First read the “Forecasting Interpretation Assignment Instructions” file here: Forecasting Interpretation Assignment Instructions.docx Download Forecasting Interpretation Assignment Instructions.docx.
The forecasting models have already been run. The goal of this project is to interpret their output.
Study the case Case Study for Individual Project.docx Download Case Study for Individual Project.docxand
Accompanying models (already run) Student File No. 1, Solution.xlsx Download Student File No. 1, Solution.xlsx.
· The Part 1 questions refer to the 2 years leading up to the opening of the new call center.
· Part 2 questions refer to the first 13 weeks of operation after opening the call center.
· Part 3 interpret the output from Part 2 and provide recommendations.
For this week (only), you do NOT need a full analytical report.
Follow APA 6 style to prepare the report and include the NSU Cover Sheet.
Here is the Individual Project Grading Rubric.docx Download Individual Project Grading Rubric.docx.
Individual Forecasting Interpretation Instructions
Instructions
This is an individual assignment and therefore must be completed by the individual student without
outside assistance. In order to complete the assignment, first read the write-up for the “Know Knead”
case study. Then, answer the questions listed below for each part of the case.
Part 1 questions refer to the 2 years leading up to the opening of the new call center (described in the
case study).
Part 2 questions refer to staffing using weekly data based on the first 13 weeks of operation after
opening the call center.
“Know Knead Student File No. 1 Solution.xlsx”
Part 3 interpret the output from Part 2 and provide recommendations.
Calculations are provided for this assignment. You do not need to conduct your own.
Include cover page and appropriate references in APA format.
This is the first of 2 forecasting projects. Make sure to use Student File 1 which has daily data.
Grading
A total of 10 points is possible for this assignment. This includes the point values which are assigned to
each question (point values are noted next to each question below).
Part 1 (3 points):
Question 1a: Define a problem statement which reflects the challenge facing Segio as he planned for the
opening of the new center.
Question 1b: Why was Segio’s initial forecast of call volume so far off? What could have been the reasons
for this?
Question 1c: What could Corey have done differently to improve his initial forecast?
Part 2 (5 points):
In answering the Part 2 questions, you should download and refer to Student Data File No. 1 which
contains the historical data that was used in preparing the forecast results that are reported in Part 2 of
the case write-up document.
*Note that you do not have to prepare any forecasts in answering this question. Hint: it will be helpful
for you to review a time-series plot of the 13 weeks of data contained on Student Data File No. 1.
Question 2a: Describe the details of the Last Value method used by Marina and explain its accuracy
(MAD value) in comparison with the accuracy of the other methods. Explain how the two last value
methods are different from each other
Question 2b: Describe the details of the Averaging method used by Marina and explain its accuracy (MAD
value) in comparison with the accuracy of the other methods.
Question 2c: Describe the details of the Moving Average (5 days) method used by Marina and explain its
accuracy (MAD value) in comparison with the accuracy of the other methods.
Question 2d: Describe the details of the Exponential Smoothing method used by Marina. Compare the
accuracy of the two exponential smoothing models to each other and then to the other models. How and
why do these two smoothing models differ from each other?
Part 3 (2 points):
Question 3: Based on the analysis above, provide your recommendations to Segrio on daily call volume
forecasting to improve the scheduling of the call enter staff.
Know Knead
Corey Rubio 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 Know Knead, Ltd, a donut bakery and distribution
company. At that time the Human Resources Department under his direction provided records and
benefits administration for approximately 80,000 cases monthly 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 Corey. He had to deal with employee complaints
often since each center interpreted company policies differently – communicating inconsistent and
sometimes inaccurate answers to employees. isr 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, Corey received the go-ahead from Know Knead
corporate headquarters. He prepared a “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.
Corey 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.
Corey 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
center since the representatives answering phones interact directly with employees. The customer service
representatives would receive extensive training so that they would know the records and benefits
This case was adapted from Hiller, Frederick S. & Belinda S. Hillier (2014). Introduction to Management Science:
A Modeling and Case Studies Approach with Spreadsheets, 5th ed., McGraw-Hill/Irwin, pp 429-432. policies backwards and forwards – enabling them to answer questions accurately and process changes
efficiently. Overstaffing would cause Corey 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 Corey to continue to suffer the headaches from customer complaints –
something he definitely wants to avoid.
