SUPPLY CHAIN MANAGEMENT

1.

Corporate triple-A bond interest rates for 12 consecutive months are as follows.

9.59.49.59.79.89.89.810.610.09.89.79.7

(a)

Construct a time series plot. What type of pattern exists in the data?

The data appear to follow a trend pattern.

The data appear to follow a cyclical pattern.

The data appear to follow a seasonal pattern.

The data appear to follow a horizontal pattern.

(b)

Develop three-month and four-month moving averages for this time series. (Round your answers to two decimal places.)

MonthTime Series Value3-Month Moving Average Forecast4-Month Moving Average Forecast19.529.439.549.7..59.8..69.8..79.8..810.6..910.0..109.8..119.7..129.7..

Does the three-month or four-month moving average provide the better forecasts based on MSE? Explain.

The four-month moving average provides more accurate forecasts, because its MSE is smaller than that of the three-month moving average.

The three-month moving average provides more accurate forecasts, because its MSE is smaller than that of the four-month moving average.

The three-month moving average provides more accurate forecasts, because its MSE is larger than that of the four-month moving average.

The four-month moving average provides more accurate forecasts, because its MSE is larger than that of the three-month moving average.

(c)

Using the more accurate approach, what is the moving average forecast for the next month? (Round your answer to two decimal places.) .

• Show My Work

2.

The president of a small manufacturing firm is concerned about the continual increase in manufacturing costs over the past several years. The following figures provide a time series of the cost per unit for the firm's leading product over the past eight years.

YearCost/Unit ($)YearCost/Unit ($)120.10526.70224.60630.10328.10730.90427.60835.90

(a)

Construct a time series plot. What type of pattern exists in the data?

The time series plot shows a nonlinear trend.

The time series plot shows a seasonal pattern.

The time series plot shows a horizontal pattern.

The time series plot shows a linear trend.

(b)

Use simple linear regression analysis to find the parameters for the line that minimizes MSE for this time series. (Round your answers to two decimal places.)

b₀=.

b₁=.

(c)

What is the average cost increase (in $) that the firm has been realizing per year? (Round your answer to two decimal places.)

$.

(d)

Compute an estimate of the cost/unit (in $) for the next year. (Round your answer to two decimal places.)

$.

• Show My Work

3.

DATAFile:IceCreamSales (See below)

Hogs & Dawgs is an ice cream parlor on the border of north-central Louisiana and southern Arkansas that serves 43 flavors of ice creams, sherbets, frozen yogurts, and sorbets. During the summer Hogs & Dawgs is open from 1:00 p.m. to 10:00 p.m. on Monday through Saturday, and the owner believes that sales change systematically from hour to hour throughout the day. She also believes that her sales increase as the outdoor temperature increases. Hourly sales (in $) and the outside temperature (in Fahrenheit) at the start of each hour for the last week are provided in the file IceCreamSales.

(a)

Construct a time series plot of hourly sales and a scatter plot of outdoor temperature and hourly sales. What types of relationships exist in the data?

.

.

(b)

Use a simple regression model with outside temperature as the causal variable to develop an equation to account for the relationship between outside temperature (in Fahrenheit) and hourly sales (in $) in the data. (Use the variableTfor outdoor temperature. Round your numerical values to three decimal places.)

=.

Based on this model, compute an estimate of hourly sales for today (in $) from 2:00 p.m. to 3:00 p.m. if the temperature at 2:00 p.m. is95F. (Round your answer to two decimal places.)

$.

(c)

Use a multiple linear regression model with the causal variable outside temperature and dummy variables as follows to develop an equation to account for both seasonal effects and the relationship between outside temperature (in Fahrenheit) and hourly sales (in $) in the data.

x₁=1 if the sales were recorded between 1:00 p.m. and 2:00 p.m., 0 otherwisex₂=1 if the sales were recorded between 2:00 p.m. and 3:00 p.m., 0 otherwisex₈=1 if the sales were recorded between 8:00 p.m. and 9:00 p.m., 0 otherwise

Note that when the values of the eight dummy variables are equal to 0, the observation corresponds to the 9:00-to-10:00-p.m. hour. (Use the variableTfor outdoor temperature. Round your numerical values to three decimal places.)

=.

Based on this model, compute an estimate of hourly sales for today (in $) from 2:00 p.m. to 3:00 p.m. if the temperature at 2:00 p.m. is95F. (Round your answer to two decimal places.)

