Questions
A batch production operation has a machine setup time of 4.0 hr and a processing time...

A batch production operation has a machine setup time of 4.0 hr and a processing time of 1.50 min per cycle. Three parts are produced each cycle. No tool handling time is included in the cycle. Part handling time each cycle is 30 sec. It consists of the worker obtaining three starting work units from a parts tray, loading them into the machine, and then after processing, unloading the completed units and placing them into the same tray. Each tray holds 27 work units. When all of the starting work units have been replaced with completed units, the tray of completed parts is moved aside and a new tray of starting parts is moved into position at the machine. This irregular work element takes 4.0 min. Batch quantity is 2,700 units.

Determine

(a) average cycle time,

(b) time to complete the batch,

(c) average production rate.

In: Mechanical Engineering

Background Helio, Inc. (the Company) is a medical device company founded in 2013 in Provo, Utah...

Background

Helio, Inc. (the Company) is a medical device company founded in 2013 in Provo, Utah that specializes in the development and manufacturing of cutting-edge medical devices designed for all types of joint replacement surgeries. In January 2015, the FDA approved Helio’s premier product, a hinged titanium axle designed to provide physicians with more precise placement of joints during joint replacement surgery.

In early 2016, approximately one year after the new product’s approval, the Company hired a new senior vice president (SVP) of sales to oversee sales, physician training, product delivery, and customer service. The broad set of responsibilities allowed the charismatic SVP to significantly influence the Company’s revenue generation. The hiring of the new SVP was also done in large part to help guide the company’s development of an important new sales channel: third-party distributors that are each strategically located in close proximity to key hospitals in regions around the country.

The move to hire the SVP was in direct response to overwhelming disappointment about the first year’s sales volume for the new surgical implant, which was lagging significantly behind expectations. Reports from the field led management to recommend the new sales channel to the board of directors that overwhelmingly approved the new strategy, the execution of which was being led by the new SVP.

Execution of strategy

To help execute the new strategy, the SVP hired five regional sales managers who would become his trusted cohorts. Together, they set aggressive sales targets for the Company’s surgical implants. The sales targets focused on achieving a growth pattern that was characterized by a record high sales volume for each successive quarter in each region. In fact, it is fair to say that the sales targets were intentionally created at almost unreachable levels to remove any question about possible weakness in demand for the Company’s new product.

The strategy focused on the development of a new sales channel with third-party distributors. Each of the distributors had already established close relationships with the physicians that were actually using the product during surgical procedures. To help pay for the launch of their new product, along with the execution of the new strategy, the Company was also working hard to raise a significant amount of new investment capital to fund the resulting increased operating costs. In order to be successful in attracting the new investment capital, top management made it clear to the SVP how important it was to report strong sales for its premier product, the surgical implant for titanium joints. The SVP, in turn, passed along the same message to the regional sales managers.

© 2018 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. NDPPS 741662

1

Management control philosophy

The upper management team of Helio can be described as being aggressive in business practices and often emphasizes speed and efficiency when implementing their decisions. Management rarely hires external consultants because they are of the opinion that consultants are too expensive and often follow a conservative approach. The upper management team meets regularly with its key managers. In general, the upper management team has cooperated with the audit team in order to provide fair and adequate financial reporting, but there have been disagreements in the past. The Company has a strict policy for following all established internal control procedures.

Incentive compensation

Top management focuses significant attention on achieving short-term performance measures based on the audited financial statements when determining compensation and making promotion decisions. Revenue earned is the most important criterion in performance assessment throughout the organization. As part of the launch of its new surgical implant, a new bonus plan was established to provide additional incentives for the entire organization to focus on this new opportunity, with revenue earned as the key criterion used to determine incentive compensation.

Preliminary results

Despite the SVP’s optimism about sales in 2017, internal reports have indicated that the actual sales volume of the surgical implant was well below budget each quarter. The SVP responded to these reports by repeatedly communicating his disappointment to the regional sales managers. Furthermore, he consistently warned that if the team could not boost sales, the Company would likely not be able to raise additional investment capital and would then be forced to significantly downsize its headcount.

