Reference no: EM131395882

Assignment 1

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

                          

Year 1		Year 2
Period	Sales	Period	Sales
January-February	115	January-February	124
March-April	112	March-April	132
May-June	159	May-June	168
July-August	182	July-August	203
September-October	126	September-October	135
November-December	106	November-December	123

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 demandover 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 suf?cient warehouse spacefor the ?nished jeans that come from the production plants. In order to estimatethe space requirements the demand manager is evaluating moving-average forecasts. Thedemand (in 1,000 case units) for the last ?scal year is shown below.

Month	1	2	3	4	5	6	7	8	9	10	11	12
Demand	20	18	21	25	24	27	22	30	23	20	29	22

a) Use a three-month moving average to estimate the month-in-advance forecast of demand formonths 4-12 and generate a forecast for the ?rst 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 tolast recent, respectively) to calculate month-in-advance forecasts for months 4-12 andforecast for the ?rst month of next year. Calculate the MAD.

c) Use anexponential smoothing method witha starting forecast of 20 for month 1 and a smoothing constantα = 0.5to calculate month-in-advance forecasts for months 4-12 and forecast for the ?rst 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?