30 Dec Ethical Decision-Making Essay

Ethical Decision-Making Essay

Throughout this course, you will participate in a variety of critical thinking exercises designed to engage you in evaluating and selecting appropriate quantitative models and methods. A key aspect of this process involves ethical considerations. In an essay of 750-1,000 words, explore ethical decision making and arrive at conclusions relevant to your industry and perspectives of Christian worldview.

How do ethical business practices influence the evaluation, selection, and application of an analytical, quantitative business model? How does the selection of an appropriate business model reflect ethical practice? Frame your ethical considerations from both a Christian worldview and business practice perspective.

What role do individuals and management play in ensuring the appropriate business model is chosen, used, and evaluated for effectiveness?

Support your assertions with evidence from the readings, external research, and the textbook.

Prepare the assignment according to the guidelines found in the APA Style Guide, located in the Student Success Center. An abstract is not required.

This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.

You are required to submit this assignment to Turnitin. Please refer to the directions in the Student Success Center.

Week 2 assignment

Decision Analysis Case Study: Valley of the Sun Reviews

For many of the remaining topics in BUS-660, assignments will be in the form of case studies. These case studies are designed to provide an opportunity to engage in that topic’s quantitative analysis method, as well as demonstrate critical thinking and appropriate professional communication.

Review “Decision Analysis Case Study: Valley of the Sun Reviews” for this topic’s case study, a proposal to change the faculty performance review process at Valley of the Sun Academy (VSA).

Based on the information presented in the case study, create a decision tree or Excel-based analysis to determine the most appropriate recommendation.

In a 500-750-word report to VSA’s Human Resources department and the chief financial officer, explain your approach and the rationale for this method. Evaluate both outcomes and how they would be applied to this decision. Conclude your report with your recommendation for the review process VSA should adopt.

Submit your Excel-based analysis or decision tree with your report.

Prepare the assignment according to the guidelines found in the APA Style Guide, located in the Student Success Center. An abstract is not required.

This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.

You are required to submit this assignment to Turnitin. Please refer to the directions in the Student Success Center.

Decision Analysis Case Study:

Valley of the Sun Reviews

Valley of the Sun Academy (VSA) is an online school specializing in GED programs for the Phoenix area. Valley of the Sun Academy enrolls 813 students and has a part-time faculty pool of 65 online instructors.

Online faculty are reviewed annually and provided with feedback about their facilitation techniques, content expertise, engagement, and classroom management. If necessary, remediation and additional support are provided by the Faculty Advisory Board (FAB). The online faculty reviews are one factor used to determine overall performance, teaching status, and potential performance appraisals.

Recently, the FAB submitted a proposal for a new approach for the next fiscal year, the Peer Faculty Performance Review (PFPR). Human Resources (HR) and the school’s chief financial officer are evaluating the suggestion against the current design, described by VSA’s director. Both review processes are outlined below.

Current Design

Valley of the Sun Academy uses an external firm, TeachBest Consulting, to conduct annual reviews for online faculty. The review team is composed of faculty members at other online institutions, including universities and high schools. Valley of the Sun Academy faculty are not part of the review process, and TeachBest Consulting handles hiring and training internally. Valley of the Sun Academy’s HR department assigns completed courses to review, and VSA’s Technical Support team is responsible for providing access.

Once completed, the TeachBest consultant submits the review form to VSA’s HR department, and HR submits a payment for each review. In addition, VSA has an annual contract with TeachBest Consulting.

The overall contract is $2,500/year. If VSA’s enrollment reaches 1,000 or more students or their faculty pool expands to 75 or more instructors, the contract amount will increase to $5,000/year. There is a 75% chance the student enrollment will reach 1,000 students within the next 18 months and a 25% chance enrollment will not increase. During the next nine months, Human Resources anticipates hiring at least six math instructors.

Individual reviewers are paid $75 for each review. Reviews are conducted in March, July, and November, with all faculty reviewed by December 1.

Valley of the Sun Academy is responsible for disseminating the results of the review to faculty members. If questions arise about review results, the FAB is responsible for verifying the review and responding to the instructor. Periodically, the Faculty Advisory Board finds fault with the initial review and follow-up must be scheduled. Each year, about 5% of the initial reviews are found to be inaccurate and new reviews must be scheduled. Valley of the Sun Academy pays a discounted price of $50 for each follow-up review.