Unfortunately, boosting revenue of the new surgical implants was not as simple as merely shipping the product to distributors. The distributors were hesitant to purchase product until the sale to the final customer was finalized as the distributors did not want to be stuck with the inventory on their own balance sheets. Further, the terms of the sales do not include any refund or rebate conditions. In addition, the Company has no intention of changing those terms and accepting any return. Therefore, any sale to distributors are final.

By the end of 2017, the Company had signed on a total of 73 distributors to sell its surgical implants in more than 20 different states throughout the United States. Each distributor was independently owned and operated but the company routinely shared best practices among its network. The SVP monitored sales closely from the distributor network through his regional sales managers. In fact, he even maintained a monthly sales report from each of the 73 distributors.

The Company invoices customers when the goods are shipped, and invoicing triggers the recording of revenue. The Company does not include freight costs in sales revenue but does offset shipping costs with any freight charged to customers.

The following relevant financial data is taken from the Company’s unaudited trial balance, which was used to produce the unaudited financial statements:

© 2018 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. NDPPS 741662

Sales revenue, year ended 12/31/2017

$84,867,855

Gross accounts receivable, 12/31/2017

$11,988,886

2

Audit approach

Your audit team is currently in the midst of year-end testing in the revenue and accounts receivable cycle for the audit of the calendar year 2017 financial statements. Your testing will focus on the existence/occurrence, cutoff, and accuracy assertions for sales revenue, as well as the existence and valuation assertions for accounts receivable. The audit team has assessed the risk of material misstatement (RMM) for each relevant assertion in order to determine the nature, timing, and extent of the procedures to be performed at Helio.

Other members of the audit team have already completed a walk-through of the revenue and accounts receivable processes, identified “what could go wrongs” within the process, and identified the controls that have been placed in operation to mitigate the risks. Based on the work performed, the team decided to test the operating effectiveness of certain key controls during interim testing. The results are found below.

Tests of controls – Revenue and accounts receivable cycle – Interim

Four key application controls were tested at interim. The information technology (IT) auditors tested the general controls (GITCs) over program changes, access to programs, and computer operations that are relevant to the revenue and accounts receivable cycle. The GITCs were found to be effective. In addition, the IT auditors tested the system to make sure that proper segregation of duties occurred throughout the period and were operating effectively.

The first control is an automated three-way sales match. The control matches the details from 1) an approved sales order; 2) relevant shipping documents; and 3) the sales invoice before revenue is recorded. A test of the control’s operating effectiveness was conducted at the interim. No exceptions

new customers, including the new distributors. A test of the control’s operating effectiveness was conducted at interim. No exceptions were noted.

The third control is an automated sales authorization control. When a sales order is entered into the system, the amount of the sale is added to the existing accounts receivable balance for that customer. The sum is then compared to the customer’s credit limit. A test of the control’s operating effectiveness was conducted at interim. No exceptions were noted.

The fourth control is a monthly review of the adequacy of the allowance for doubtful accounts, completed by the controller. A test of the control’s operating effectiveness was conducted at interim. No exceptions were noted.

© 2018 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. NDPPS 741662

were noted.

The second control requires the credit department at Helio to conduct a detailed credit check for all

3

Roll-forward period

By the end of the third quarter of 2017, sales revenue for the company’s premier surgical implant was still lagging far behind expectations. To help ensure that Helio delivered impressive fourth quarter revenue numbers, the entire sales team, led by the SVP and the regional sales managers, began to exert pressure on a number of distributors in an attempt to improve sales in 2017. This effort seemed to be paying off as the sales team successfully persuaded more than a dozen distributors to purchase product in advance of final customer demand.

These circumstances presented a problem for the Company, because the distributors began to ask for concessions from Helio. For example, in order to persuade the distributors, the Company agreed to hold the inventory in their own warehouse.

The SVP’s actions led to a dramatic increase in revenue for the fourth quarter of 2017. In fact, sales increased year-over-year by 214 percent for the fourth quarter alone. The upward trajectory of sales revenue helped the Company raise the much-needed investment capital as Helio issued more than 10 million shares of common stock for $40 million in early 2018.

  1. [5 points] Based on your understanding of fraud risk assessment and material:
    • Identify at least three (3) specific fraud risk factors related to Helio.

