Question
Starbucks has a large, global supply chain that must efficiently supply over 17,000 stores. Although the stores might appear to be very similar, they are
Starbucks has a large, global supply chain that must efficiently supply over 17,000 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 lets 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 18 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 | 35 | 34 | 33 | 58 | 32 | 32 | 46 | 35 | 33 | 54 | 30 | 20 | 58 | 46 | 35 | 26 | 57 | 42 |
Boston | 58 | 29 | 49 | 45 | 33 | 34 | 33 | 45 | 42 | 46 | 49 | 55 | 21 | 64 | 45 | 33 | 43 | 53 |
Chicago | 53 | 24 | 62 | 40 | 40 | 45 | 33 | 26 | 50 | 47 | 65 | 65 | 30 | 25 | 95 | 34 | 44 | 48 |
Dallas | 36 | 30 | 34 | 55 | 40 | 28 | 28 | 35 | 38 | 47 | 60 | 68 | 62 | 45 | 40 | 35 | 46 | 43 |
LA | 42 | 42 | 46 | 38 | 36 | 36 | 42 | 44 | 46 | 46 | 66 | 42 | 35 | 39 | 42 | 45 | 50 | 50 |
Total | 224 | 159 | 224 | 236 | 181 | 175 | 182 | 185 | 209 | 240 | 270 | 250 | 206 | 219 | 257 | 173 | 240 | 236 |
a. Consider using a simple moving average model. Experiment with models using five weeks and three weeks past data. (Round your answers to 2 decimal places.)
3-week MA
Week | ATL | BOS | CHI | DAL | LA | Total |
1 | ||||||
2 | ||||||
3 | ||||||
4 | ||||||
5 | ||||||
6 | ||||||
7 | ||||||
8 | ||||||
9 | ||||||
10 | ||||||
11 | ||||||
12 | ||||||
13 | ||||||
|
5-week MA
Week | ATL | BOS | CHI | DAL | LA | Total |
1 | ||||||
2 | ||||||
3 | ||||||
4 | ||||||
5 | ||||||
6 | ||||||
7 | ||||||
8 | ||||||
9 | ||||||
10 | ||||||
11 | ||||||
12 | ||||||
13 | ||||||
b. Evaluate the forecasts that would have been made over the 13 weeks using the overall (at the end of the 13 weeks) mean absolute deviation, mean absolute percent error, and tracking signal as criteria. (Round your answers to 2 decimal places. Negative values should be indicated by a minus sign.)
ATL | BOS | CHI | DAL | LA | Avg of DCs | ||
3-week MA | MAD | ||||||
MAPE | |||||||
TS | |||||||
5-week MA | MAD | ||||||
MAPE | |||||||
TS |
Step by Step Solution
There are 3 Steps involved in it
Step: 1
Get Instant Access to Expert-Tailored Solutions
See step-by-step solutions with expert insights and AI powered tools for academic success
Step: 2
Step: 3
Ace Your Homework with AI
Get the answers you need in no time with our AI-driven, step-by-step assistance
Get Started