Executive Solution Overview
**files (9)** – ## Excelspreadsheet.docx – ## KnowKneadStudentFileNo.2.xlsx – ## Assignment3TP.docx – ## Last_Valuefig10.5withoutseasonality.xlsx – Moving_Averagefig10.7withoutseasonality.xlsx – Linear_Regressionfig10.167.xlsx – Seasonality_Factorsfig10.3.xlsx – CaseStudyforIndividualProject_Kn…
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**files (9)**
– ## Excelspreadsheet.docx
– ## KnowKneadStudentFileNo.2.xlsx
– ## Assignment3TP.docx
– ## Last_Valuefig10.5withoutseasonality.xlsx
– Moving_Averagefig10.7withoutseasonality.xlsx
– Linear_Regressionfig10.167.xlsx
– Seasonality_Factorsfig10.3.xlsx
– CaseStudyforIndividualProject_KnowKnead-1.docx
– Exponential_Season__Trendfig10.10.xlsx
**## Excelspreadsheet.docx**
Click on the method names below to download a sample Excel spreadsheet displaying each.
[Averaging](https://nsu.instructure.com/courses/2552606/files/153776599/download?wrap=1) [Download Averaging](https://nsu.instructure.com/courses/2552606/files/153776599/download?download_frd=1)
[Averaging with Seasonality](https://nsu.instructure.com/courses/2552606/files/153776942/download?wrap=1) [Download Averaging with Seasonality](https://nsu.instructure.com/courses/2552606/files/153776942/download?download_frd=1)
[Exponential Smoothing](https://nsu.instructure.com/courses/2552606/files/153776547?wrap=1) [Download Exponential Smoothing](https://nsu.instructure.com/courses/2552606/files/153776547/download?download_frd=1)
[Exponential Smoothing with Seasonality](https://nsu.instructure.com/courses/2552606/files/153776904/download?wrap=1) [Download Exponential Smoothing with Seasonality](https://nsu.instructure.com/courses/2552606/files/153776904/download?download_frd=1)
[Exponential Smoothing with Trend](https://nsu.instructure.com/courses/2552606/files/153776902/download?wrap=1) [Download Exponential Smoothing with Trend](https://nsu.instructure.com/courses/2552606/files/153776902/download?download_frd=1)
[Exponential Smoothing with Trend and Seasonality](https://nsu.instructure.com/courses/2552606/files/153776600/download?wrap=1) [Download Exponential Smoothing with Trend and Seasonality](https://nsu.instructure.com/courses/2552606/files/153776600/download?download_frd=1)
[Last Value](https://nsu.instructure.com/courses/2552606/files/153776661/download?wrap=1) [Download Last Value](https://nsu.instructure.com/courses/2552606/files/153776661/download?download_frd=1)
[Last Value with Seasonality](https://nsu.instructure.com/courses/2552606/files/153776915/download?wrap=1) [Download Last Value with Seasonality](https://nsu.instructure.com/courses/2552606/files/153776915/download?download_frd=1)
[Linear Regression](https://nsu.instructure.com/courses/2552606/files/153776545?wrap=1) [Download Linear Regression](https://nsu.instructure.com/courses/2552606/files/153776545/download?download_frd=1)
[Moving Average](https://nsu.instructure.com/courses/2552606/files/153776598/download?wrap=1) [Download Moving Average](https://nsu.instructure.com/courses/2552606/files/153776598/download?download_frd=1)
[Moving Average with Seasonality](https://nsu.instructure.com/courses/2552606/files/153776659/download?wrap=1) [Download Moving Average with Seasonality](https://nsu.instructure.com/courses/2552606/files/153776659/download?download_frd=1)
[Seasonality Factors](https://nsu.instructure.com/courses/2552606/files/153776745/download?wrap=1) [Download Seasonality Factors](https://nsu.instructure.com/courses/2552606/files/153776745/download?download_frd=1)
**## KnowKneadStudentFileNo.2.xlsx**
## Sheet1
| | | | | | |
| — | — | — | — | — | — |
| Individual Forecasting Analysis Assignment, QNT 5160 |
| File No. 2 (Monthly Data for 2024 and 2025) |
| | Year | Month | Call Volume | Monthly Cases | Notes |
| | 2023 | Jul | 25,944 | 64,332 |
| | 2023 | Aug | 28,112 | 68,111 | New regional distribution contract effective 8/1/23 |
| | 2023 | Sep | 27,937 | 69,120 |
| | 2023 | Oct | 28,610 | 70,301 |
| | 2023 | Nov | 28,922 | 71,205 |
| | 2023 | Dec | 29,212 | 70,885 |
| | 2024 | Jan | 29,566 | 72,112 | |
| | 2024 | Feb | 31,174 | 75,217 | |
| | 2024 | Mar | 31,178 | 77,950 | |
| | 2024 | Apr | 34,550 | 85,805 | Sweet Teeth corporation acquired 4/1/2024 |
| | 2024 | May | 35,560 | 87,313 |
| | 2024 | Jun | 35,760 | 85,499 | Dental insurance plan changed effective 7/1/2024 |
| | 2024 | Jul | 33,531 | 88,402 |
| | 2024 | Aug | 31,386 | 77,953 | Coffee division sold to CMI Corporation 8/1/2024 |
| | 2024 | Sep | 33,881 | 77,639 |
| | 2024 | Oct | 33,219 | 81,136 | Major tax law changes signed into law by U.S. President |
| | 2024 | Nov | 33,760 | 80,936 |
| | 2024 | Dec | 33,321 | 79,199 | Year-end bonuses announced on 12/10/2024 |