Peer Faculty Performance Review (PFPR) Proposal

The FAB proposes to conduct faculty reviews in-house and no longer contract TeachBest Consulting. Human Resources will review faculty files and invite the top three performing instructors in four disciplines (Literacy and Communication, Social Sciences, Math, and Science and Technology) to join the PFPR committee.

Initial responsibilities will involve creating a new review form and conducting a norming session for consistency. There will be ongoing technology fees of $20/month for each reviewer, to ensure access to create and complete the review forms. There will also be an initial cost to set up the norming session. The Faculty Advisory Board recommends one of three options:

1. A $500 session that can be scheduled at any time with TeachBest Consulting.

2. A $750 session offered monthly by an external employee development firm.

3. A session designed by VSA’s HR and instructional design specialists, which would be free to attend but would require internal time and labor costs; HR anticipates a start of two months from implementation would prevent interrupting normal business practices.

Because the responsibilities are not included in current faculty contracts, FAB recommends stipends of $50 for each review completed. With the new internal PFPR process, FAB anticipates faculty reviews would no longer be overturned and there would not be a need to conduct secondary reviews. Additionally, FAB expects reviews to move to a 9-month rolling cycle rather than once every academic year.

Week 3 assignment

Simple Regression Models Case Study: Mystery Shoppers

Review “Simple Regression Models Case Study: Mystery Shoppers” for this topic’s case study, a request to evaluate consignment stores from mystery shopper data.

Based on the information presented in the case study, create a regression model to determine the most appropriate recommendation.

Prepare a 250-500-word response to Mrs. Turner’s questions about predicting final scores, statistical significance, and whether a store location should be closed based on the data provided. Explain your approach and the rationale for this method. Evaluate the outcomes of your regression model and the responses to Mrs. Turner’s questions.

Submit a copy of the Excel spreadsheet file you used to design your regression model and to determine statistical significance.

Note: Students should use Excel’s regression option to perform the regression.

Use an Excel spreadsheet file for the calculations and explanations. Cells should contain the formulas (i.e., if a formula was used to calculate the entry in that cell).

Mac users can use StatPlus:mac LE, free of charge, from AnalystSoft.

StatPlus:mac LE can be used with Excel 2011 to perform statistical functions.

Go to the AnalystSoft website and follow the installation instructions: http://www.analystsoft.com/en/products/statplusmacle/.

Once installed, Apple users can use StatPlus:mac LE to complete homework problems that require the use of Excel’s data analysis statistical functions.

Prepare the written portion of this assignment according to the guidelines found in the APA Style Guide, located in the Student Success Center. An abstract is not required.

This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.

You are not required to submit this assignment to Turnitin.

Simple Regression Models Case Study: Mystery Shoppers

Chic Sales is a high-end consignment store with several locations in the metro area. The company noticed a decrease in sales over the last fiscal year. Research indicated customer satisfaction had decreased and the owner, Pat Turner, decided to create a mystery shopper program.

The mystery shopper program lasted over a 6-month period, employing several loyal and new customers assigned to each location. Surveys were on a 100-point scale and involved categories such as “Staff Attitude,” “Store Cleanliness,” “Product Availability,” and “Display(s) Appeal.”

After the mystery shopper period concludes, Mrs. Turner sends you the following e-mail:

From: Pat Turner

Sent: Thursday, July 7, 2016 8:57 a.m.

Subject: Mystery Data Shopper Stats and Store Performance?

Good morning! Welcome back from vacation ? I hope you had a wonderful Fourth of July.

The last mystery shopper surveys came in and I have the final numbers. I am interested in whether there is a way to predict the final average based on the initial survey score. Also, is there a statistically significant relationship between how stores initially performed and what the overall average is?

The initial survey score and the final average data for all seven store locations is in the table below:

Store 1 2 3 4 5 6 7

Initial Survey Score 83 97 84 72 85 64 93

Final Average 78 98 92 75 88 70 93

Also, how good is the relationship between Initial Survey Score and the Final Average? Could I use an Initial Survey Score to predict a Final Average? In fact, could I predict a Final Average if I have an Initial Survey Score of 90?

If you could have this to me before the weekend, that would be great.

Thanks so much!

Pat Turner, Owner

Chic Sales Consignment, LLC

Week 4 assignment

Multiple Regression Models Case Study: Web Video on Demand

Review “Multiple Regression Models Case Study: Web Video on Demand” for this topic’s case study, predicting advertising sales for an Internet video-on-demand streaming service.

