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 60 sales periods.Each sales period is defined as one month.The variables are as follows:

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

DIFF = AIP - Price = the "price difference" in the sales period

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

2-Obtain the correlation matrix for all six variables and list the variables that have strong correlation with Demand.High correlation is r > 0.70.Explain your findings in plain language.

3-Use 3-month and 6-month moving averages to predict the demand for January 2021. 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.

4-Use Exponential smoothing forecasts with alpha of 0.1, 0.2, ..., 0.9 to predict January 2021 demand.Identify the value of alpha that results in the lowest MAD.

5-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 seasonal indices.

MGT 3332 Forecasting Dataset Summer 2021 Month/Yr. PERIOD PRICE an. 2016 AIP DIFF ADV 5.4 DEMAND 5.9 0.5 5.3 2 5.5 13.9 6.6 1.1 6.8 14.5 6.0 6.4 0.4 7.3 6.1 14. 6.1 0.0 7.3 5.9 14.9 6.4 0.5 6 7.2 5.9 14.9 5.3 0.4 7 5.5 5.9 14.6 6.0 0.1 8 6.8 5.8 14.1 6.0 9 -0.8 5.0 12.0 6.8 5.8 -1.0 10 5.8 6.4 14.2 6.3 -0.1 11 5.5 6.5 13.9 5.3 -0.2 6.5 12 6.3 13.9 an. 2017 6.2 -0.1 13 6.3 5.1 13.8 5.5 0.4 7.0 14 6.1 14.0 6.6 0.5 15 7.7 14.5 6.0 6.3 0.3 5.8 16 5.4 16.0 6.7 0.3 17 6.8 6.2 15.7 6.5 0.3 18 7.1 5,0 15.8 5.8 0.8 7.0 19 6.1 15.2 6.6 0.5 7.2 20 5.4 15.9 6.1 -0.3 7.5 21 6.0 16.2 6.1 0.1 7.8 22 15.0 5.2 6.2 D.O 8.2 23 16.9 5.1 6.0 -0.1 8.3 24 6.0 17.1 an. 2018 6.2 0.2 8.4 25 6.1 16.9 6.7 D.16 8.9 26 5.9 17.4 6.9 1.O 27 9.1 5.0 17.7 5.8 -0.2 9.3 28 17.6 5.3 5.8 -0.5 9.4 29 18.4 6.0 6.0 0.0 30 9.3 5.7 18.6 6.7 1.0 9.4 31 5.6 17.4 6.4 0.8 32 9.5 5.2 18.4 7.0 0.8 9.6 33 6.4 17.6 7.2 0.8 9.7 34 6.5 16.7 5.9 -0.6 9.9 35 6.2 18.2 6.0 -0.2 9.8 36 18.5 5.7 6.2 Jan. 2019 0.5 9.9 37 19.1 6.9 6.0 -0.9 10.1 38 19.0 5.9 5.3 -0.6 10.2 39 19.0 5.7 6.5 -0.2 10.5 40 19.8 7.0 6.0 -1.0 10.3 41 7,1 19.8 6.1 -1.0 9.9 42 20.0 7.2 6.3 -0.9 10.5 43 20.9 7.2 5.4 -0.8 10.6 44 19.6 7.3 6.5 -0.8 10.5 45 19.5 7.2 5.0 -1.2 11.6 46 18.4 7.1 6.2 -0.9 10.1 47 19.3 6.9 5.9 -1.0 10.3 18 19.3 7.2 5.0 Jan. 2020 -1.2 10.7 19.9 49 7.3 6.4 -0.9 10.9 50 20.0 7.4 5.5 -0.9 51 10.8 20.1 7.5 6.5 -1.0 11.1 52 20.1 7.0 6.2 -0.8 11.2 53 20.2 5.8 5.8 D.O 11.6 54 21.1 7.4 6.9 -0.5 11.5 55 20.6 7.3 5.5 -0.8 11.6 56 20.7 7.3 6.9 -0.4 57 11.9 7.2 21.3 7.0 -0.2 11.8 58 7.5 21.4 6.8 -0.7 11.9 59 7.5 21.5 6.8 -0.7 12.0 50 7.5 21.8 5.5 an. 2021 -1.0 11.9 61 21.5 Feb. 2021 62 Mar. 2021 53