| | 2025 | Jan | 32,773 | 83,510 |
| | 2025 | Feb | 35,499 | 81,463 |
| | 2025 | Mar | 39,484 | 89,910 | Doughey's 'Nutz acquired 3/15/2025 |
| | 2025 | Apr | 38,622 | 97,752 |
| | 2025 | May | 41,584 | 93,526 |
| | 2025 | Jun | 38,923 | 94,212 | New employee insurance deductions in effect starting 7/1/2025 |
| | 2025 | Jul |
**## Assignment3TP.docx**
First read the “Forecasting Interpretation Project Instructions” file here:
[Forecasting Analysis Project Instructions.docx](https://nsu.instructure.com/courses/2552606/files/153777032?wrap=1) [Download Forecasting Analysis Project Instructions.docx](https://nsu.instructure.com/courses/2552606/files/153777032/download?download_frd=1)
Study the case here:
[Case Study for Individual Project.docx](https://nsu.instructure.com/courses/2552606/files/153777034?wrap=1) [Download Case Study for Individual Project.docx](https://nsu.instructure.com/courses/2552606/files/153777034/download?download_frd=1) and
accompanying historical datafile here:
[Student File No. 2.xlsx](https://nsu.instructure.com/courses/2552606/files/153777036?wrap=1) [Download Student File No. 2.xlsx](https://nsu.instructure.com/courses/2552606/files/153777036/download?download_frd=1).
Follow the instructions in the to analyze the case.
· This project refers to staffing needs for the first 24 months of operating the call center.
· Part 1 questions refer to
_hiring_ using
_monthly data_ based on the first 24 months of operating the call center. You will need to run two different forecasting models using the Hillier templates.
· Part 2 offers a recommendation to management based on the analysis you conducted.
For Part 1, you will need the
exponential smoothing template and the
linear regression template found here:
·
[Hiller Excel Templates](https://nsu.instructure.com/courses/2552606/pages/hillier-forecasting-templates?module_item_id=46565196)
Here is a video tutorial on running the Exponential Smoothing template.
individual Forecasting Analysis 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.
1. Part 1 questions refer to hiring using monthly data based on the first 24 months of operating the call center.
“Know Knead Student File No. 2.xlsx”
Part 2 offers a recommendation to management based on the analysis you conducted.
Conduct necessary calculations and visualizations to answer the questions.
For full credit you must submit
1. Excel spreadsheet model(s) with calculations/formulas (not harded-coded numbers)
2. Properly formatted Business Report which includes your answers to the assignment
questions.
· Include a cover page, and all citations and headers should be in APA format.
· Reports and models should be uploaded in the Canvas Dropbox before the posted deadline.
This is the 2nd of two forecasting projects. Make sure to use Student File 2 which has monthly 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). Your report should follow the prescribed assignment format, the proper writing style, and APA format.
Part 1 (10 points):
In answering the Part 1 questions, you should download and refer to Student Data File No. 2 which contains the historical data that you will need to answer the questions.
Question 1a (3 points):
Prepare a forecast of call volume for July 2025 by applying Exponential Smoothing to the prior 24 months of data. Use the appropriate Excel template from the Hillier text to prepare your forecast. Either assume that initial call volume is 26,644 and/or justify using a different initial value.
Choose at least two different alpha values for your model. Model do these choices change your forecasts?
Show your forecast below and attach the completed Excel template.
You must show your formulas within your spreadsheet (not hard-coded numbers).
2
Question 1b (3 points):
Apply Linear Regression to predict call volume from monthly cases using the appropriate Excel template.
Use 95,050 as your July 2025 monthly cases input or a simple time-series method to project July 2025.
Show your forecast below and attach the completed Excel template. Show your formulas (not hard-coded numbers).
Question 1c (1 point):
Calculate the Mean absolute deviation value of the Exponential Smoothing model (Question 3a) and the Average Absolute Estimation Error of the Linear Regression model
(Question 3b). Explain the difference
between these two values. Why does one method out-perform the other?
Question 1d (1 point):
What is your best forecast for July 2025? Show your forecast value. Explain how you came up with this forecast. Justify the Methods used in this analysis. Consider your answers to Questions 1a, 1b and 1c and all the factors that have been described above. You may present an additional model if you feel it could beat the models you have already run.
