection Costs 1 254 66 $1,740 2 322 110 2,500 3 335 82 2,250 4 431 123 2,800 5 198 48 1,400 6 239 62 1,690 7 252 108 1,720 8 325 127 2,200 Mary runs regressions on each of the possible cost drivers and estimates these cost functions: Inspection Costs = $193.19 + ($6.26 * Number of rooms inspected) Inspection Costs = $944.66 + ($12.04 * Inspection labor @ hours) 1. Explain why rooms inspected and inspection labor-hours are plausible cost drivers of inspection costs. 2. Plot the data and regression line for rooms inspected and inspection costs. Plot the data and regres- sion line for inspection labor-hours and inspection costs. Which cost driver of inspection costs would you choose? Explain. 3. Mary expects inspectors to inspect 300 rooms and work for 105 hours next week. Using the cost driver you chose in requirement 2, what amount of inspection costs should Mary budget? Explain any implica- tions of Mary choosing the cost driver you did not choose in requirement 2 to budget inspection costs. 10-39 Interpreting regression results. Spirit Freightways is a leader in transporting agricultural products in the western provinces of Canada. Reese Brown, a financial analyst at Spirit Freightways, is studying the behav- ior of transportation costs for budgeting purposes. Transportation costs at Spirit are of two types: (a) operating costs (such as labor and fuel) and (b) maintenance costs (primarily overhaul of vehicles). Brown gathers monthly data on each type of cost, as well as the total freight miles traveled by Spirit vehicles in each month. The data collected are shown below (all in thousands): Month Operating Costs Maintenance Costs Freight Miles January $ 942 $ 974 1,710 February 1,008 776 2,655 March 1,218 686 2,705 April 1,380 694 4,220 May 1,484 588 4,660 June 1,548 422 4,455 July 1,568 352 4,435 August 1,972 420 4,990 September 1,190 564 2,990 October 1,302 788 2,610 November 962 762 2,240 December 772 1,028 1,490 1. Conduct a regression using the monthly data of operating costs on freight miles. You should obtain the following result: Regression : Operating costs = a + ( b * Number of freight miles) Variable Coefficient Standard Error t -Value Constant $445.76 $112.97 3.95 Independent variable: No. of freight miles $ 0.26 $ 0.03 7.83 r 2 = 0.86; Durbin @ Watson statistic = 2.18 2. Plot the data and regression line for the above estimation. Evaluate the regression using the criteria of economic plausibility, goodness of fit, and slope of the regression line. 3. Brown expects Spirit to generate, on average, 3,600 freight miles each month next year. How much in operating costs should Brown budget for next year? Required Required

ASSIGNMENT MATERIAL 419 4. Name three variables, other than freight miles, that Brown might expect to be important cost drivers for Spirit’s operating costs. 5. Brown next conducts a regression using the monthly data of maintenance costs on freight miles. Verify that she obtained the following result: Regression : Maintenance costs = a + ( b * Number of freight miles) Variable Coefficient Standard Error t -Value Constant $1,170.57 $91.07 12.85 Independent variable: No. of freight miles $ - 0.15 $ 0.03 - 5.83 r 2 = 0.77; Durbin @ Watson statistic = 1.94 6. Provide a reasoned explanation for the observed sign on the cost driver variable in the maintenance cost regression. What alternative data or alternative regression specifications would you like to use to better capture the above relationship? 10-40 Cost estimation, cumulative average-time learning curve. The Pacific Boat Company, which is under contract to the U.S. Navy, assembles troop deployment boats. As part of its research program, it completes the assembly of the first of a new model (PT109) of deployment boats. The Navy is impressed with the PT109. It requests that Pacific Boat submit a proposal on the cost of producing another six PT109s. Pacific Boat reports the following cost information for the first PT109 assembled and uses a 90% cumu- lative average-time learning model as a basis for forecasting direct manufacturing labor-hours for the next six PT109s. (A 90% learning curve means b = - 0.152004.) ± ² ³ ´ µ ¶ · ¸ ¹ ±º ±± & % $ $ 199,000 l cost a i r e t a m t c e r i D Direct manufacturing labor time for first boat 14,700 labor hours r u o h - r o b a l g n i r u t c a f u n a m t c e r i d r e p 2 4 $ e t a r r o b a l g n i r u t c a f u n a m t c e r i D r u o h - r o b a l g n i r u t c a f