2026-09-20T07:16:05Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/995722022-01-28T15:36:38Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Stephen Graves and David Simchi-Levi.
Wu, Yalu
Leaders for Global Operations Program.
Leaders for Global Operations Program at MIT
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Sloan School of Management
2015-10-30T18:56:37Z
2015-10-30T18:56:37Z
2015
2015
http://hdl.handle.net/1721.1/99572
924815866
Thesis: S.M., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2015. In conjunction with the Leaders for Global Operations Program at MIT.
Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2015. In conjunction with the Leaders for Global Operations Program at MIT.
Cataloged from PDF version of thesis.
Includes bibliographical references (page 69).
Demand Planning forecasts at Nike, Inc. are used by many groups: Supply Planning/Materials Planning, Sourcing, Categories/Merchandising, Finance, S&OP, and Sales. These groups take forecasts as an input to make key decisions. Forecasts, by nature, will be inaccurate. There are two big unknowns to answer as Nike considers how to improve forecast accuracy: 1) how accurate can or should forecasts become (target setting) and 2) what are the causes and impacts of inaccuracy. However, the first step to addressing these questions is to understand and measure forecast accuracy metrics in a consistent way across Nike's various Demand Planning groups. This project investigates the following through the design of a Tableau dashboard * which metrics should be reviewed (accuracy, bias, volatility, etc.) * how they should be computed (what to compare) * at what level of aggregation for which groups * at what level of detail for which groups (category, classification, etc.) * over how many seasons * with which filters In addition to aligning on forecast accuracy metrics, the project also focuses on the dashboard design (determining the most appropriate structure/views, how information is laid out or presented, and the use of labels and color) and on setting the long-term vision for viewing and using forecast accuracy metrics through researching and outlining the process for root cause analysis and target setting.
by Yalu Wu.
S.M.
M.B.A.
69 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Civil and Environmental Engineering.
Sloan School of Management.
Leaders for Global Operations Program.
A Framework for Analyzing Forecast Accuracy Metrics
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="4e177c19-4633-430a-bfbf-5bb984c258ea">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>A Framework for Analyzing Forecast Accuracy Metrics</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2015</PublicationDate>
<Authors>
<Author>
<DisplayName>Wu, Yalu</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Civil and Environmental Engineering.</Keyword>
<Keyword>Sloan School of Management.</Keyword>
<Keyword>Leaders for Global Operations Program.</Keyword>
<Abstract>Demand Planning forecasts at Nike, Inc. are used by many groups: Supply Planning/Materials Planning, Sourcing, Categories/Merchandising, Finance, S&OP, and Sales. These groups take forecasts as an input to make key decisions. Forecasts, by nature, will be inaccurate. There are two big unknowns to answer as Nike considers how to improve forecast accuracy: 1) how accurate can or should forecasts become (target setting) and 2) what are the causes and impacts of inaccuracy. However, the first step to addressing these questions is to understand and measure forecast accuracy metrics in a consistent way across Nike's various Demand Planning groups. This project investigates the following through the design of a Tableau dashboard * which metrics should be reviewed (accuracy, bias, volatility, etc.) * how they should be computed (what to compare) * at what level of aggregation for which groups * at what level of detail for which groups (category, classification, etc.) * over how many seasons * with which filters In addition to aligning on forecast accuracy metrics, the project also focuses on the dashboard design (determining the most appropriate structure/views, how information is laid out or presented, and the use of labels and color) and on setting the long-term vision for viewing and using forecast accuracy metrics through researching and outlining the process for root cause analysis and target setting.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>