Question 2 (2 points)
Provide your recommendations to Corey on how to modify forecasting processes and improve its accuracy.
Appendix
Business Report Format
Executive Summary
Problem statement
Methods
Describe your dataset
Describe and justify analytical methods
Results (or Analysis)
Results with interpretation
Descriptive statistics (how big is your dataset?)
Inferential statistics and tests
Recommendation
Appendices (if necessary)
Example in Getting Started>Grading Policy
For full credit you must submit:
a) Business memo to report and discuss your findings on this case and answer the case questions.
b) Your spreadsheet models coded with the appropriate formulas (not hard coded values).
Follow APA 6 style to prepare the report and include the NSU Cover Sheet.
Here is the
[Individual Project Grading Rubric.docx](https://nsu.instructure.com/courses/2552606/files/153776935/download?wrap=1) [Download Individual Project Grading Rubric.docx](https://nsu.instructure.com/courses/2552606/files/153776935/download?download_frd=1).
**## Last_Valuefig10.5withoutseasonality.xlsx**
## Last Value
| | | | | | | | | | | |
| — | — | — | — | — | — | — | — | — | — | — |
| Template for Last-Value Forecasting Method |
| | Time | True | Last-Value | Forecasting | | | | | Range Name | Cells |
| | Period | Value | Forecast | Error | | Mean Absolute Deviation | | | Forecast | D5:D34 |
| | 1 | 6,809 | | | | MAD = | 885 | | ForecastingError | E5:E34 |
| | 2 | 6,465 | 6,809 | 344 | | | | | MAD | H5 |
| | 3 | 6,569 | 6,465 | 104 | | Mean Square Error | | | MSE | H8 |
| | 4 | 8,266 | 6,569 | 1,697 | | MSE = | 1,150,777 | | TrueValue | C5:C34 |
| | 5 | 7,257 | 8,266 | 1,009 |
| | 6 | 7,064 | 7,257 | 193 |
| | 7 | 7,784 | 7,064 | 720 |
| | 8 | 8,724 | 7,784 | 940 |
| | 9 | 6,992 | 8,724 | 1,732 |
| | 10 | 6,822 | 6,992 | 170 |
| | 11 | 7,949 | 6,822 | 1,127 |
| | 12 | 9,650 | 7,949 | 1,701 |
| | 13 | | 9,650 | |
| | 14 | | ERROR:#N/A | |
| | 15 | | ERROR:#N/A | |
| | 16 | | ERROR:#N/A | |
| | 17 | | ERROR:#N/A | |
| | 18 | | ERROR:#N/A | |
| | 19 | | ERROR:#N/A | |
| | 20 | | ERROR:#N/A | |
| | 21 | | ERROR:#N/A | |
| | 22 | | ERROR:#N/A | |
| | 23 | | ERROR:#N/A | |
| | 24 | | ERROR:#N/A | |
| | 25 | | ERROR:#N/A | |
| | 26 | | ERROR:#N/A | |
| | 27 | | ERROR:#N/A | |
| | 28 | | ERROR:#N/A | |
| | 29 | | ERROR:#N/A | |
| | 30 | | ERROR:#N/A | |
True Value 6809 6465 6569 8266 7257 7064 7784 8724 6992 6822 7949 9650 Forecast 6809 6465 6569 8266 7257 7064 7784 8724 6992 6822 7949 9650 #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A
Time Period
Value
**Moving_Averagefig10.7withoutseasonality.xlsx**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/moving-averagefig10-7withoutseasonality-xlsx)
**Linear_Regressionfig10.167.xlsx**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/linear-regressionfig10-167-xlsx)
**Seasonality_Factorsfig10.3.xlsx**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/seasonality-factorsfig10-3-xlsx)
**CaseStudyforIndividualProject_KnowKnead-1.docx**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/casestudyforindividualproject-knowknead-1-docx)
**Exponential_Season__Trendfig10.10.xlsx**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/exponential-season-trendfig10-10-xlsx)
**Exponential_Season__Trendfig10.10.xlsx**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/exponential-season-trendfig10-10-xlsx)
**## Excelspreadsheet.docx**
Click on the method names below to download a sample Excel spreadsheet displaying each.
