Forecasting Case Analysis

MGT 3332


All team members must do all parts of the project and compare results. DO NOT divide up parts of the project as many parts are related.

Teams must submit both Word and Excel files of their work to Canvas.

Do not submit any file until all team members agree on the deliverables.

Only one team member will submit the files. There is no second chance on submittal.

Post names of all contributing team members on top of Word file.

The Fresh Detergent Case

Enterprise Industries produces Fresh, a brand of liquid detergent. In order to more effectively manage its inventory, the company would like to better predict demand for Fresh. To develop a prediction model, the company has gathered data concerning demand for Fresh over the last 33 sales periods. Each sales period is defined as one month. The variables are as follows:

Period = Time period in month

Demand = Y = demand for a large size bottle of Fresh (in 100,000)

Price = the price of Fresh as offered by Ent. Industries

AIP = the Average Industry Price

ADV = Enterprise Industries Advertising Expenditure (in $100,000) to Promote Fresh in the sales period.

DIFF = AIP – Price = the “price difference” in the sales period

1. Download the data file from Canvas site. Save the original download (just in case), and copy it into Excel spreadsheet.

IMPORTANT: Your downloaded data file should match both your section number and your team number. This is shown in both the file name and in the title line of the file. If you download and use the wrong data file for your team, you will fail the assignment!

2. Make time series scatter plots of all five variables (five graphs). Insert trend line, equation, and R-squared. Observe graphs and provide interpretation.


· These and all other charts must be made in Excel. You can copy & paste them as images into your Word if you wish; otherwise, just refer in the word document to the Chart location in your Excel worksheet.

· These and all other charts should be of quality sufficient for presentation to senior management at Enterprise Industries. All charts should have titles, legends, and both axes labeled.

3. Construct scatter plots of Demand vs. DIFF and Demand vs. ADV, Demand vs. AIP, and Demand vs. Price. Insert fitted line, equation, and R-squared. Observe graphs and provide interpretation. Note that Demand is always on the Y axis.

4. Obtain the correlation matrix for all six variables and rank the variables that have strong correlation with Demand (must use absolute value.) Explain your findings in plain language.

5. Use 3-month and 6-month moving averages to predict the demand for October 2023. Find MAD for both forecasts and identify the preferred one based on each calculation. Is the moving average suitable method for forecasting for this data set? Explain your reasoning.

6. Use Exponential smoothing forecasts with alpha of 0.1, 0.2, …, 0.9 to predict October 2023 demand. Identify the value of alpha that results in the lowest MAD.

7. Find the monthly seasonal indices for the demand values using Simple Average (SA) method. Find the de-seasonalized demand values by dividing monthly demand by respective seasonal indices.

8. Use regression to perform trend analysis on the de-seasonalized demand values. Is trend analysis suitable for this data? Find MAD and explain the Excel Regression output (trend equation, r, r-squared, goodness of model).

9. Find the seasonally adjusted trend forecasts for October through December 2023.

10. Perform simple linear regression analysis with ADV as the independent variable to predict demand. Write the complete equation, find MAD and explain the Excel Regression output.
Make sure to use the de-seasonalized demand data for this model and all future models.

11. Repeat part (10) with DIFF as the independent variable.

12. Construct multiple linear regression model with Period, AIP, DIFF, and ADV as independent variables. Formulate the equation, find MAD, and explain the output. Rank variables based on their degree of contribution to the model. Observe significant F, R-squared, and p-values and explain.

13. Perform multiple linear regression analysis with Period, DIFF, and ADV as independent variables. Formulate the equation and find MAD. Which variable is the most significant predictor of demand? Rank the independent variables based on their degree of contribution to the model. Observe significant F, R-squared, and p-values and explain.

14. Use the model obtained in parts 13 and make forecasts for the following months. Make sure to seasonalize final forecasts.

Period Price AIP ADV

Oct. 2023 $7.44 $7.78 $11.03

Nov. 2023 $7.58 $7.83 $11.63

Dec. 2023 $7.93 $8.08 $11.83

15. Provide a case conclusion based on above analysis.

Grading Criteria

· Completeness/Correctness 70%

· Quality of Interpretations/Analysis 20%

· General Quality of Report/graphs/writing 10%

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