Operation management

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	126	January–February	172
March–April	152	March–April	151
May–June	165	May–June	204
July–August	184	July–August	238
September–October	167	September–October	189
November–December	123	November–December	149

1. Plot the data.
2. Fit a linear regression model to all the sales data.
3. In addition to the regression model, determine multiplicative seasonal index factors. A full cycle is assumed to be a full year.
4. Using the results from parts b) and c), prepare a forecast for the next year.

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

Year	2016		2017		2018
I	5700	I	4400	I	3400
II	4000	II	3100	II	2800
III	4900	III	4400	III	2700
IV	3700	IV	3100	IV	2000

Fit all the data above by a linear regression model with an additive form (using dummy variables) to forecast the four quarters of 2019.

• 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.

Month	1	2	3	4	5	6	7	8	9	10	11	12
Demand	23	26	24	28	24	30	24	20	31	22	27	31

1. 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).
2. Use an exponential smoothing method with a starting forecast of 21 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.
3. Compare the MAD for the forecasting methods in parts a) and b). Based on these error calculations, which of the two forecast methods would you recommend?

 

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