[Averaging](https://nsu.instructure.com/courses/2552606/files/153776599/download?wrap=1) [Download Averaging](https://nsu.instructure.com/courses/2552606/files/153776599/download?download_frd=1)
[Averaging with Seasonality](https://nsu.instructure.com/courses/2552606/files/153776942/download?wrap=1) [Download Averaging with Seasonality](https://nsu.instructure.com/courses/2552606/files/153776942/download?download_frd=1)
[Exponential Smoothing](https://nsu.instructure.com/courses/2552606/files/153776547?wrap=1) [Download Exponential Smoothing](https://nsu.instructure.com/courses/2552606/files/153776547/download?download_frd=1)
[Exponential Smoothing with Seasonality](https://nsu.instructure.com/courses/2552606/files/153776904/download?wrap=1) [Download Exponential Smoothing with Seasonality](https://nsu.instructure.com/courses/2552606/files/153776904/download?download_frd=1)
[Exponential Smoothing with Trend](https://nsu.instructure.com/courses/2552606/files/153776902/download?wrap=1) [Download Exponential Smoothing with Trend](https://nsu.instructure.com/courses/2552606/files/153776902/download?download_frd=1)
[Exponential Smoothing with Trend and Seasonality](https://nsu.instructure.com/courses/2552606/files/153776600/download?wrap=1) [Download Exponential Smoothing with Trend and Seasonality](https://nsu.instructure.com/courses/2552606/files/153776600/download?download_frd=1)
[Last Value](https://nsu.instructure.com/courses/2552606/files/153776661/download?wrap=1) [Download Last Value](https://nsu.instructure.com/courses/2552606/files/153776661/download?download_frd=1)
[Last Value with Seasonality](https://nsu.instructure.com/courses/2552606/files/153776915/download?wrap=1) [Download Last Value with Seasonality](https://nsu.instructure.com/courses/2552606/files/153776915/download?download_frd=1)
[Linear Regression](https://nsu.instructure.com/courses/2552606/files/153776545?wrap=1) [Download Linear Regression](https://nsu.instructure.com/courses/2552606/files/153776545/download?download_frd=1)
[Moving Average](https://nsu.instructure.com/courses/2552606/files/153776598/download?wrap=1) [Download Moving Average](https://nsu.instructure.com/courses/2552606/files/153776598/download?download_frd=1)
[Moving Average with Seasonality](https://nsu.instructure.com/courses/2552606/files/153776659/download?wrap=1) [Download Moving Average with Seasonality](https://nsu.instructure.com/courses/2552606/files/153776659/download?download_frd=1)
[Seasonality Factors](https://nsu.instructure.com/courses/2552606/files/153776745/download?wrap=1) [Download Seasonality Factors](https://nsu.instructure.com/courses/2552606/files/153776745/download?download_frd=1)
**## KnowKneadStudentFileNo.2.xlsx**
## Sheet1
| | | | | | |
| — | — | — | — | — | — |
| Individual Forecasting Analysis Assignment, QNT 5160 |
| File No. 2 (Monthly Data for 2024 and 2025) |
| | Year | Month | Call Volume | Monthly Cases | Notes |
| | 2023 | Jul | 25,944 | 64,332 |
| | 2023 | Aug | 28,112 | 68,111 | New regional distribution contract effective 8/1/23 |
| | 2023 | Sep | 27,937 | 69,120 |
| | 2023 | Oct | 28,610 | 70,301 |
| | 2023 | Nov | 28,922 | 71,205 |
| | 2023 | Dec | 29,212 | 70,885 |
| | 2024 | Jan | 29,566 | 72,112 | |
| | 2024 | Feb | 31,174 | 75,217 | |
| | 2024 | Mar | 31,178 | 77,950 | |
| | 2024 | Apr | 34,550 | 85,805 | Sweet Teeth corporation acquired 4/1/2024 |
| | 2024 | May | 35,560 | 87,313 |
| | 2024 | Jun | 35,760 | 85,499 | Dental insurance plan changed effective 7/1/2024 |
| | 2024 | Jul | 33,531 | 88,402 |
| | 2024 | Aug | 31,386 | 77,953 | Coffee division sold to CMI Corporation 8/1/2024 |
| | 2024 | Sep | 33,881 | 77,639 |
| | 2024 | Oct | 33,219 | 81,136 | Major tax law changes signed into law by U.S. President |
| | 2024 | Nov | 33,760 | 80,936 |
| | 2024 | Dec | 33,321 | 79,199 | Year-end bonuses announced on 12/10/2024 |
| | 2025 | Jan | 32,773 | 83,510 |
| | 2025 | Feb | 35,499 | 81,463 |
| | 2025 | Mar | 39,484 | 89,910 | Doughey's 'Nutz acquired 3/15/2025 |
| | 2025 | Apr | 38,622 | 97,752 |
| | 2025 | May | 41,584 | 93,526 |
| | 2025 | Jun | 38,923 | 94,212 | New employee insurance deductions in effect starting 7/1/2025 |
| | 2025 | Jul |
**## Assignment3TP.docx**
First read the “Forecasting Interpretation Project Instructions” file here:
[Forecasting Analysis Project Instructions.docx](https://nsu.instructure.com/courses/2552606/files/153777032?wrap=1) [Download Forecasting Analysis Project Instructions.docx](https://nsu.instructure.com/courses/2552606/files/153777032/download?download_frd=1)
Study the case here:
[Case Study for Individual Project.docx](https://nsu.instructure.com/courses/2552606/files/153777034?wrap=1) [Download Case Study for Individual Project.docx](https://nsu.instructure.com/courses/2552606/files/153777034/download?download_frd=1) and
accompanying historical datafile here:
[Student File No. 2.xlsx](https://nsu.instructure.com/courses/2552606/files/153777036?wrap=1) [Download Student File No. 2.xlsx](https://nsu.instructure.com/courses/2552606/files/153777036/download?download_frd=1).