In: Operations Management

Go to the Files section and download the AFE_Test file from the Datasets folder. We are...

Go to the Files section and download the AFE_Test file from the Datasets folder. We are interested in a one­tail test described in the following fashion: Ho: u < or = to 200 CFM; H1: u > 200 CFM. At 5% significance level,

we can reject the null hypothesis given the sample information in AFE_Test1.

we can reject the null hypothesis given the sample information in AFE_Test2.

we cannot reject the null hypothesis.

we can reject the null hypothesis given the sample information in AFE_Test1 and AFE_Test2.

Engine

Number Air Flow Error

1

415.4743512

2

346.09016

3

-8.596266867

4

532.7726337

5

283.0702257

6

189.3824034

7

105.5314484

8

334.2984697

9

184.4056484

10

256.663138

11

-104.457736

12

467.3703235

13

258.2404746

14

-2.074507734

15

-40.79538064

16

6.80056894

17

90.27253748

18

338.7010153

19

331.0819963

20

649.4146936

21

313.2307526

22

503.0561507

23

403.6624912

24

223.0000044

25

214.666741

26

-201.8286026

27

181.0092996

28

405.3486368

29

78.97865964

30

105.2625563

31

-20.7479801

32

305.6369038

33

-25.34042812

34

290.649136

35

160.2956032

36

581.8788331

37

87.38225426

38

236.7845624

39

461.8920726

40

-1.765506909

41

-1.139319807

42

155.2220552

43

172.5709168

44

324.958664

45

137.8738076

46

431.9881597

47

294.0063993

48

370.8186838

49

-268.3206689

50

377.8966444

51

15.56555128

52

400.5351499

53

237.0884473

54

-114.9040103

55

-1.842450161

56

258.17274

57

306.9807505

58

199.0776624

59

-159.7275472

60

90.7100499

61

50.57186593

62

-235.5723112

63

239.8702733

64

252.8041065

65

66.01740517

66

139.3463705

67

157.9240731

68

398.7363967

69

349.8917564

70

157.576396

71

108.0615717

72

246.23303

73

284.4200068

74

416.5905053

75

39.32832863

76

188.2311195

77

218.0355792

78

198.1066631

79

399.3020115

80

158.6990307

81

404.0966402

Engine Number Air Flow Error

1

-5.910636889

2

454.3618488

3

248.8773234

4

280.4565938

5

353.3013896

6

-218.8510862

7

94.68939062

8

332.6359874

9

425.0468792

10

299.2086466

11

-5.854975813

12

304.4174591

13

539.9043275

14

408.6882527

15

-130.7805305

16

712.8235887

17

110.8343768

18

41.30960043

19

293.1632807

20

219.859895

21

147.6832367

22

522.4087418

23

-22.70800359

24

102.6290747

25

518.992134

26

49.33093443

27

324.0126134

28

486.8666893

29

522.5290679

30

264.1219098

31

37.28276716

32

106.6241894

33

45.27340572

34

362.001093

35

110.5986357

36

335.748915

37

226.6452257

38

350.815877

39

275.9994104

40

195.8830681

41

391.2196789

42

439.131587

43

274.5389211

44

210.0179118

45

302.4803718

46

307.4775905

47

-112.4925929

48

463.7959919

49

204.2295691

50

371.1563168

51

86.33736435

52

68.51681611

53

262.680861

54

268.7462811

55

444.2777809

56

468.5967597

57

388.3007466

58

276.8384193

59

184.5206371

60

94.26292855

61

453.4004675

62

175.5802814

63

22.65986588

64

249.5145609

65

155.0875923

66

243.9447699

67

528.9350029

68

512.2006642

69

94.9378191

70

604.680723

71

240.4991037

72

399.2537004

73

194.1203309

74

197.734822

75

268.7425977

76

356.5817097

77

515.0917659

78

394.3821284

79

399.1902631

80

338.4149168

81

192.9782557

In: Statistics and Probability

A study is conducted to determine the relationship between a driver's age and the number of...

A study is conducted to determine the relationship between a driver's age and the number of accidents he or she has over a 1-year period. The data are shown here. If there is a significant relationship, predict the number of accidents of a driver who is 28.