u n a m t c e r i d r e p 6 2 $ t s o c d a e h r e v o g n i r u t c a f u n a m e l b a i r a V s t s o c r o b a l g n i r u t c a f u n a m t c e r i d f o % 20 d a e h r e v o g n i r u t c a f u n a m r e h t O Tooling costs a $ 279,000 Learning curve for manufacturing labor time per boat 90% cumulative average time b b Using the formula (page 391) for a 90% learning curve, a Tooling can be reused at no extra cost because all of its cost has been assigned to the first deployment boat. b 5 5 5 ] 0.152004 ln 0.9 ln 2 ] 0.105361 0.693147 1. Calculate predicted total costs of producing the six PT109s for the Navy. (Pacific Boat will keep the first deployment boat assembled, costed at $1,477,600, as a demonstration model for potential customers.) 2. What is the dollar amount of the difference between (a) the predicted total costs for producing the six PT109s in requirement 1 and (b) the predicted total costs for producing the six PT109s, assuming that there is no learning curve for direct manufacturing labor? That is, for (b) assume a linear function for units produced and direct manufacturing labor-hours. 10-41 Cost estimation, incremental unit-time learning model. Assume the same information for the Pacific Boat Company as in Problem 10-40 with one exception. This exception is that Pacific Boat uses a 90% incremental unit-time learning model as a basis for predicting direct manufacturing labor-hours in its assembling operations. (A 90% learning curve means b = - 0.152004.) 1. Prepare a prediction of the total costs for producing the six PT109s for the Navy. 2. If you solved requirement 1 of Problem 10-40, compare your cost prediction there with the one you made here. Why are the predictions different? How should Pacific Boat decide which model it should use? Required Required

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420 CHAPTER 10 DETERMINING HOW COSTS BEHAVE 10-42 Regression; choosing among models. Apollo Hospital specializes in outpatient surgeries for rela- tively minor procedures. Apollo is a nonprofit institution and places great emphasis on controlling costs in order to provide services to the community in an efficient manner. Apollo’s CFO, Julie Chen, has been concerned of late about the hospital’s consumption of medical sup- plies. To better understand the behavior of this cost, Julie consults with Rhett Bratt, the person responsible for Apollo’s cost system. After some discussion, Julie and Rhett conclude that there are two potential cost drivers for the hospital’s medical supplies costs. The first driver is the total number of procedures performed. The second is the number of patient-hours generated by Apollo. Julie and Rhett view the latter as a poten- tially better cost driver because the hospital does perform a variety of procedures, some more complex than others. Rhett provides the following data relating to the past year to Julie. ± ² ³ $ ´ µ ¶ · ¸ % ¹ 2 1 Month 4 3 5 6 7 8 9 ±º ±± ±² ±³ Medical supplies costs 230,000 84,000 238,000 193,000 180,000 210,000 92,000 222,000 $106,000 Number of procedures 500 240 520 240 340 420 360 320 320 Number of patient-hours 3,900 1,900 4,100 3,400 3,700 3,100 1,200 3,000 10 78,000 180 1,300 11 127,000 440 2,800 12 225,000 380 3,800 2,000 ' & 1. Estimate the regression equation for (a) medical supplies costs and number of procedures and (b) medical supplies costs and number of patient-hours. You should obtain the following results: Regression 1 : Medical supplies costs = a + ( b * Number of procedures) Variable Coefficient Standard Error t -Value Constant $36,939.77 $56,404.86 0.65 Independent variable: No. of procedures $ 361.91 $ 152.93 2.37 r 2 = 0.36; Durbin @ Watson statistic = 2.48 Regression 2 : Medical supplies costs = a + ( b * Number of patient @ hours) Variable Coefficient Standard Error t -Value Constant $3,654.86 $23,569.51 0.16 Independent variable: No. of patient-hours $ 56.76 $ 7.82 7.25 r 2 = 0.84; Durbin @ Watson statistic = 1.91 2. On different graphs plot the data and the regression lines for each of the following cost functions: a. Medical supplies costs = a + ( b * Number of procedures) b. Medical supplies costs = a + ( b * Number of patient @ hours) 3. Evaluate the regression models for “Number of procedures” and “Number of patient-hours” as the cost driver according to the format of Exhibit 10-18 (page 406). 4. Based on your analysis, which cost driver should Julie Chen adopt for Apollo Hospital? Explain your answer. Required

