Algorithmic intervention to mitigate inventory and ordering amplification in multi-echelon supply chains

The 'bullwhip effect' is a classic, yet persisting, problem with reverberating consequences in inventory management and refers to how forecast errors and safety stock builds yield increasing amplitudes in both orders and on-hand inventory positions the further one moves away from a source of order variability. The bullwhip effect is responsible for both excessive strain on real world inventory management systems, stock outs, and unnecessary capital reservation though safety stock building. In this paper, the author develops algorithmic approaches to mitigating bullwhip using simulation modeling, including cost minimization and amplification minimization, and then interprets the results in the context of existing models of human heuristics in ordering decisions. The algorithmic approaches are optimized as one member within a model of a human decision makers operating within a multi-echelon supply chain with imperfect information sharing and information delays.

Department dc:contributor.department

Sloan School of Management

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Paine, James Edward.

Advisor dc:contributor.advisor

• Hazhir Rahmandad and David R. Keith.

• Sloan School of Management.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

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

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/129114

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/129114