Follow the instructions in the to analyze the case.
· This project refers to staffing needs for the first 24 months of operating the call center.
· Part 1 questions refer to
_hiring_ using
_monthly data_ based on the first 24 months of operating the call center. You will need to run two different forecasting models using the Hillier templates.
· Part 2 offers a recommendation to management based on the analysis you conducted.
For Part 1, you will need the
exponential smoothing template and the
linear regression template found here:
·
[Hiller Excel Templates](https://nsu.instructure.com/courses/2552606/pages/hillier-forecasting-templates?module_item_id=46565196)
Here is a video tutorial on running the Exponential Smoothing template.
individual Forecasting Analysis 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.
1. Part 1 questions refer to hiring using monthly data based on the first 24 months of operating the call center.
“Know Knead Student File No. 2.xlsx”
Part 2 offers a recommendation to management based on the analysis you conducted.
Conduct necessary calculations and visualizations to answer the questions.
For full credit you must submit
1. Excel spreadsheet model(s) with calculations/formulas (not harded-coded numbers)
2. Properly formatted Business Report which includes your answers to the assignment
questions.
· Include a cover page, and all citations and headers should be in APA format.
· Reports and models should be uploaded in the Canvas Dropbox before the posted deadline.
This is the 2nd of two forecasting projects. Make sure to use Student File 2 which has monthly 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). Your report should follow the prescribed assignment format, the proper writing style, and APA format.
Part 1 (10 points):
In answering the Part 1 questions, you should download and refer to Student Data File No. 2 which contains the historical data that you will need to answer the questions.
Question 1a (3 points):
Prepare a forecast of call volume for July 2025 by applying Exponential Smoothing to the prior 24 months of data. Use the appropriate Excel template from the Hillier text to prepare your forecast. Either assume that initial call volume is 26,644 and/or justify using a different initial value.
Choose at least two different alpha values for your model. Model do these choices change your forecasts?
Show your forecast below and attach the completed Excel template.
You must show your formulas within your spreadsheet (not hard-coded numbers).
2
Question 1b (3 points):
Apply Linear Regression to predict call volume from monthly cases using the appropriate Excel template.
Use 95,050 as your July 2025 monthly cases input or a simple time-series method to project July 2025.
Show your forecast below and attach the completed Excel template. Show your formulas (not hard-coded numbers).
Question 1c (1 point):
Calculate the Mean absolute deviation value of the Exponential Smoothing model (Question 3a) and the Average Absolute Estimation Error of the Linear Regression model
(Question 3b). Explain the difference
between these two values. Why does one method out-perform the other?
Question 1d (1 point):
What is your best forecast for July 2025? Show your forecast value. Explain how you came up with this forecast. Justify the Methods used in this analysis. Consider your answers to Questions 1a, 1b and 1c and all the factors that have been described above. You may present an additional model if you feel it could beat the models you have already run.
Question 2 (2 points)
Provide your recommendations to Corey on how to modify forecasting processes and improve its accuracy.
Appendix
Business Report Format
Executive Summary
Problem statement
Methods
Describe your dataset
Describe and justify analytical methods
Results (or Analysis)
Results with interpretation
Descriptive statistics (how big is your dataset?)
Inferential statistics and tests
Recommendation
Appendices (if necessary)
Example in Getting Started>Grading Policy
For full credit you must submit:
a) Business memo to report and discuss your findings on this case and answer the case questions.
b) Your spreadsheet models coded with the appropriate formulas (not hard coded values).
Follow APA 6 style to prepare the report and include the NSU Cover Sheet.
Here is the
[Individual Project Grading Rubric.docx](https://nsu.instructure.com/courses/2552606/files/153776935/download?wrap=1) [Download Individual Project Grading Rubric.docx](https://nsu.instructure.com/courses/2552606/files/153776935/download?download_frd=1).