Driver's No. of
Age x accidents y
16 3
24 2
18 5
17 2
23 0
27 1
32 1


1. What is your alternative hypothesis (H1)?

2. Compute the value of the correlation coefficient.

3. Determine the regression equation line even if the equation is not valid.

4. Test the significance of the correlation coefficient and the regression equation at a level of significance of .01.

In: Statistics and Probability

An air engine is modeled after the ott cycle. Assume ideal gas and air standard cold...

An air engine is modeled after the ott cycle. Assume ideal gas and air standard cold valus for properties. Intake air is at 100kPa and Temperature of 27°C. The compression ration is 8:1. The heat added during ignition is 1740 kJ/kg
a) Draw and Label the Pv and Ts diagrams for the Otto cycle
b)Find the specific volume at intake state #1
c) Find the temperature at the end of the compression stroke
d) Find the Pressure at the end of the compression stroke
e) Calculate the efficiency for the cycle
f) For the Carnot air heat engine working between the same temperature resevoirs, what is the Carnot Efficiency

In: Mechanical Engineering

a. A ball is traveling towards another ball which is at rest. The ball that is...

a. A ball is traveling towards another ball which is at rest. The ball that is traveling has a speed of 3.5 m/s and weighs 22 g. The ball that is at rest weighs 27 g. There is a head on elastic collision between the balls. What is the velocity of each ball after the collision?

b. A dog sled is sliding down a hill. The sled starts from a height of 25 m and has a weight of 66 kg. It collides with a person at the bottom of the hill. The person weighs 72 kg. The collision is completely inelastic. Friction can be ignored due to icy conditions. Determine the speed of the sled and the person after the collision.

In: Physics

I want to know how long basketball shoes last for people on average. I rubbed a...

I want to know how long basketball shoes last for people on average. I rubbed a dusty coffee pot and a genie popped out to tell me that the standard deviation for the lifespan of all basketball shoes is 4.2 months. I polled 1225 people at the PEIF (before it closed) on how long their shoes lasted, and I got an average of 27 months.

(a) What is the 95% confidence interval for the population mean lifespan of a basketball shoe?

(b) What is the 99.7% confidence interval for the population mean lifespan of a basketball shoe?

(c) Draw appropriate shaded bell curves to show the difference between these.

In: Statistics and Probability

In the following problem, check that it is appropriate to use the normal approximation to the...

In the following problem, check that it is appropriate to use the normal approximation to the binomial. Then use the normal distribution to estimate the requested probabilities. Ocean fishing for billfish is very popular in the Cozumel region of Mexico. In the Cozumel region about 43% of strikes (while trolling) resulted in a catch. Suppose that on a given day a fleet of fishing boats got a total of 27 strikes. Find the following probabilities. (Round your answers to four decimal places.)

(a) 12 or fewer fish were caught Incorrect: Your answer is incorrect.

(b) 5 or more fish were caught

(c) between 5 and 12 fish were caught

In: Statistics and Probability

Another market researcher is trying to determine if exposure to a particular ad can change consumer’s...

Another market researcher is trying to determine if exposure to a particular ad can change consumer’s attitudes about a new product. He collects the data below.    Use hypothesis testing and the appropriate method to see if there is a difference between attitudes before and after exposure to the ad. (α = .02).

Subject

Pre-Exposure

Attitude (A1)

Post-Exposure

Attitudes (A2)

1 48 55
2 20 27
3 35 38
4 48 55
5 63 61
6 88 85
7 45 45
8 34 31
9 68 72
10 70 78

In: Statistics and Probability

The Pepsi Corporation wants to explore the relationship between the daily temperature in California and the...

The Pepsi Corporation wants to explore the relationship between the daily temperature in California and the quantity of soft drinks that it sells at Dadger’s Baseball Stadium. The average daily temperature for 12 randomly selected days and the quantity of soft drinks sold on each of these days are given below:

Average Daily Temperature

            70
            75
            80
            90
            93
            98
            72
            75
            75
            80
            90
            95

Quantity Sold (thousands)

           30
           28
           40
           52
           57
           54
           27
           38
           32
           46
           49
           51

Construct a 90% confidence interval for the quantity of soft drinks sold when the temperature is 750.

In: Statistics and Probability