Loading presentation...

Present Remotely

Send the link below via email or IM

Copy

Present to your audience

Start remote presentation

  • Invited audience members will follow you as you navigate and present
  • People invited to a presentation do not need a Prezi account
  • This link expires 10 minutes after you close the presentation
  • A maximum of 30 users can follow your presentation
  • Learn more about this feature in our knowledge base article

Do you really want to delete this prezi?

Neither you, nor the coeditors you shared it with will be able to recover it again.

DeleteCancel

Make your likes visible on Facebook?

Connect your Facebook account to Prezi and let your likes appear on your timeline.
You can change this under Settings & Account at any time.

No, thanks

Forecasting Supply Chain Demand: Starbucks Corporation

No description
by

Chad McKerlie

on 8 April 2014

Comments (0)

Please log in to add your comment.

Report abuse

Transcript of Forecasting Supply Chain Demand: Starbucks Corporation

Forecasting Supply Chain Demand: Starbucks Corporation
Colwick, Kevin
Le, Thanh Ha
Makki, Amr
McKerlie, Chad
Patel, Niralee
Thapa, Aditi

Team 2
Starbucks logo branded coffee maker

It is distributed from five distribution centers in the US

They want to simplify the supply chain

New system is programmed to use one of two forecasting methods:
Simple Moving Average
Exponential Smoothing
Simple Moving Average
What is it?
Three Weeks and Five Weeks of Data

Evaluation Methods
Mean Absolute Deviation
Mean Absolute Percent Error
Tracking Signal

Simple Moving Average Results:
3-Week data is less biased
28% Error from Actual Demand


Three Weeks and Five Weeks of Data

No need to continually carry large amounts of historical data

Most used of all forecasting techniques

Exponential Smoothing Results:
.4 alpha-value is less biased
26.3 % Error from Actual Demand



Exponential Smoothing
Future Considerations
Exponential Smoothing is the Forecasting Method of Choice
Provides more accurate results
Easy to use and requires little computation

Starbucks should consolidate to a single distribution center
Uniform data
Lower maintenance cost
In Conclusion
Any Questions?
Full transcript