$.

(d)

Is the model you developed in part (b) or the model you developed in part (c) more effective? Justify your answer.

The model from part (d) is more effective. Since the hour of operation and outside temperature are highly correlated, we should choose the model which uses both variables as predictors, regardless of MSE.

The model from part (b) is more effective. Even though it has a slightly higher MSE, the hour of operation and outside temperature are highly correlated, so we should use the part (b) model since it's simpler.

The model from part (b) is more effective since it has a significantly lower MSE than the more complicated model from part (d).

The model from part (d) is more effective. Even though the hour of operation and outside temperature are highly correlated, part (d)'s model has a slightly lower MSE, so we should use the more complicated model.

The model from part (d) is more effective since it has a significantly lower MSE than the simpler model from part (b).

3.

DATAFile:IceCreamSales (See below)

DayHourTemperatureSalesMonday1:00 P.M. to 2:00 P.M.8255.49Monday2:00 P.M. to 3:00 P.M.8361.89Monday3:00 P.M. to 4:00 P.M.8744.79Monday4:00 P.M. to 5:00 P.M.9368.62Monday5:00 P.M. to 6:00 P.M.9558.82Monday6:00 P.M. to 7:00 P.M.9651.85Monday7:00 P.M. to 8:00 P.M.9366.2Monday8:00 P.M. to 9:00 P.M.8952.89Monday9:00 P.M. to 10:00 P.M.8661.95Tuesday1:00 P.M. to 2:00 P.M.8659.55Tuesday2:00 P.M. to 3:00 P.M.9047.07Tuesday3:00 P.M. to 4:00 P.M.9253.29Tuesday4:00 P.M. to 5:00 P.M.9647.42Tuesday5:00 P.M. to 6:00 P.M.9960.52Tuesday6:00 P.M. to 7:00 P.M.10071.98Tuesday7:00 P.M. to 8:00 P.M.9755.71Tuesday8:00 P.M. to 9:00 P.M.9464.95Tuesday9:00 P.M. to 10:00 P.M.9360.12Wednesday1:00 P.M. to 2:00 P.M.9048.72Wednesday2:00 P.M. to 3:00 P.M.9466.41Wednesday3:00 P.M. to 4:00 P.M.9665.27Wednesday4:00 P.M. to 5:00 P.M.9854.76Wednesday5:00 P.M. to 6:00 P.M.10048.08Wednesday6:00 P.M. to 7:00 P.M.10353.59Wednesday7:00 P.M. to 8:00 P.M.10162.99Wednesday8:00 P.M. to 9:00 P.M.9866.35Wednesday9:00 P.M. to 10:00 P.M.9567.92Thursday1:00 P.M. to 2:00 P.M.8847.85Thursday2:00 P.M. to 3:00 P.M.9056.62Thursday3:00 P.M. to 4:00 P.M.9246.05Thursday4:00 P.M. to 5:00 P.M.9556.72Thursday5:00 P.M. to 6:00 P.M.9969.94Thursday6:00 P.M. to 7:00 P.M.9965.72Thursday7:00 P.M. to 8:00 P.M.9751.01Thursday8:00 P.M. to 9:00 P.M.9473.51Thursday9:00 P.M. to 10:00 P.M.9265.57Friday1:00 P.M. to 2:00 P.M.9063.26Friday2:00 P.M. to 3:00 P.M.9344.09Friday3:00 P.M. to 4:00 P.M.9665.69Friday4:00 P.M. to 5:00 P.M.9948.62Friday5:00 P.M. to 6:00 P.M.10368.16Friday6:00 P.M. to 7:00 P.M.10567.79Friday7:00 P.M. to 8:00 P.M.10462.79Friday8:00 P.M. to 9:00 P.M.10170.16Friday9:00 P.M. to 10:00 P.M.9968.05Saturday1:00 P.M. to 2:00 P.M.8860.43Saturday2:00 P.M. to 3:00 P.M.9063.78Saturday3:00 P.M. to 4:00 P.M.9267.8Saturday4:00 P.M. to 5:00 P.M.9569.53Saturday5:00 P.M. to 6:00 P.M.9761.8Saturday6:00 P.M. to 7:00 P.M.9957.92Saturday7:00 P.M. to 8:00 P.M.9853.97Saturday8:00 P.M. to 9:00 P.M.9571.55Saturday9:00 P.M. to 10:00 P.M.9257.93

Show My Work