**## Last_Valuefig10.5withoutseasonality.xlsx**
## Last Value
| | | | | | | | | | | |
| — | — | — | — | — | — | — | — | — | — | — |
| Template for Last-Value Forecasting Method |
| | Time | True | Last-Value | Forecasting | | | | | Range Name | Cells |
| | Period | Value | Forecast | Error | | Mean Absolute Deviation | | | Forecast | D5:D34 |
| | 1 | 6,809 | | | | MAD = | 885 | | ForecastingError | E5:E34 |
| | 2 | 6,465 | 6,809 | 344 | | | | | MAD | H5 |
| | 3 | 6,569 | 6,465 | 104 | | Mean Square Error | | | MSE | H8 |
| | 4 | 8,266 | 6,569 | 1,697 | | MSE = | 1,150,777 | | TrueValue | C5:C34 |
| | 5 | 7,257 | 8,266 | 1,009 |
| | 6 | 7,064 | 7,257 | 193 |
| | 7 | 7,784 | 7,064 | 720 |
| | 8 | 8,724 | 7,784 | 940 |
| | 9 | 6,992 | 8,724 | 1,732 |
| | 10 | 6,822 | 6,992 | 170 |
| | 11 | 7,949 | 6,822 | 1,127 |
| | 12 | 9,650 | 7,949 | 1,701 |
| | 13 | | 9,650 | |
| | 14 | | ERROR:#N/A | |
| | 15 | | ERROR:#N/A | |
| | 16 | | ERROR:#N/A | |
| | 17 | | ERROR:#N/A | |
| | 18 | | ERROR:#N/A | |
| | 19 | | ERROR:#N/A | |
| | 20 | | ERROR:#N/A | |
| | 21 | | ERROR:#N/A | |
| | 22 | | ERROR:#N/A | |
| | 23 | | ERROR:#N/A | |
| | 24 | | ERROR:#N/A | |
| | 25 | | ERROR:#N/A | |
| | 26 | | ERROR:#N/A | |
| | 27 | | ERROR:#N/A | |
| | 28 | | ERROR:#N/A | |
| | 29 | | ERROR:#N/A | |
| | 30 | | ERROR:#N/A | |
True Value 6809 6465 6569 8266 7257 7064 7784 8724 6992 6822 7949 9650 Forecast 6809 6465 6569 8266 7257 7064 7784 8724 6992 6822 7949 9650 #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A
Time Period
Value
**Moving_Averagefig10.7withoutseasonality.xlsx**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/moving-averagefig10-7withoutseasonality-xlsx)
**Linear_Regressionfig10.167.xlsx**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/linear-regressionfig10-167-xlsx)
**Seasonality_Factorsfig10.3.xlsx**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/seasonality-factorsfig10-3-xlsx)
**CaseStudyforIndividualProject_KnowKnead-1.docx**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/casestudyforindividualproject-knowknead-1-docx)
**Exponential_Season__Trendfig10.10.xlsx**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/exponential-season-trendfig10-10-xlsx)
**## Excelspreadsheet.docx**
Click on the method names below to download a sample Excel spreadsheet displaying each.
[Averaging](https://nsu.instructure.com/courses/2552606/files/153776599/download?wrap=1) [Download Averaging](https://nsu.instructure.com/courses/2552606/files/153776599/download?download_frd=1)
[Averaging with Seasonality](https://nsu.instructure.com/courses/2552606/files/153776942/download?wrap=1) [Download Averaging with Seasonality](https://nsu.instructure.com/courses/2552606/files/153776942/download?download_frd=1)
[Exponential Smoothing](https://nsu.instructure.com/courses/2552606/files/153776547?wrap=1) [Download Exponential Smoothing](https://nsu.instructure.com/courses/2552606/files/153776547/download?download_frd=1)
[Exponential Smoothing with Seasonality](https://nsu.instructure.com/courses/2552606/files/153776904/download?wrap=1) [Download Exponential Smoothing with Seasonality](https://nsu.instructure.com/courses/2552606/files/153776904/download?download_frd=1)
[Exponential Smoothing with Trend](https://nsu.instructure.com/courses/2552606/files/153776902/download?wrap=1) [Download Exponential Smoothing with Trend](https://nsu.instructure.com/courses/2552606/files/153776902/download?download_frd=1)
[Exponential Smoothing with Trend and Seasonality](https://nsu.instructure.com/courses/2552606/files/153776600/download?wrap=1) [Download Exponential Smoothing with Trend and Seasonality](https://nsu.instructure.com/courses/2552606/files/153776600/download?download_frd=1)
[Last Value](https://nsu.instructure.com/courses/2552606/files/153776661/download?wrap=1) [Download Last Value](https://nsu.instructure.com/courses/2552606/files/153776661/download?download_frd=1)
[Last Value with Seasonality](https://nsu.instructure.com/courses/2552606/files/153776915/download?wrap=1) [Download Last Value with Seasonality](https://nsu.instructure.com/courses/2552606/files/153776915/download?download_frd=1)
[Linear Regression](https://nsu.instructure.com/courses/2552606/files/153776545?wrap=1) [Download Linear Regression](https://nsu.instructure.com/courses/2552606/files/153776545/download?download_frd=1)
[Moving Average](https://nsu.instructure.com/courses/2552606/files/153776598/download?wrap=1) [Download Moving Average](https://nsu.instructure.com/courses/2552606/files/153776598/download?download_frd=1)