ASSIGNMENT MATERIAL 421 10-43 Multiple regression (continuation of 10-42). After further discussion, Julie and Rhett wonder if they should view both the number of procedures and number of patient-hours as cost drivers in a multiple regression estimation in order to best understand Apollo’s medical supplies costs. 1. Conduct a multiple regression to estimate the regression equation for medical supplies costs using both number of procedures and number of patient-hours as independent variables. You should obtain the following result: Regression 3 : Medical supplies costs = a + ( b 1 * No. of procedures) + ( b 2 * No. of patient @ hours) Variable Coefficient Standard Error t -Value Constant - $3,103.76 $30,406.54 - 0.10 Independent variable 1: No. of procedures $ 38.24 $ 100.76 0.38 Independent variable 2: No. of patient-hours $ 54.37 $ 10.33 5.26 r 2 = 0.84; Durbin @ Watson statistic = 1.96 2. Evaluate the multiple regression output using the criteria of economic plausibility goodness of fit, sig- nificance of independent variables, and specification of estimation assumptions. 3. What potential issues could arise in multiple regression analysis that are not present in simple regres- sion models? Is there evidence of such difficulties in the multiple regression presented in this problem? Explain. 4. Which of the regression models from Problems 10-42 and 10-43 would you recommend Julie Chen use? Explain. 10-44 Cost estimation. Hankuk Electronics started production on a sophisticated new smartphone running the Android operating system in January 2017. Given the razor-thin margins in the consumer electronics indus- try, Hankuk’s success depends heavily on being able to produce the phone as economically as possible. At the end of the first year of production, Hankuk’s controller, Inbee Kim, gathered data on its monthly levels of output, as well as monthly consumption of direct labor-hours (DLH). Inbee views labor-hours as the key driver of Hankuk’s direct and overhead costs. The information collected by Inbee is provided below: ± ² ³ $ ´ µ ¶ · ¸ % ¹ February January Month April March May June July August September ±º ±± ±² ±³ Output (Units) Direct Labor-Hours October November December 492 660 504 612 636 648 600 648 684 696 672 675 820 875 670 760 765 735 660 695 710 690 700 1,400 & 1. Inbee is keen to examine the relationship between direct labor consumption and output levels. She decides to estimate this relationship using a simple linear regression based on the monthly data. Verify that the following is the result obtained by Inbee: Regression 1 : Direct labor @ hours = a + ( b * Output units) Variable Coefficient Standard Error t -Value Constant 345.24 589.07 0.59 Independent variable: Output units 0.71 0.93 0.76 r 2 = 0.054; Durbin @ Watson statistic = 0.50 Required Required

422 CHAPTER 10 DETERMINING HOW COSTS BEHAVE 2. Plot the data and regression line for the above estimation. Evaluate the regression using the criteria of economic plausibility, goodness of fit, and slope of the regression line. 3. Inbee estimates that Hankuk has a variable cost of $17.50 per direct labor-hour. She expects that Han- kuk will produce 650 units in the next month, January 2018. What should she budget as the expected variable cost? How confident is she of her estimate? 10-45 Cost estimation, learning curves (continuation of 10-44). Inbee is concerned that she still does not understand the relationship between output and labor consumption. She consults with Jim Park, the head of engineering, and shares the results of her regression estimation. Jim indicates that the production of new smartphone models exhibits significant learning effects—as Hankuk gains experience with produc- tion, it can produce additional units using less time. He suggests that it is more appropriate to specify the following relationship: y = ax b where x is cumulative production in units, y is the cumulative average direct labor-hours per unit (i.e., cumu- lative DLH divided by cumulative production), and a and b are parameters of the learning effect. To estimate this, Inbee and Jim use the original data to calculate the cumulative output and cumulative average labor-hours per unit for each month. They then take natural logarithms of these variables in order to be able to estimate a regression equation. Here is the transformed data: ²± ²² ²³ $ ²´ ²µ ²¶ ²· ²¸ % ²¹ February January Month April March May