After developing Regression Model A and Regression Model B, prepare a 250-500-word executive summary of your findings. Explain your approach and evaluate the outcomes of your regression models.

Submit a copy of the Excel spreadsheet file you used to design your regression model and to determine statistical significance.

Note: Students should use Excel’s regression option to perform the regression.

Use an Excel spreadsheet file for the calculations and explanations. Cells should contain the formulas (i.e., if a formula was used to calculate the entry in that cell). Students are highly encouraged to use the “Multiple Regression Dataset” Excel resource to complete this assignment.

Mac users can use StatPlus:mac LE, free of charge, from AnalystSoft.

Prepare the written portion of this assignment according to the guidelines found in the APA Style Guide, located in the Student Success Center. An abstract is not required.

This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.

You are required to submit this assignment to Turnitin. Please refer to the directions in the Student Success Center.

Multiple Regression Models Case Study: Web Video on Demand

Web Video on Demand (WVOD) is an Internet video-on-demand streaming service. The company offers a subscription service for $5.99/month, which includes access to all programming and 30-second commercial intervals.

In the last year, the company has recently begun producing its own programming, including 30-, 60-, and 120-minute television shows, specials, and films. Programming has been developed for teen audiences as well as adults.

The following data represent the amount of money brought in through advertising sales, the average number of viewers, length of the program, and the average viewer age per program.

Advertising Sales

($) Average # of Viewers

(Millions) Length of Program (Minutes) Average Viewer Age

(Years)

28,000 10.1 30 30

25,500 11.4 30 25

31,000 19.9 60 30

29,000 13.6 60 38

20,500 12.5 60 20

14,500 3.5 30 15

27,000 15.1 60 24

23,500 3.7 30 17

19,500 4.3 30 19

23,000 12.2 120 45

18,000 5.1 120 19

29,500 15.9 60 28

30,000 16.8 120 31

25,000 8.5 120 58

22,500 9.1 30 43

The WVOD executives are in the process of evaluating a partnership with several independent filmmakers to fund and distribute socially conscious and diverse programming. The executives have asked for regression models to be developed based on specific needs. The three regression model requests and programming details are included below.

The WVOD executives would like to see a regression model that predicts the amount of advertising sales based on the number of viewers and the length of the program. Develop this regression model (“Regression Model A”). Web Video on Demand would like to acquire a 60-minute documentary special about social media and bullying. The special is aimed at teen viewers and is estimated to bring in 3.2 million viewers. Based on the regression model, predict the advertising sales that could be generated by the special.

The WVOD executives would also like to see a regression model that predicts the amount of advertising sales based on the number of viewers, the length of the program, and the average viewer age. Develop this regression model (“Regression Model B”). Web Video on Demand may acquire a 2-hour film that was a hit with critics and audiences at several international film festivals. Initial customer surveys indicate that the film could bring in 14.1 viewers and the average viewer age would be 32. Use this information to predict the advertising sales.

Week 5 assignment

Forecasting Case Study: New Business Planning

Access the “Entrepreneurship and the U.S. Economy” page of the Bureau of Labor Statistics website (https://www.bls.gov/bdm/entrepreneurship/entrepreneurship.htm) and complete this forecasting assignment according to the directions provided in the “Forecasting Case Study: New Business Planning” resource.

Use an Excel spreadsheet file for the calculations and explanations. Cells should contain the formulas (if a formula was used to calculate the entry in that cell). Students are highly encouraged to use the Excel resource, “Forecasting Template,” to complete this assignment.

Mac users can use StatPlus:mac LE, free of charge, from AnalystSoft.

Prepare the assignment according to the guidelines found in the APA Style Guide, located in the Student Success Center. An abstract is not required.

This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.

You are not required to submit this assignment to Turnitin.

Forecasting Case Study: New Business Planning

Important Note: Students must access the “Entrepreneurship and the U.S. Economy” page of the Bureau of Labor Statistics website in order to complete this assignment.

Scenario

The generation of new business start-up is vital to the growth of the economy as it builds new jobs and creates new opportunities for the community. The Bureau of Labor Statistics tracks new business development and jobs created on the website for the United States Department of Labor. You have been tasked with forecasting economic growth and decline patterns for new businesses in the United States.