[Moving Average with Seasonality](https://nsu.instructure.com/courses/2552606/files/153776659/download?wrap=1) [Download Moving Average with Seasonality](https://nsu.instructure.com/courses/2552606/files/153776659/download?download_frd=1)
[Seasonality Factors](https://nsu.instructure.com/courses/2552606/files/153776745/download?wrap=1) [Download Seasonality Factors](https://nsu.instructure.com/courses/2552606/files/153776745/download?download_frd=1)
**## KnowKneadStudentFileNo.2.xlsx**
## Sheet1
| | | | | | |
| — | — | — | — | — | — |
| Individual Forecasting Analysis Assignment, QNT 5160 |
| File No. 2 (Monthly Data for 2024 and 2025) |
| | Year | Month | Call Volume | Monthly Cases | Notes |
| | 2023 | Jul | 25,944 | 64,332 |
| | 2023 | Aug | 28,112 | 68,111 | New regional distribution contract effective 8/1/23 |
| | 2023 | Sep | 27,937 | 69,120 |
| | 2023 | Oct | 28,610 | 70,301 |
| | 2023 | Nov | 28,922 | 71,205 |
| | 2023 | Dec | 29,212 | 70,885 |
| | 2024 | Jan | 29,566 | 72,112 | |
| | 2024 | Feb | 31,174 | 75,217 | |
| | 2024 | Mar | 31,178 | 77,950 | |
| | 2024 | Apr | 34,550 | 85,805 | Sweet Teeth corporation acquired 4/1/2024 |
| | 2024 | May | 35,560 | 87,313 |
| | 2024 | Jun | 35,760 | 85,499 | Dental insurance plan changed effective 7/1/2024 |
| | 2024 | Jul | 33,531 | 88,402 |
| | 2024 | Aug | 31,386 | 77,953 | Coffee division sold to CMI Corporation 8/1/2024 |
| | 2024 | Sep | 33,881 | 77,639 |
| | 2024 | Oct | 33,219 | 81,136 | Major tax law changes signed into law by U.S. President |
| | 2024 | Nov | 33,760 | 80,936 |
| | 2024 | Dec | 33,321 | 79,199 | Year-end bonuses announced on 12/10/2024 |
| | 2025 | Jan | 32,773 | 83,510 |
| | 2025 | Feb | 35,499 | 81,463 |
| | 2025 | Mar | 39,484 | 89,910 | Doughey's 'Nutz acquired 3/15/2025 |
| | 2025 | Apr | 38,622 | 97,752 |
| | 2025 | May | 41,584 | 93,526 |
| | 2025 | Jun | 38,923 | 94,212 | New employee insurance deductions in effect starting 7/1/2025 |
| | 2025 | Jul |
**## Assignment3TP.docx**
First read the “Forecasting Interpretation Project Instructions” file here:
[Forecasting Analysis Project Instructions.docx](https://nsu.instructure.com/courses/2552606/files/153777032?wrap=1) [Download Forecasting Analysis Project Instructions.docx](https://nsu.instructure.com/courses/2552606/files/153777032/download?download_frd=1)
Study the case here:
[Case Study for Individual Project.docx](https://nsu.instructure.com/courses/2552606/files/153777034?wrap=1) [Download Case Study for Individual Project.docx](https://nsu.instructure.com/courses/2552606/files/153777034/download?download_frd=1) and
accompanying historical datafile here:
[Student File No. 2.xlsx](https://nsu.instructure.com/courses/2552606/files/153777036?wrap=1) [Download Student File No. 2.xlsx](https://nsu.instructure.com/courses/2552606/files/153777036/download?download_frd=1).
Follow the instructions in the to analyze the case.
· This project refers to staffing needs for the first 24 months of operating the call center.
· Part 1 questions refer to
_hiring_ using
_monthly data_ based on the first 24 months of operating the call center. You will need to run two different forecasting models using the Hillier templates.
· Part 2 offers a recommendation to management based on the analysis you conducted.
For Part 1, you will need the
exponential smoothing template and the
linear regression template found here:
·
[Hiller Excel Templates](https://nsu.instructure.com/courses/2552606/pages/hillier-forecasting-templates?module_item_id=46565196)
Here is a video tutorial on running the Exponential Smoothing template.
individual Forecasting Analysis 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.
1. Part 1 questions refer to hiring using monthly data based on the first 24 months of operating the call center.
“Know Knead Student File No. 2.xlsx”
Part 2 offers a recommendation to management based on the analysis you conducted.
Conduct necessary calculations and visualizations to answer the questions.
For full credit you must submit
1. Excel spreadsheet model(s) with calculations/formulas (not harded-coded numbers)
2. Properly formatted Business Report which includes your answers to the assignment
questions.
· Include a cover page, and all citations and headers should be in APA format.
· Reports and models should be uploaded in the Canvas Dropbox before the posted deadline.