June July August September ³º ³± ³² ³³ Cumulative Output (x) Cumulative DLH Cumulative Avg DLH (y) LN (y) LN (x) October November December 1,176 1,836 2,340 2,952 3,588 4,236 4,836 5,484 684 6,180 6,852 7,527 2,220 3,095 3,765 4,525 5,290 6,025 6,685 7,380 8,090 8,780 9,480 1,400 7.070 7.515 7.758 7.990 8.185 8.351 8.484 8.610 8.729 8.832 8.926 6.528 0.635 0.522 0.476 0.427 0.388 0.352 0.324 0.297 0.269 0.248 0.231 0.716 1.888 1.686 1.609 1.533 1.474 1.422 1.382 1.346 1.309 1.281 1.259 2.047 & ' ( ) 1. Estimate the relationship between the cumulative average direct labor-hours per unit and cumulative output (both in logarithms). Verify that the following is the result obtained by Inbee and Jim: Regression 1 : Ln (Cumulative avg DLH per unit) = a + [ b * Ln (Cumulative Output)] Variable Coefficient Standard Error t -Value Constant 2.087 0.024 85.44 Independent variable: Ln (Cum Output) - 0.208 0.003 - 69.046 r 2 = 0.998; Durbin @ Watson statistic = 2.66 2. Plot the data and regression line for the above estimation. Evaluate the regression using the criteria of economic plausibility, goodness of fit, and slope of the regression line. 3. Verify that the estimated slope coefficient corresponds to an 86.6% cumulative average-time learning curve. 4. Based on this new estimation, how will Inbee revise her budget for Hankuk’s variable cost for the ex- pected output of 650 units in January 2018? How confident is she of this new cost estimate? Required

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ASSIGNMENT MATERIAL 423 10-46 Interpreting regression results, matching time periods. Nandita Summers works at Modus, a store that caters to fashion for young adults. Nandita is responsible for the store’s online advertising and promotion budget. For the past year, she has studied search engine optimization and has been purchasing keywords and display advertising on Google, Facebook, and Twitter. In order to analyze the effectiveness of her efforts and to decide whether to continue online advertising or move her advertising dollars back to traditional print media, Nandita collects the following data: ± ² ³ $ ´ µ ¶ · ¸ % ¹ June ±± Month October September December November January February March April May ±º Online Advertising Expense 5,472 3,942 1,440 4,919 4,142 1,290 5,722 2,214 $5,125 5,730 Sales Revenue 42,480 53,106 64,560 34,517 59,438 51,840 36,720 59,568 July ±² 1,716 35,450 August ±³ 1,875 36,211 $44,875 62,564 & 1. Nandita performs a regression analysis, comparing each month’s online advertising expense with that month’s revenue. Verify that she obtains the following result: Revenue = $51,999.64 - (0.98 * Online advertising expense) Variable Coefficient Standard Error t -Value Constant $51,999.64 7,988.68 6.51 Independent variable: Online advertising expense - 0.98 1.99 - 0.49 r 2 = 0.02; Durbin @ Watson statistic = 2.14 2. Plot the preceding data on a graph and draw the regression line. What does the cost formula indicate about the relationship between monthly online advertising expense and monthly revenues? Is the rela- tionship economically plausible? 3. After further thought, Nandita realizes there may have been a flaw in her approach. In particular, there may be a lag between the time customers click through to the Modus website and peruse its social media content (which is when the online ad expense is incurred) and the time they actually shop in the physical store. Nandita modifies her analysis by comparing each month’s sales revenue to the advertis- ing expense in the prior month. After discarding September revenue and August advertising expense, show that the modified regression yields the following: Revenue = $28,361.37 + (5.38 * Online advertising expense) Variable Coefficient Standard Error t -Value Constant $28,361.37 5,428.69 5.22 Independent variable: Previous month’s online advertising expense r 2 = 0.65; Durbin @ Watson statistic = 1.71 5.38 1.31 4.12 4. What does the revised formula indicate? Plot the revised data on a graph. Is this relationship economi- cally plausible? 5. Can Nandita conclude that there is a cause-and-effect relationship between online advertising ex- pense and sales revenue? Why or why not? Required

