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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.

SOLUTION

Part 1: Pre-Opening Forecasting Analysis (2 Years Prior)

Question 1a. Problem Statement

The primary problem facing Sergio (Corey) was determining an accurate forecast of call volume for a newly centralized call center in order to staff the center efficiently, balancing the high costs of overstaffing and training against the service failures and employee burnout caused by understaffing.

Question 1b. Why the Initial Forecast Was So Inaccurate

Sergio’s initial forecast was inaccurate because it relied on judgmental forecasting using data from only one decentralized center and assumed a linear scaling relationship between number of employees served and call volume. This approach ignored several critical factors, including pent-up demand created by inconsistent service across decentralized centers, behavioral changes caused by centralization, and the likelihood that employees would use the new call center more frequently once access was simplified. In addition, no historical trend analysis or seasonality was considered.

Question 1c. How the Initial Forecast Could Have Been Improved

The forecast could have been improved by aggregating historical data from multiple decentralized centers, incorporating time-series forecasting methods, adjusting for weekly seasonality, and conducting pilot testing prior to full implementation. Using multiple forecasting models and validating them against historical patterns would have reduced bias and improved accuracy.

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Part 2: Interpretation of Forecasting Models (Weeks 1–13)

Question 2a. Last Value Methods

The Last Value method uses the most recent observed value as the forecast for the next period. Its MAD of 202.7 indicates relatively poor accuracy.
The Last Value with Seasonal Adjustment incorporates weekday seasonality and significantly improves accuracy, reducing MAD to 158.8. The difference between the two lies in the seasonal method’s ability to account for predictable within-week demand patterns, which are clearly present in the data.

Question 2b. Averaging Method

The Averaging method forecasts demand by calculating the mean of all prior observations. This method produced the highest MAD (299.0), making it the least accurate. Its poor performance is due to its inability to adapt to recent trends or seasonal fluctuations, causing it to overestimate demand on low-volume days and underestimate it on high-volume days.

Question 2c. Moving Average (5-Day) Method

The 5-day moving average uses only the most recent five observations, allowing it to respond more quickly to changes in demand. Its MAD of 221.4 shows modest improvement over simple averaging but worse performance than seasonal and exponential smoothing models. This method still struggles with systematic weekly seasonality.

Question 2d. Exponential Smoothing Models

Exponential smoothing assigns more weight to recent observations. The low smoothing constant model produced an MAD of 224.3, while the high smoothing constant model yielded the lowest MAD overall at 157.0, making it the most accurate method tested.
The difference between the two lies in responsiveness: the high-alpha model reacts more quickly to recent changes, which is beneficial in a dynamic call-center environment with fluctuating demand.

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Part 3: Recommendations to Sergio

Question 3. Forecasting Recommendation

Based on the analysis, Sergio should adopt exponential smoothing with a higher smoothing constant or last value forecasting with seasonal adjustment for daily call volume forecasting. These methods demonstrated the lowest MAD values and best captured both recent trends and within-week seasonality. Implementing these models will improve staffing accuracy, reduce employee burnout, and enhance customer service levels. Sergio should also regularly re-evaluate model performance as organizational changes (such as acquisitions) continue to affect call volume.

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