Forecasting

Access the “Entrepreneurship and the U.S. Economy” page of the Bureau of Labor Statistics website. Under the “Business establishment age” heading, the first chart reviews new businesses less than 1 year old during the March 1994 to March 2015 period. Click on the [Chart data] link below the chart:

Once the chart data window opens, you will see the number of establishments that are less than 1 year old for each year during this period:

Using the five most recent years and the “Forecasting Template” spreadsheet provided, complete the forecasts for the next two periods and provide updated Totals and Average Bias, median absolute deviation (MAD), mean squared error (MSE), and mean absolute percentage error (MAPE) for all four charts. Provide a Summary Page in Excel with a 500-750 word report on the analysis completed by the forecasting models. Include review of error, recommendations on the best forecasting model to use, and analysis of the business trend data for new business startup in the United States.

.png” alt=”Text box: enter the past demands in the data area”>

Forecasting

Moving averages – 2 period moving average

Num pds

3

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Data

Forecasts and Error Analysis

Period

Demand

Forecast

Error

Absolute

Squared

Abs Pct Err

Period 1

38

Period 2

40

Period 3

41

39

2

2

4

04.88%

Period 4

37

40.5

-3.5

3.5

12.25

09.46%

Period 5

45

39

6

6

36

13.33%

Total

4.5

11.5

52.25

27.67%

Average

1.5

3.833333

17.41667

09.22%

before forecast

Bias

MAD

MSE

MAPE

Period 6

50

47.5

2.5

2.5

6.25

05.00%

Period 7

44

Average

after forecast period 6

Bias

MAD

MSE

MAPE

.png”>

.png” alt=”Text box: enter the past demands in the data area”>

Forecasting

Moving averages – 3 period moving average

Num pds

3

.png”>

Data

Forecasts and Error Analysis

Period

Demand

Forecast

Error

Absolute

Squared

Abs Pct Err

Period 1

38

Period 2

40

Period 3

41

Period 4

37

39.66667

-2.66667

2.666667

7.111111

07.21%

Period 5

45

39.33333

5.666667

5.666667

32.11111

12.59%

Total

3

8.333333

39.22222

19.80%

Average

1.5

4.166667

19.61111

09.90%

Bias

MAD

MSE

MAPE

Period 6

50

44

6

6

36

12.00%

Period 7

44

Average

after forecast period 6

Bias

MAD

MSE

MAPE

.png”>

.png” alt=”Text box: enter alpha (between 0 and 1), enter the past demands in the shaded column then enter a starting forecast. if the starting forecast is not in the first period then delete the error analysis for all rows above the starting forecast.”>

Forecasting

Exponential smoothing

Alpha

0.3

Data

Forecasts and Error Analysis

Period

Demand

Forecast

Error

Absolute

Squared

Abs Pct Err

Period 1

38

38

0

0

0

0.00%

Period 2

40

38

2

2

4

5.00%

Period 3

41

38.6

2.4

2.4

5.76

5.85%

Period 4

37

39.32

-2.32

2.32

5.3824

6.27%

Period 5

45

38.624

6.376

6.376

40.65338

14.17%

Total

8.456

13.096

55.79578

31.29%

Average

1.6912

2.6192

11.15916

06.26%

Before forecast

Bias

MAD

MSE

MAPE

SE

4.312608

Period 6

50

40.5368

9.4632

9.4632

89.55215

18.93%

Period 7

44

Average

after forecast period 6

Bias

MAD

MSE

MAPE

.png”>

.png” alt=”Text box: enter alpha and beta (between 0 and 1), enter the past demands in the shaded column then enter a starting forecast. if the starting forecast is not in the first period then delete the error analysis for all rows above the starting forecast.”>

Forecasting

Trend adjusted exponential smoothing

Alpha

0.3

Beta

0.7

Data

Forecasts and Error Analysis

Period

Demand

Smoothed Forecast, Ft

Smoothed Trend, Tt

Forecast Including Trend, FITt

Error

Absolute

Squared

Abs Pct Err

Period 1

38

38

38

0

0

0

00.00%

Period 2

40

38

0

38

2

2

4

05.00%

Period 3

41

38.6

0.42

39.02

1.98

1.98

3.9204

04.83%

Period 4

37

39.614

0.8358

40.4498

-3.4498

3.4498

11.90112

09.32%

Period 5

45

39.41486

0.111342

39.5262

5.473798

5.4738

29.96246

0.12164

Next period

41.16834

1.26084

42.42918

Total

6.003998

12.9036

49.78398

31.32%

41.16834

Average

1.2008

2.58072

9.956797

06.26%

Bias

MAD

MSE

MAPE

SE

4.073655

Next period

42.05093

0.617811

42.66874

Total

After forecast

Average

Bias

MAD

MSE

MAPE

SE

0

.png”>

Week 6 assignment

Introduction to Optimization Modeling Problem Set

Manually complete the following problems in the textbook:

Problem 7-14

Problem 7-15

Problem 7-18

Use an Excel spreadsheet file for the calculations and explanations. Cells should contain the formulas (i.e., if a formula was used to calculate the entry in that cell). Students are highly encouraged to use the “Optimization Modeling Problem Set” Excel resource to complete this assignment.