424 CHAPTER 10 DETERMINING HOW COSTS BEHAVE 10-47 Purchasing department cost drivers, activity-based costing, simple regression analysis. Perfect Fit operates a chain of 10 retail department stores. Each department store makes its own purchasing deci- sions. Carl Hart, assistant to the president of Perfect Fit, is interested in better understanding the drivers of purchasing department costs. For many years, Perfect Fit has allocated purchasing department costs to products on the basis of the dollar value of merchandise purchased. A $100 item is allocated 10 times as many overhead costs associated with the purchasing department as a $10 item. Hart recently attended a seminar titled “Cost Drivers in the Retail Industry.” In a presentation at the seminar, Kaliko Fabrics, a leading competitor that has implemented activity-based costing, reported num- ber of purchase orders and number of suppliers to be the two most important cost drivers of purchasing department costs. The dollar value of merchandise purchased in each purchase order was not found to be a significant cost driver. Hart interviewed several members of the purchasing department at the Perfect Fit store in Miami. They believed that Kaliko Fabrics’ conclusions also applied to their purchasing department. Hart collects the following data for the most recent year for Perfect Fit’s 10 retail department stores: ± ² ³ $ ´ µ ¶ · ¸ % ¹ Chicago Baltimore Department Store Miami Los Angeles New York Phoenix Seattle St. Louis Vancouver ±± Toronto ±º Purchasing Department Costs (PDC) 1,095,000 542,000 2,053,000 1,068,000 517,000 1,544,000 1,761,000 1,263,000 $1,522,000 1,605,000 Dollar Value of Merchandise Purchased (MP$) 33,463,000 121,800,000 119,450,000 33,575,000 29,836,000 102,840,000 38,725,000 130,110,000 $ 68,307,000 139,300,000 Number of Purchase Orders (No. of POs) 2,548 1,420 5,935 2,786 1,334 7,581 3,623 1,712 4,736 4,345 Number of Suppliers (No. of Ss) 230 8 188 21 29 101 127 202 196 125 ' ( & Hart decides to use simple regression analysis to examine whether one or more of three variables (the last three columns in the table) are cost drivers of purchasing department costs. Summary results for these regressions are as follows: Regression 1 : PDC = a + ( b * MP$) Variable Coefficient Standard Error t -Value Constant $1,041,421 $346,709 3.00 Independent variable 1: MP$ 0.0031 0.0038 0.83 r 2 = 0.08; Durbin @ Watson statistic = 2.41 Regression 2 : PDC = a + ( b * No. of POs) Variable Coefficient Standard Error t -Value Constant $722,538 $265,835 2.72 Independent variable 1: No. of POs $ 159.48 $ 64.84 2.46 r 2 = 0.43; Durbin @ Watson statistic = 1.97 Regression 3 : PDC = a + ( b * No. of Ss) Variable Coefficient Standard Error t -Value Constant $828,814 $246,571 3.36 Independent variable 1: No. of Ss $ 3,816 $ 1,698 2.25 r 2 = 0.39; Durbin @ Watson statistic = 2.01

ASSIGNMENT MATERIAL 425 1. Compare and evaluate the three simple regression models estimated by Hart. Graph each one. Also, use the format employed in Exhibit 10-18 (page 406) to evaluate the information. 2. Do the regression results support the Kaliko Fabrics’ presentation about the purchasing department’s cost drivers? Which of these cost drivers would you recommend in designing an ABC system? 3. How might Hart gain additional evidence on drivers of purchasing department costs at each of Perfect Fit’s stores? 10-48 Purchasing department cost drivers, multiple regression analysis (continuation of 10-47). Carl Hart decides that the simple regression analysis used in Problem 10-47 could be extended to a multiple regression analysis. He finds the following results for two multiple regression analyses: Regression 4 : PDC = a + ( b 1 * No. of POs) + ( b 2 * No. of Ss) Variable Coefficient Standard Error t -Value Constant $484,522 $256,684 1.89 Independent variable 1: No. of POs $ 126.66 $ 57.80 2.19 Independent variable 2: No. of Ss $ 2,903 $ 1,459 1.99 r 2 = 0.64; Durbin @ Watson statistic = 1.91 Regression 5 : PDC = a + ( b 1 * No. of POs) + ( b 2 * No. of Ss) + ( b 3 * MP$) Variable Coefficient Standard Error t -Value Constant $483,560 $312,554 1.55 Independent variable 1: No. of POs $ 126.58 $ 63.75 1.99 Independent variable 2: No. of Ss $ 2,901 $ 1,622 1.79 Independent variable 3: MP$ 0.00002 0.0029 0.01 r 2 = 0.64; Durbin @ Watson statistic = 1.91 The coefficients of correlation between combinations of pairs of the variables are as follows: PDC MP$ No. of POs MP$ 0.28 No. of POs 0.66 0.27 No. of Ss 0.62 0.30 0.29 1. Evaluate regression 4 using the criteria of economic plausibility, goodness of fit, significance of independent variables, and specification analysis. Compare regression 4 with regressions 2 and 3 