**Group Forecasting Analysis Instructions**

*Instructions*

This is a group assignment and therefore must be completed by the student group without outside assistance. To complete the assignment, first read the write-up for the “Clean Sweep” case study. Then, answer the questions listed below for each part of the case.

**Part 1** questions refer to

*hiring* using

*monthly data* based on the first 18 months of operating the call center.

“

*Clean Sweep Student File No. 2, Fall 2023.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 group’s 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 before the posted deadline.

This is the 2nd of

*two* forecasting projects. Make sure to use

*Student File
2
* which has

*data.*

__monthly__“

*Clean Sweep Student File No. 2, Fall 2023.xlsx*”

*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 2023 by applying Exponential Smoothing to the prior 18 months of data. Use the appropriate Excel template from the Hillier text to prepare your forecast. Either assume that initial call volume is 29,778 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).

**
Question 1b (3 points)
**:

Apply Linear Regression to predict call volume from monthly cleans using the appropriate Excel template. Use 95,000 as your July 2023 monthly cleans input or a simple time-series method to project July 2023. 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 2023? 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 Belinda 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

1