Mac users can use StatPlus:mac LE, free of charge, from AnalystSoft.

You are not required to submit this assignment to Turnitin.

Problem 7-14

Input the numbers in the beige cells

Write formulas to compute values (e.g. profit, resoure utilization) in blue cells

Solution

AC

FAN

Total

RHS

Maximize

0

WIRING TIME

0

<= DRILLING TIME 0 <= Problem 7-15 Input the numbers in the beige cells Write formulas to compute values (e.g. profit, resoure utilization) in blue cells Solution AC FAN Total RHS Maximize 0 WIRING TIME 0 <= DRILLING TIME 0 <= AC Demand 0 >=

Fan Demand

0

<= Problem 7-18 Input the numbers in the beige cells Write formulas to compute values (e.g. profit, resoure utilization) in blue cells Solution U G Total RHS Maximize 0 Undergrad 0 >=

Grad

0

>=

Total

0

>=

Week 7 assignment

Benchmark – Data Analysis Case Study

Review “Benchmark Assignment – Data Analysis Case Study” and “Benchmark Assignment – Data Analysis Case Study Data” for this topic’s case study, evaluating operations for a local restaurant.

Although your friend and restauranteur Michael Tanaglia offered to go over your findings in person, you believe it would be appropriate to also prepare a report and document your findings in writing. In a 1,000-1,250-word report, explain your approach for each evaluation and the rationale for the methods you used. Include any recommendations based on customer satisfaction, forecasting, and staff scheduling data.

Use an Excel spreadsheet file for the calculations and explanations. Cells should contain the formulas (i.e., if a formula was used to calculate the entry in that cell). Students are highly encouraged to use the “Benchmark Assignment – Data Analysis Case Study Template” and “Benchmark Assignment – Data Analysis Case Study Linear Programming Template” to complete this assignment.

Mac users can use StatPlus:mac LE, free of charge, from AnalystSoft.

Prepare the assignment according to the guidelines found in the APA Style Guide, located in the Student Success Center. An abstract is not required.

This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.

You are required to submit this assignment to Turnitin. Please refer to the directions in the Student Success Center.

Benchmark Assignment – Data Analysis Case Study

The Cicero Italian Restaurant was founded by Anthony Tanaglia in 1947 in Cicero, Illinois, a suburb of Chicago. He built the business with his family from a small pizza and pasta restaurant to 10 locations in the Chicago area. Michael Tanaglia, Anthony’s grandson, moved to Arizona to escape the cold Chicago winters and opened a restaurant in the Chandler area. The Arizona restaurant gained momentum thanks to the Chicago-style pizza and quality Italian dishes. Anthony decided to expand operations in Arizona, adding a second location in Glendale. The Glendale location was managed by Michael’s son Tony.

After a year of operations, Michael had some concerns with the Glendale location. Michael does not want his family’s business to fail, and he wants his grandfather’s legacy to last. Michael also understands how important an operational evaluation can be to identifying the strengths and weaknesses of a business. Michael confides his concerns to you and asks if you will do him a favor and use your quantitative analytic expertise to help him evaluate the Glendale location’s operations in three key areas: customer satisfaction, customer forecasting, and staff scheduling. As his friend, you agree – though his offer to treat you to the large pizza of your choice did not hurt.

First Evaluation

The first evaluation required an understanding of the factors that contribute to customer satisfaction and spending. Refer to the data Michael provided in the Excel spreadsheet “Benchmark Assignment – Data Analysis Case Study Data.” Identify which variables are significant to predicting overall satisfaction. Develop and interpret the prediction equation and the coefficient of determination. Based upon the data in this evaluation, what areas should Michael and Tony Tanaglia focus on to improve customer satisfaction?

Second Evaluation

The second evaluation requires a forecast of customers based upon demand. Michael reviewed data for the previous 11 months in an attempt to better forecast restaurant customer volume.

