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(solution) Hello, I desperately need the case write up completed today and


Hello,

I desperately need the case write up completed today and the excel file No.2. 

The excel file #1 is only for reference. Our professor says it does not need to be completed.




QNT 5160, Fall 2016 Semester

 

Individual Case Assignment: Cutting Edge

 

This case was adapted from Hiller, Frederick S. & Mark S. Hillier (2014 ). Introduction to Management Science:

 

A Modeling and Case Studies Approach with Spreadsheets, 5th ed., McGraw-Hill/Irwin, pp 429-432.

 

Mark Lawrence has been pursuing a vision for more than two years. This pursuit began when he became

 

frustrated in his role as director of Human Resources at Cutting Edge, a large company manufacturing

 

computers and computer peripherals. At that time the Human Resources Department under his

 

direction provided records and benefits administration to the 60,000 Cutting Edge employees throughout

 

the United States, and 35 separate records and benefits administration centers existed across the

 

country. Employees contact these records and benefits centers to obtain information about dental plans

 

and stock options, change tax forms and personal information, and process leaves of absence and

 

retirements. The decentralization of these administration centers caused numerous headaches for Mark.

 

He had to deal with employee complaints often since each center interpreted company policies

 

differently ? communicating inconsistent and sometimes inaccurate answers to employees. His

 

department also suffered high operating costs since operating 35 separate centers created inefficiency.

 

His vision? To centralize records and benefits administration by establishing one administration center.

 

This centralized records and benefits administration center would perform two distinct functions: data

 

management and customer service. The data management function would include updating employee

 

records after performance reviews and maintaining the human resource management system. The

 

customer service function would include establishing a call center to answer employee questions

 

concerning records and benefits and to process records and benefits changes over the phone.

 

One year after proposing his vision to management, Mark received the go-ahead from Cutting Edge

 

corporate headquarters. He prepared his ?to do? list ? specifying computer and phone systems

 

requirements, installing hardware and software, integrating data from the 35 separate administration

 

centers, standardizing record-keeping and response procedures, and staffing the administration center.

 

Mark delegated the systems requirements, installation, and integration jobs to a competent group of

 

technology specialists. He took on the responsibility of standardizing procedures and staffing the

 

administration center.

 

Mark had spent many years in human resources and therefore had little problem with standardizing

 

record-keeping and response procedures. He encountered trouble in determining the number of

 

representatives needed to staff the center, however. He was particularly worried about staffing the call

 

1 center since the representatives answering phones interact directly with customers ? the 60,000 Cutting

 

Edge employees. The customer service representatives would receive extensive training so that they

 

would know the records and benefits policies backwards and forwards ? enabling them to answer

 

questions accurately and process changes efficiently. Overstaffing would cause Mark to suffer the high

 

costs of training unneeded representatives and paying the surplus representatives the high salaries that

 

go along with such an intense job. Understaffing would cause Mark to continue to suffer the headaches

 

from customer complaints ? something he definitely wanted to avoid.

 

The number of customer service representatives Mark needed to hire depended on the number of calls

 

that the records and benefits call center would receive. Mark therefore needed to forecast the number

 

of calls that the new centralized center would receive. He approached the forecasting problem by using

 

judgmental forecasting. He studied data from one of the 35 decentralized administration centers and

 

learned that the decentralized center had serviced 15,000 customers and had received 2,000 calls per

 

month. He concluded that since the new centralized center would service four times the number of

 

customers ? 60,000 customers ? it would receive four times the number of calls ? 8,000 calls per month.

 

Mark slowly checked off the items on his ?to do? list, and the centralized records and benefits center

 

opened one year after Mark had received the go-ahead from corporate headquarters.

 

Now, after operating the new center for 13 weeks, Mark?s call center forecasts are proving to be terribly

 

inaccurate. The number of calls the center receives is roughly three times as large as the 8,000 calls per

 

month that Mark had forecasted. Because of demand overload, the call center is slowly going to hell in a

 

handbasket. Customers calling the center must wait an average of five minutes before speaking to a

 

representative, and Mark is receiving numerous complaints. At the same time, the customer service

 

representatives are unhappy and on the verge of quitting because of the stress created by the demand

 

overload. Even corporate headquarters has become aware of the staff and service inadequacies, and

 

executives have been breathing down Mark?s neck demanding improvements.

 

Answer Questions 1a through 1c below:

 

Question 1a: Define a problem statement which reflects the challenge facing Mark as he planned for the

 

opening of the new center.

 

The challenge Mark faced as he planned for the opening of the new call center was using judgmental

 

forecasting to predict the number of calls the new centralized center would receive in order to determine

 

the accurate amount of representatives needed to staff the center which would prevent him from

 

overstaffing which could lead to a high cost of unnecessary training and paying a surplus in high

 

representative salaries and to also prevent him from understaffing which would lead to more customer

 

complaints. 2 Question 1b: Why was Mark?s initial forecast of call volume so far off? What could have been the reasons

 

for this?

