Starbucks has a large, global supply chain that must efficiently supply over 34,000 stores. Although the stores might appear to be very similar, they are actually very different. Depending on the location of the store, its size, and the profile of the customers served, Starbucks management configures the store offerings to take maximum advantage of the space available and customer preferences.

Starbucks' actual distribution system is much more complex, but for the purpose of our exercise, let's focus on a single item that is currently distributed through five distribution centers in the United States. Our item is a logo-branded coffeemaker that is sold at some of the larger retail stores. The coffeemaker has been a steady seller over the years due to its reliability and rugged construction. Starbucks does not consider this a seasonal product, but there is some variability in demand. Demand for the product over the past 13 weeks is shown in the following table. (week 1 is the week before week 1 in the table, 2 is two weeks before week 1, etc.).

Management would like you to experiment with some forecasting models to determine what should be used in a new system to be implemented. The new system is programmed to use one of two forecasting models: simple moving average or exponential smoothing.

WEEK	5	4	3	2	1	1	2	3	4	5	6	7	8	9	10	11	12	13
Atlanta	44	36	28	55	36	32	45	38	37	54	28	17	57	48	36	24	54	42
Boston	61	20	48	40	36	27	34	39	42	45	48	54	19	61	45	30	45	50
Chicago	61	24	74	43	48	45	33	20	54	48	72	62	28	26	96	34	46	48
Dallas	41	36	40	64	44	28	42	35	40	50	62	70	65	54	40	38	47	40
Los Angeles	42	40	50	45	35	32	42	53	40	46	72	40	35	45	38	48	55	50
Total	249	156	240	247	199	164	196	185	213	243	282	243	204	234	255	174	247	230

Consider using a simple moving average model. Experiment with models using five weeks' and three weeks' past data.

Note: Round your answers to 2 decimal places.

3-week Moving Average Week 1-2 - Atlanta, Boston, Chicago, Dallas, Los Angeles, Total