The number of customer service representatives Corey needed to hire depended on the number of calls
that the records and benefits call center would receive. Corey 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 20,000 monthly cases and had received 2,500 calls per
month. He concluded that since the new centralized center would service four times the number of
customers, it would receive four times the number of calls, 10,000 calls per month.
Corey slowly checked off the items on her “to do” list, and the centralized records and benefits center
opened one year after Corey had received the go-ahead from corporate headquarters.
Now, after operating the new center for 13 weeks, Corey’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 10,000 calls per
month that Corey 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 Corey 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 Corey’s neck demanding improvements.
Corey needed help, and he approached Marina, a corporate analyst, to forecast demand for the call center
more accurately.
Luckily, when Corey first established the call center, he realized the importance of keeping operational
data, and he provided Marina with the number of calls received on each day of the week over the last 13
weeks. The data (refer to Know Knead Student File No. 1) begins in week 44 of the last year (2024) and
continues to week 5 of the current year (2025).
Corey indicates that the days where no calls were received were holidays.
As a start, Marina 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). She
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, Marina 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, Corey prepared and usedseasonally adjusted call volumes for the past 13 weeks. After Week 6 ended, Marina compared the five
forecasts with the actual volumes and calculated the Mean Absolute Deviation (MAD) values for each
method. The result of Marina’s work is summarized below:
Know Knead
Week 6 Forecast vs. Actual Daily Call Volume
Forecast
Week Day
Actual
Value
Last
Value
Last
Value w
Seasona
l
Averagin
g
Moving
Averag
e
Exponentia
l
Smoothing
(lo)
Exponentia
l
Smoothing
(hi)
5 Mon 877 877 932 1,032 797 841 919
5 Tue 722 722 664 1,027 786 816 734
5 Wed 515 515 664 1,021 772 784 665
5 Thur 584 584 664 1,014 731 738 564
5 Fri 493 493 664 1,009 699 718 632
MAD 202.7 158.8 299.0 221.4 224.3 157.0
After many months of work and with Marina’s help, Corey has been able to stabilize the call center
operation. Corey 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, Corey is still experiencing difficulty in forecasting the volume from month to month. Know
Knead has been very active in acquiring new companies while, at the same time, selling off portions of
their existing business. Corey believes that this activity is causing fluctuations in call volume because it is
affecting the employee head count of Know Knead.
Corey has assembled monthly data for call volume and head count for the past 18 months (refer to Know
Knead Student File No. 2). Corey 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 Doughey’s ‘Nutz on 7/1/2025, the forecast of monthly cases for July 2025 is 95,000. seasonally adjusted call volumes for the past 13 weeks. After Week 6 ended, Marina compared the five
forecasts with the actual volumes and calculated the Mean Absolute Deviation (MAD) values for each
method. The result of Marina’s work is summarized below:
Know Knead
Week 6 Forecast vs. Actual Daily Call Volume
Forecast
Week Day
Actual
Value
Last
Value
Last
Value w
Seasona
l
Averagin
g
Moving
Averag
e
Exponentia
l
Smoothing
(lo)
Exponentia
l
Smoothing
(hi)
5 Mon 877 877 932 1,032 797 841 919
5 Tue 722 722 664 1,027 786 816 734
5 Wed 515 515 664 1,021 772 784 665
5 Thur 584 584 664 1,014 731 738 564
5 Fri 493 493 664 1,009 699 718 632
MAD 202.7 158.8 299.0 221.4 224.3 157.0
After many months of work and with Marina’s help, Corey has been able to stabilize the call center
operation. Corey 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, Corey is still experiencing difficulty in forecasting the volume from month to month. Know
Knead has been very active in acquiring new companies while, at the same time, selling off portions of
their existing business. Corey believes that this activity is causing fluctuations in call volume because it is
affecting the employee head count of Know Knead.
Corey has assembled monthly data for call volume and head count for the past 18 months (refer to Know
Knead Student File No. 2). Corey 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 Doughey’s ‘Nutz on 7/1/2025, the forecast of monthly cases for July 2025 is 95,000.