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
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
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
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
(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
(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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