This is the 2nd of two forecasting projects. Make sure to use Student File 2 which has monthly 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). Your report should follow the prescribed assignment format, the proper writing style, and APA format.
Part 1 (10 points):
In answering the Part 1 questions, you should download and refer to Student Data File No. 2 which contains the historical data that you will need to answer the questions.
Question 1a (3 points):
Prepare a forecast of call volume for July 2025 by applying Exponential Smoothing to the prior 24 months of data. Use the appropriate Excel template from the Hillier text to prepare your forecast. Either assume that initial call volume is 26,644 and/or justify using a different initial value.
Choose at least two different alpha values for your model. Model do these choices change your forecasts?
Show your forecast below and attach the completed Excel template.
You must show your formulas within your spreadsheet (not hard-coded numbers).
2
Question 1b (3 points):
Apply Linear Regression to predict call volume from monthly cases using the appropriate Excel template.
Use 95,050 as your July 2025 monthly cases input or a simple time-series method to project July 2025.
Show your forecast below and attach the completed Excel template. Show your formulas (not hard-coded numbers).
Question 1c (1 point):
Calculate the Mean absolute deviation value of the Exponential Smoothing model (Question 3a) and the Average Absolute Estimation Error of the Linear Regression model
(Question 3b). Explain the difference
between these two values. Why does one method out-perform the other?
Question 1d (1 point):
What is your best forecast for July 2025? Show your forecast value. Explain how you came up with this forecast. Justify the Methods used in this analysis. Consider your answers to Questions 1a, 1b and 1c and all the factors that have been described above. You may present an additional model if you feel it could beat the models you have already run.
Question 2 (2 points)
Provide your recommendations to Corey on how to modify forecasting processes and improve its accuracy.
Appendix
Business Report Format
Executive Summary
Problem statement
Methods
Describe your dataset
Describe and justify analytical methods
Results (or Analysis)
Results with interpretation
Descriptive statistics (how big is your dataset?)
Inferential statistics and tests
Recommendation
Appendices (if necessary)
Example in Getting Started>Grading Policy
For full credit you must submit:
a) Business memo to report and discuss your findings on this case and answer the case questions.
b) Your spreadsheet models coded with the appropriate formulas (not hard coded values).
Follow APA 6 style to prepare the report and include the NSU Cover Sheet.
Here is the
[Individual Project Grading Rubric.docx](https://nsu.instructure.com/courses/2552606/files/153776935/download?wrap=1) [Download Individual Project Grading Rubric.docx](https://nsu.instructure.com/courses/2552606/files/153776935/download?download_frd=1).
**## Last_Valuefig10.5withoutseasonality.xlsx**
## Last Value
| | | | | | | | | | | |
| — | — | — | — | — | — | — | — | — | — | — |
| Template for Last-Value Forecasting Method |
| | Time | True | Last-Value | Forecasting | | | | | Range Name | Cells |
| | Period | Value | Forecast | Error | | Mean Absolute Deviation | | | Forecast | D5:D34 |
| | 1 | 6,809 | | | | MAD = | 885 | | ForecastingError | E5:E34 |
| | 2 | 6,465 | 6,809 | 344 | | | | | MAD | H5 |
| | 3 | 6,569 | 6,465 | 104 | | Mean Square Error | | | MSE | H8 |
| | 4 | 8,266 | 6,569 | 1,697 | | MSE = | 1,150,777 | | TrueValue | C5:C34 |
| | 5 | 7,257 | 8,266 | 1,009 |
| | 6 | 7,064 | 7,257 | 193 |
| | 7 | 7,784 | 7,064 | 720 |
| | 8 | 8,724 | 7,784 | 940 |
| | 9 | 6,992 | 8,724 | 1,732 |
| | 10 | 6,822 | 6,992 | 170 |
| | 11 | 7,949 | 6,822 | 1,127 |
| | 12 | 9,650 | 7,949 | 1,701 |
| | 13 | | 9,650 | |
| | 14 | | ERROR:#N/A | |
| | 15 | | ERROR:#N/A | |
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| | 30 | | ERROR:#N/A | |
True Value 6809 6465 6569 8266 7257 7064 7784 8724 6992 6822 7949 9650 Forecast 6809 6465 6569 8266 7257 7064 7784 8724 6992 6822 7949 9650 #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A
Time Period
Value
**Moving_Averagefig10.7withoutseasonality.xlsx**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/moving-averagefig10-7withoutseasonality-xlsx)
**Linear_Regressionfig10.167.xlsx**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/linear-regressionfig10-167-xlsx)
**Seasonality_Factorsfig10.3.xlsx**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/seasonality-factorsfig10-3-xlsx)
**CaseStudyforIndividualProject_KnowKnead-1.docx**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/casestudyforindividualproject-knowknead-1-docx)
**Exponential_Season__Trendfig10.10.xlsx**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/exponential-season-trendfig10-10-xlsx)