MONTH # OF CUSTOMERS

January 650

February 725

March 850

April 825

May 865

June 915

July 900

August 930

September 950

October 899

November 935

December ?

Which method should the business owner use to yield the lowest amount of error and what would be the forecast for December? Refer to the Excel spreadsheet “Benchmark Assignment – Data Analysis Case Study Template.”

Third Evaluation

The third evaluation concerns staff scheduling. Some of the customers have complained that service is slow. The restaurant is open from 11:00 a.m. to midnight every day of the week. Tony divided the workday into five shifts. The table below shows the minimum number of workers needed during the five shifts of time into which the workday is divided.

Shift Time # of Staff Required

1 10:00 a.m. – 1:00 p.m. 3

2 1:00 p.m. – 4:00 p.m. 4

3 4:00 p.m. – 7:00 p.m. 6

4 7:00 p.m. – 10:00 p.m. 7

5 10:00 p.m. – 1:00 a.m. 4

The owners must find the right number of staff to report at each start time to ensure that there is sufficient coverage. The organization is trying to keep costs low and balance the number of staff with the size of the restaurant, so the total number of workers is constrained to 15.

Based on these factors, recommend the staff for each shift to accommodate the minimum requirements for customer service. Refer to the Excel spreadsheet “Benchmark Assignment – Data Analysis Case Study Linear Programming Template.”

Week 8 assignment

Simulation Case Study: Phoenix Boutique Hotel Group

Review “Simulation Case Study: Phoenix Boutique Hotel Group” for this topic’s case study, in which you provide guidance to Phoenix Boutique Hotel Group (PBHG) founder Bree Bristowe.

In addition to creating a simulation model, prepare a 500-750-word recommendation for Bristowe’s best course of action. Explain your model and the rationale for your recommendations.

Use an Excel spreadsheet file for the calculations and explanations. Cells should contain the formulas (i.e., if a formula was used to calculate the entry in that cell). Students are highly encouraged to use the “Simulation Case Study: Phoenix Boutique Hotel Group Template” Excel resource to complete this assignment.

Mac users can use StatPlus:mac LE, free of charge, from AnalystSoft.

Prepare the assignment according to the guidelines found in the APA Style Guide, located in the Student Success Center. An abstract is not required.

This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion

You are required to submit this assignment to Turnitin. Please refer to the directions in the Student Success Center.

Simulation Case Study:

Phoenix Boutique Hotel Group

Phoenix Boutique Hotel Group (PBHG) was founded in 2007 by Bree Bristowe. Having worked for several luxury resorts, Bristowe decided to pursue her dream of owning and operating a boutique hotel. Her hotel, which she called PHX, was located in an area that included several high-end resorts and business hotels. PHX filled a niche market for “modern travelers looking for excellent service and contemporary design without the frills.” Since opening PHX, Bristowe has invested, purchased, or renovated three other small hotels in the Phoenix metropolitan area: Canyon Inn PHX, PHX B&B, and The PHX Bungalows.

One of the customer service enhancements Bristowe has implemented is a centralized, toll-free reservation system. Although many customers book specific hotels online, the phone reservation system enables PBHG to find the best reservation match at all properties. It has been an excellent option for those customers who have preferences regarding the type of room, amenity options, and the best price across the four hotel locations.

Currently, three agents are on staff for the 6 a.m. to 2 p.m. call shift. The time between calls during this shift is represented in Table 1. The time to process reservation requests during this shift is in Table 2.

Table 1: Incoming Call Distribution

Time Between Calls (Minutes) Probability

1 0.13

2 0.23

3 0.27

4 0.19

5 0.15

6 0.09

Table 2: Service Time Distribution

Time to Process Customer Inquiries (Minutes) Probability

1 0.19

2 0.17

3 0.16

4 0.15

5 0.11

6 0.08

7 0.03

Bristowe wants to ensure customers are not on hold for longer than 2 minutes. She is debating hiring additional staff for this shift based on the available data. Additionally, Bristowe and PBHG will soon be featured in a national travel magazine with a circulation of over a million subscriptions. Bristowe is worried that the current operators may not be able to handle the increase in reservations. The projected increase for call distribution is represented in Table 3.

Table 3: Incoming Call Distribution

Time Between Calls (Minutes) Probability

1 0.26

2 0.27

3 0.24

4 0.14

5 0.11

6 0.06

Bristowe has asked for your advice in evaluating the current phone reservation system. Create a simulation model to investigate her concerns. Make recommendations about the reservation agents.