Improving lead time setting and on-time delivery commitments under uncertain supply conditions

As Dell seeks to continually improve customer experience, the company is identifying new and innovative ways to improve on-time delivery. Inventory shortages that occur prior to production account for approximately 35% of missed delivery dates. When these part shortages occur, demand planners must apply "extended" lead times to these parts to ensure that Dell's customers have the correct expectation for when their order will be delivered. This project focuses on part shortage problems and how to generate accurate lead times for customers commitments. Previous research on the topic on lead time setting has focused predominately on buffering and measuring uncertainty in supply chains, which detail the benefits of having appropriate levels of safety stock and flexibility. However, prior research does not adequately describe methods for adjusting product lead times under uncertain supply conditions. The project develops a deterministic model for identifying when parts in Dell's supply chain require lead time adjustments due to supply shortages and then for setting the new lead times. Additionally, this project includes a statistical analysis of previous extended lead time events. After a five-week testing period, the deterministic model was quite accurate in identifying what parts require extended lead times. This offers a 3% improvement in identifying when extended lead times are needed as it decreases human error in missed and late lead time extensions. Predominant sources of error resulted from backlog management issues, part deviations in production, and miscellaneous data errors. The statistical analysis yields two insights into part recovery in Dell's supply chain: (1) larger volume shortages take shorter time to recover than small volume shortages, and (2) approximately 80% of all part shortages recover within 10 days. This research offers valuable insight into the problems associated with lead times in Dell's supply chain and recommends ways to best mitigate these errors. As Dell develops more robust and comprehensive databases on its inventory, future research can identify methods to accurately and automatically update lead times in real-time.

Department dc:contributor.department

Leaders for Global Operations Program at MIT

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Balent, Zachariah (Zachariah Francis)

Advisor dc:contributor.advisor

• David Simchi-Levi and Stephen Graves.

• Civil and Environmental Engineering.
• Sloan School of Management.
• Leaders for Global Operations Program.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written 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/1721.1/119330

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/119330