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

First read the “Forecasting Interpretation Project Instructions” file here: Forecasting Analysis Project Instructions.docx Download Forecasting Analysis Project Instructions.docx 

Study the case here: Case Study for Individual Project.docx Download Case Study for Individual Project.docxand

accompanying historical datafile here: Student File No. 2.xlsx Download Student File No. 2.xlsx.

 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:

o   Hiller Excel Templates

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 Download Individual Project Grading Rubric.docx.