 

Marks initial forecast of call volume was so far off because he was using the judgmental forecasting

 

method salesforce composite. This method will only be accurate if the estimate of sales in the region is

 

correct, however in this case his estimate was way off. Mark used data from one of the decentralized

 

administration centers and concluded that based on their numbers, his call center would only receive 8,000

 

calls per month. The actual amount of call received turned out to be roughly three times larger, causing a

 

demand overload. Mark also did not think to use historical and utilize a statistical forecasting method which

 

could have provided a more accurate measure. He also did not take into account any seasonal demand

 

patterns which could have an effect on the forecast. Question 1c: What could Mark have done differently to improve his initial forecast?

 

Mark could have used a statistical forecasting method which uses actual data rather than relying solely

 

on judgmental factors. He could have used his operational data to forecast and also take into account

 

seasonal factors. 3 Mark needed help, and he approached Harry, a corporate analyst, to forecast demand for the call center

 

more accurately.

 

Luckily, when Mark first established the call center, he realized the importance of keeping operational

 

data, and he provided Harry with the number of calls received on each day of the week over the last 13

 

weeks. The data (refer to Cutting Edge Student File No. 1) begins in week 44 of the last year (2012) and

 

continues to week 5 of the current year (2013).

 

Mark indicates that the days where no calls were received were holidays.

 

As a start, Harry used the data from the past 13 weeks and applied five different time-series forecasting

 

methods in preparing a trial forecast of the call volume for each day of the upcoming week (Week 6). He

 

provided a different forecast for each day of the week by treating the forecast for a single day as being

 

the actual call volume on that day.

 

From plotting the data, Harry could see that demand follows ?seasonal? patterns within the week. For

 

example, more employees call at the beginning of the week when they are fresh and productive than at

 

the end of the week when they are planning for the weekend. Therefore, Mark prepared and used

 

seasonally adjusted call volumes for the past 13 weeks. After Week 6 ended, Harry compared the five

 

forecasts with the actual volumes and calculated the Mean Absolute Deviation (MAD) values for each

 

method. The result of Harry?s work is summarized below: Cutting Edge

 

Week 6 Forecast vs. Actual Daily Call Volume

 

FORECAST FORECAST FORECAST FORECAST FORECAST ACTUAL Day Last Value Averaging Moving

 

Average (5

 

days) Exponential

 

Smoothing

 

(alpha=0.1) Exponential

 

Smoothing

 

(alpha=0.5) Actual Call

 

Volume Mon 795 982 735 802 701 723 Tue 774 946 668 768 689 698 Wed 809 1037 737 837 763 534 Thu 947 1227 833 962 773 578 4 Fri 759 1032 676 782 572 Mean

 

Absolute

 

Deviation

 

(MAD) 171 399 104 184 116 697 Answer Questions 2a through 2e below:

 

Question 2: Describe the details of each forecasting method used by Harry and explain its accuracy (MAD

 

value) in comparison with the accuracy of the other methods. (Hint: In answering this question, it is

 

helpful to review a time-series plot of the 13 weeks of data.)

 

2a) Last Value 2b) Averaging 2c) Moving Average (5 days) 2d) Exponential Smoothing (alpha = 0.1) 2e) Exponential Smoothing (alpha = 0.5) 5 After many months of work and with Harry?s help, Mark has been able to stabilize the call center

 

operation. Mark now has a better handle on how to forecast the daily call demand and he is able to

 

prepare effective weekly staffing schedules for handling the daily variation in volume.

 

However, Mark is still experiencing difficulty in forecasting the volume from month to month. Cutting

 

Edge has been very active in acquiring new companies while, at the same time, selling off portions of

 

their existing business. Mark believes that this activity is causing fluctuations in call volume because it is

 

affecting the employee head count of Cutting Edge.

 

Mark has assembled monthly data for call volume and head count for the past 18 months (refer to

 

Cutting Edge Student File No. 2). Mark also suspects that there are other factors which may be affecting

 

the call volume, and he has noted these factors on the attached spreadsheet. Based on the upcoming

 

acquisition of Cutter Corp on 7/1/2015, the forecast of head count for July 2015 is 77,000. Answer Questions 3a through 3d below:

 

Question 3a: Prepare a forecast of call volume for July 2015 by applying Exponential Smoothing (with

 

alpha = 0.5) to the prior 18 months of data. Use the appropriate Excel template from the Hillier text to

 

prepare your forecast and assume that initial call volume is 24,000. Show your forecast below and attach

 

the completed template.

 

Call Volume Forecast for July 2015 (Exponential Smoothing, alpha=0.5): _________________ Question 3b: Apply Linear Regression to predict call volume from head count using the appropriate Excel

 

template. Show your forecast below and attach the completed Excel template.

 

Call Volume Forecast for July 2015 (Causal Forecasting based on head count): _________________ Question 3c: Calculate the Mean absolute deviation value of the Exponential Smoothing model (Question

 

3a) and the Average Estimation Error of the Linear Regression model (Question 3b). Explain the

 

difference between these two values.

 

Mean absolute deviation of Exponential Smoothing model, alpha=0.5: ______________________ Average

 

Estimation Error for Causal Forecasting model based on headcount: __________________ Explanation of

 

the difference in values:

 

6 Question 3d: Considering your answers to Questions 3a, 3b and 3c and all the factors that have been

 

described above, prepare your best forecast for July 2015. Show your forecast value below and explain

 

and justify how you came up with this forecast.

 

Call Volume Forecast for July 2015 (My forecast): _________________

 

Explanation and Justification of Your Method: 7

 


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