Cost of complexity : mitigating transition complexity in mixed-model assembly lines

The Nissan Smyrna automotive assembly plant is a mixed-model production facility which currently produces six different vehicle models. This mixed-model assembly strategy enables the production level adjustment of different vehicles to match changing market demand, but it necessitates a trained workforce who are familiar with the different parts and processes required for each vehicle. Currently, the mixed-model production process is not batched; assembly line technicians might switch between assembling different vehicles several times every hour. When a switch or 'transition' occurs between different models, variations in the defect rate could occur as technicians must familiarize themselves with a different set of parts and processes. This thesis identifies this confusion as the consequence of 'transition' complexity, which results not only from variety but also familiarity; how quickly can a new situation be recognized, and how quickly can associates remember what to do and recover the skills needed to succeed. Recommendations follow to mitigate the impact of transition complexity on associate performance, thereby improving vehicle production quality. Transition complexity is an important factor in determining the performance of the assembly system (with respect to defect rates) and could supplement existing models of complexity measurement in assembly systems. Several mitigation measures at the assembly plant level are recommended to limit the impact of transition complexity on system performance. These measures include improvements to the offline kitting system to reduce errors such as reconfiguring the physical layout and implementing a visual error detection system. Additionally, we recommend altering the production scheduling system to ensure low volume models are produced at more regular intervals and with consistently low sequence gaps.

Name thesis:degree_name

Master

Department dc:contributor.department

Sloan School of Management

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Addy, Robert.

Advisor dc:contributor.advisor

• Sang-Gook Kim and Steven Spear.

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

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/1721.1/126942

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/126942