in Problem 10-47. Which one of these models would you recommend that Hart use? Why? 2. Compare regression 5 with regression 4. Which one of these models would you recommend that Hart use? Why? 3. Hart estimates the following data for the Baltimore store for next year: dollar value of merchandise purchased, $78,500,000; number of purchase orders, 4,100; number of suppliers, 110. How much should Hart budget for purchasing department costs for the Baltimore store for next year? 4. What difficulties do not arise in simple regression analysis that may arise in multiple regression analy- sis? Is there evidence of such difficulties in either of the multiple regressions presented in this prob- lem? Explain. 5. Give two examples of decisions in which the regression results reported here (and in Problem 10-47) could be informative. Required

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426 LEARNING OBJECTIVES 1 Use the five-step decision-making process 2 Distinguish relevant from irrelevant information in decision situations 3 Explain the concept of opportunity cost and why managers should consider it when making insourcing-versus-outsourcing decisions 4 Know how to choose which products to produce when there are capacity constraints 5 Explain how to manage bottlenecks 6 Discuss the factors managers must consider when adding or dropping customers or business units 7 Explain why book value of equipment is irrelevant to managers making equipment- replacement decisions 8 Explain how conflicts can arise between the decision model a manager uses and the performance-evaluation model top management uses to evaluate managers 11 How many decisions have you made today? Maybe you made a big decision, such as investing in a mutual fund. Or maybe your decision was as simple as buying a coffee maker or choosing a restaurant for dinner. Regardless of whether decisions are significant or routine, the decision process often includes evaluating the costs and benefits of each choice. For decisions that involve costs, some costs are irrelevant. For example, once you purchase a coffee maker, its cost is irrelevant when calculating how much money you save each time you brew coffee at home versus buy it at Starbucks. You incurred the cost of the coffee maker in the past, and you can’t recoup that cost. This chapter will explain which costs and benefits are relevant and which are not—and how you should think of them when choosing among alternatives. RELEVANT COSTS AND BROADWAY SHOWS 1 The incremental cost to a Broadway producer for an additional customer to attend a Broadway musical like “Hamilton” is incredibly small. Most of the costs (actor fees, performance sets, theater rental, and publicity and marketing) are fixed weeks and months in advance of the performance. An orchestra ticket for “Hamilton” sells for $177. But because incremental costs are so small, is it worthwhile for the show’s producer to sell tickets considerably below this price to avoid having empty seats that earning nothing? If demand is high and the show is sold out, the producer would not sell tickets for anything less than $177 because there are theatergoers willing to pay full price to see the show. But if on the day before the show, it appears as though the venue will not be full, the producer may be willing to lower ticket prices significantly in hopes of attracting more theatergoers and earning a profit on the unfilled seats. Enter TKTS. The famous discount ticket booth in Times Square sells same-day tickets to Broadway musicals, plays, and dance productions for up to 50% of face value. Ticket availability changes every day depending on demand and theatergoers can browse real-time listings on the TKTS mobile app. Decision Making and Relevant Information 1 Haley Goldberg, “You won’t believe what these fans are doing for ‘Hamilton’ tickets,” New York Post , November 13, 2015 (http://nypost.com/2015/11/13/ you-wont-believe- what-these-fans-are-doing-for-hamilton-tickets/); Pia Catton, “For Broadway, 2015 Was a Mixed Bag,” The Wall Street Journal , January 4, 2016 (http://www.wsj.com/articles/ for-broadway-2015-was-a-mixed-bag-1451958995); Musical Workshop, “Production Costs and ROI of Theatrical Shows—From Broadway to West End,” (http://www .musicalworkshop.org/workshop/production-costs-and-roi-of-theatrical-shows-from- broadway-to-west-end/), accessed March 2016; Theatre Development Fund, “TKTS Ticket Booths” (https://www.tdf.org/nyc/7/TKTS-Overview), accessed March 2016. Francis Vachon/Alamy Stock Photo

