1. A company wants to forecast demand using the simple moving average. If the company uses four prior yearly sales values (i.e., year 2014 = 100, year 2015 = 120, year 2016 = 140, and year 2017 = 210), which of the following is the simple moving average forecast for year 2018?

A) 100.5

B) 140.0

C) 142.5

D) 145.5

E) 155.0

2. A company wants to forecast demand using the weighted moving average. If the company uses three prior yearly sales values (i.e., year 2015 = 160, year 2016 = 140 and year 2017 = 170), and we want to assign a weight of 2 for year 2015, 3 for year 2016 and 4 for year 2017, which of the following is the weighted moving average forecast for year 2018?

3. Given a prior forecast demand value of 230, a related actual demand value of 250, and a smoothing constant alpha of 0.1, what is the exponential smoothing forecast value for the following period?

A) 230

B) 232

C) 238

D) 248

E) 250

4. As a consultant you have been asked to generate a unit demand forecast for a product for year 2018 using exponential smoothing. The actual demand in year 2017 was 750. The forecast demand in year 2017 was 960. Using this data and a smoothing constant alpha of 0.3, which of the following is the resulting year 2018 forecast value?

A) 766

B) 813

C) 897

D) 1,023

E) 1,120

5. Mikki runs a shop and she wishes to determine quarterly seasonal indices based on last year's sales which are shown in the table below,

Summer	Autumn	Winter	Spring
920	1085	1241	446

1. Please help Mikki to calculate the seasonal index for each season.
2. If this year's total sales are expected to be 3800 units, please forecast the sales for each season by using the indices calculated in question (1).

6. Please forecast the demand for each quarter of the year (2013) by computing trend and seasonal indices. The demand data for the past two years (2011 and 2012) is shown in the table below. (the outputs of linear regression are given below)

1. What is the estimated trend line, i.e., Forecast including trend (FIT)?
2. What is the seasonal index for each season? (please refer to slide 32)?
3. What is the forecasted demand for each season in year 2013 (please refer to slide 33)

	Season	time period	Demand
Year 2011	Spring	1	228
Summer	2	181
Fall	3	377
Winter	4	577
Year 2012	Spring	5	524
Summer	6	289
Fall	7	718
Winter	8	991
Year 2013	Spring	9
Summer	10
Fall	11
Winter	12

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.818151
R Square	0.669372
Adjusted R Square	0.614267
Standard Error	170.7326
Observations	8
ANOVA
	df	SS	MS	F	Significance F
Regression	1	354088.1	354088.1	12.14726	0.013058
Residual	6	174897.8	29149.63
Total	7	528985.9
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	72.4202	133.0338	0.544374	0.605808	-253.102	397.9421	-253.102	397.9421
time index	91.81868	26.34462	3.485292	0.013058	27.35573	156.2816	27.35573	156.2816

7. Please forecast the demand for each quarter of the year (2013) by computing trend and seasonal indices. The demand data for the past two years (2011 and 2012) is shown in the table below.

	Season	time period	Demand
Year 2011	Spring	1	205
Summer	2	140
Fall	3	375
Winter	4	575
Year 2012	Spring	5	475
Summer	6	275
Fall	7	685
Winter	8	965
Year 2013	Spring	9
Summer	10
Fall	11
Winter	12

Using trend and seasonal indexes to forecast the demand data for each quarter of year 2013

(please refer to slides 25-27, 31-33 for reference)

8. (Quantity Discount Model) The annual demand for an item is 40,000 units. The cost to process an order is $40 and the annual inventory holding cost is 120% of the unit price per item per year. What is the optimal order quantity, given the following price breaks for purchasing the item?

Quantity	Price
1-1,499	$2.50 per unit
1,500 - 4,999	$2.30 per unit
5,000 or more	$2.25 per unit

a. What is the optimal behavior?

b. Does the firm take advantage of the lowest price available? Explain.

9. If a vendor has correctly used marginal analysis to select their stock levels for the day (as in the newsperson problem), and the profit resulting from the last unit being sold (Cu) is $0.90 and the loss resulting from that unit if it is not sold (Co) is $0.50, which of the following is the probability of the last unit being sold?

A) Greater than 0.357

B) Greater than 0.400

C) Greater than 0.556

D) Greater than 0.678

E) None of these

10. If a vendor has correctly used marginal analysis to select their stock levels for the day (as in the newsperson problem), and the profit resulting from the last unit being sold (Cu) is $120 and the loss resulting from that unit if it is not sold (Co) is $360, which of the following is the probability of the last unit being sold?

A) Greater than 0.90

B) Greater than 0.85

C) Greater than 0.75

D) Greater than 0.25

E) None of these

11. Demand for organic super fresh lettuce in a local supermarket follows a normal distribution with mean 8 units and standard deviation 3 units. Unsold lettuces are given to a food bank with a salvage value of $1.00 per lettuce. Cost of the lettuce is $3.00 per unit. Selling price is $6.00 per unit. Using single period model, what is the optimal order quantity in number of units of lettuce the supermarket should use?

12. Daily demand for fresh cauliflower in the ZZ-Warehouse store follows normal distribution with mean 100 cartons and s.d. 20 cartons. The ZZ-Warehouse buys at a cost of $50.00 per carton, sells it for $70.00 per carton. Unsold cartons are sold for $20.00 per carton. What is the optimal order quantity, using the single period model?

A) 100

B) 80

C) 95

D) 110

E) 105