I need help with my supply chain assigement

28 Jun I need help with my supply chain assigement

1. Sales data for two years are as follows. Data are aggregated with two months of sales (in 1,000 units) in each “period.”

a) Plot the data.

b) Fit a linear regression model to the sales data.

c) In addition to the regression model, determine multiplicative seasonal index factors. A full cycle is assumed to be a full year.

d) Using the results from parts b) and c), prepare a forecast for the next year.

2. Zeus Computer Chips Inc. used to have major contracts to produce the Centrino-type chips. Here is demand over the past 12 quarters:

2014 2015 2016

I 4,800 I 3,500 I 3,200

II 3,500 II 2,700 II 2,100

III 4,300 III 3,500 III 2,700

IV 3,000 IV 2,400 IV 1,700

a) Fit a linear regression model with an additive form (using dummy variables) to forecast the four quarters of 2017.

b) Use the decomposition technique to forecast the four quarters of 2017.

3. The demand manager of Maverick Jeans is responsible for ensuring sufficient warehouse space for the finished jeans that come from the production plants. In order to estimate the space requirements the demand manager is evaluating moving-average forecasts. The demand (in 1,000 case units) for the last fiscal year is shown below.

a) Use a three-month moving average to estimate the month-in-advance forecast of demand for months 4–12 and generate a forecast for the first month of next year. Calculate mean absolute deviation (MAD).

b) Use a three-month weighted moving average with weights of 0.6, 0.3, 0.1 (most recent to last recent, respectively) to calculate month-in-advance forecasts for months 4–12 and forecast for the first month of next year. Calculate the MAD.

c) Use an exponential smoothing method with a starting forecast of 20 for month 1 and a smoothing constant α = 0.5 to calculate month-in-advance forecasts for months 4–12 and forecast for the first month of next year. Calculate the MAD.

d) Compare the MAD for the forecasting methods in parts a) – c). Based on these error calculations, which of the three forecast methods would you recommend?

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