Information and the Decision Process Managers usually follow a decision model for choosing among different courses of action. A decision model is a formal method of making a choice that often involves both quantita- tive and qualitative analyses. Management accountants analyze and present relevant data to guide managers’ decisions. Consider a strategic decision facing managers at Precision Sporting Goods, a manufac- turer of golf clubs: Should the company reorganize its manufacturing operations to reduce manufacturing labor costs? Precision Sporting Goods has only two alternatives: do not reor- ganize or reorganize. Reorganization will eliminate all manual handling of materials. Current manufactur- ing labor consists of 20 workers: 15 workers operate machines and 5 workers handle ma- terials. The 5 materials-handling workers have been hired on contracts that permit layoffs without additional payments. Each worker works 2,000 hours annually. Reorganization is predicted to cost $90,000 each year (mostly for new equipment leases). The reorganization will not affect the production output of 25,000 units, the selling price of $250, the direct material cost per unit of $50, manufacturing overhead of $750,000, or marketing costs of $2,000,000. Managers use the five-step decision-making process presented in Exhibit 11-1 and first introduced in Chapter 1 to make this decision. Study the sequence of steps in this exhibit and note how managers make no reference to information about production volumes, sell- ing price, and costs that are unaffected by the decision. Step 5 evaluates performance to pro- vide feedback about actions taken in the previous steps. This feedback might affect future predictions, the prediction methods used, the way choices are made, or the implementation of the decision. The Concept of Relevance Much of this chapter focuses on Step 4 in Exhibit 11-1 and on the concepts of relevant costs and relevant revenues when choosing among alternatives. Relevant Costs and Relevant Revenues Relevant costs are expected future costs and relevant revenues are expected future revenues that differ among the alternative courses of action being considered. Costs and revenues that are not relevant are called irrelevant . It is important to recognize that relevant costs and rel- evant revenues must : ■ Occur in the future —every decision deals with a manager selecting a course of action based on its expected future results. ■ Differ among the alternative courses of action —future costs and revenues that do not dif- fer will not matter and, therefore, will have no bearing on the decision being made. The question is always, “What difference will a particular action make?” Exhibit 11-2 presents the financial data underlying the choice between the do-not-reorganize and reorganize alternatives for Precision Sporting Goods. Managers can analyze the data in two ways: by considering “all costs and revenues” or considering only “relevant costs and revenues.” LEARNING OBJECTIVE 1 Use the five-step decision-making process . . . the five steps are identifying the problem and uncertainties; obtaining information; making predictions about the future; making decisions by choosing among alternatives; and implementing the decision, evaluating performance, and learning DECISION POINT What is the five-step process that managers can use to make decisions? LEARNING OBJECTIVE 2 Distinguish relevant from irrelevant information in decision situations . . . only costs and revenues that are expected to occur in the future and differ among alternative courses of action are relevant Just like on Broadway, managers at corporations around the world use their deep understand- ing of costs to make decisions. Managers at JPMorgan Chase gather information about financial markets, consumer preferences, and economic trends before determining whether to offer new services to customers. Managers at Macy’s examine all the relevant information related to domestic and international clothing manufacturing before selecting vendors. Managers at Porsche gather cost information to decide whether to manufacture a component part or purchase it from a sup- plier. The decision process may not always be easy, but as Peter Drucker said, “Wherever you see a successful business, someone once made a courageous decision.”

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