Using analytics to improve delivery performance

Delivery Precision is a key performance indicator that measures Nike's ability to deliver product to the customer in full and on time. The objective of the six-month internship was to quantify areas in the supply chain where the most opportunities reside in improving delivery precision. The Nike supply chain starts when a new product is conceived and ends when the consumer buys the product at retail. In between conception and selling, there are six critical process steps. The project has provided a method to evaluate the entire supply chain and determine the area that has the most opportunity for improvement and therefore needs the most focus. The first step in quantifying the areas with the most opportunity was to identify a framework of the supply chain. The framework includes the target dates that must be met in order to supply product to the customer on schedule and the actual dates that were met. By comparing the target dates to the actual dates, the area of the supply process that caused the delay can be identified. Next a data model was created that automatically compares the target dates to actual dates for a large and specified set of purchase orders. The model uses the framework and compiles all orders to quantify the areas in the supply chain that create the most area for opportunity. The model was piloted on the North America geography, Women's Training category, Apparel product engine, and Spring 2013 season, for orders shipped to the Distribution Center (DC). The pilot showed that the most area for opportunity lies in the upstream process (prior to the product reaching the consolidator). In particular the pilot showed that the area with the most opportunity for the sample set was the PO create process. This conclusion was also confirmed with the Running category. The method developed during the internship provides Nike with a method to measure the entire supply chain. By quantifying the areas in the process, Nike can focus and prioritize their efforts on those areas that need the most improvement. In addition the model created can be scaled for any region, category, or product engine to ultimately improve delivery precision across the entire company.

Department dc:contributor.department

Leaders for Global Operations Program at MIT

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2014

Author dc:creator

• Napolillo, Tacy J. (Tacy Jean)

Advisor dc:contributor.advisor

• Stephen Graves and David Simchi-Levi.

• Sloan School of Management.
• Engineering Systems Division.
• Leaders for Global Operations Program.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/90791

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/90791