Analysis and implementation of laboratory automation system

Quest Diagnostics is a large company that analyzes millions of medical specimens every day using a variety of analytical equipment. It is implementing a fully automated line in a major laboratory. The automation line is Quest's first major automation initiative and will serve as a pilot for future initiatives. An important element of implementing this initiative is the transition from manual to automated specimen handling operations. Furthermore, it is crucial to model the behavior of the newly automated production system. This thesis discusses the risk analysis performed on the transition from manual operations into automated as well as a discrete event simulation developed to model the automated system in its planned final state. The risk analysis was performed by identifying the risks of the transition of each affected analyzer and scoring each risk based on its severity and probability of occurrence. A reduction factor was added for analyzers that were to be transitioned later in the sequence schedule to account for the ability of the team to learn with each equipment transition. The simulation was based on existing process flow diagrams and populated with data from the two largest labs to be consolidated in Marlborough, MA, which comprise 90% of the expected volume. The simulation results were then compared to a previously performed static simulation and to current data from the Marlborough lab, which is now operational. This revealed a discrepancy between the simulation and the current data in terms of total specimens processed at each analyzer. This is attributed to the differences between the current manual process and the expected automated process. The dynamic model shows that the planned automation line can support the expected specimen volume even with 10% reduction in equipment efficiency. A planned 20% increase in volume was also evaluated along with its associated increase in capacity. The automation line can support the higher volume with the planned increase in capacity. Although these results are promising, further work is needed to validate the model results once the automation system is fully operational.

Department dc:contributor.department

Leaders for Global Operations Program at MIT

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2015

Author dc:creator

• Secundo, Rafael Garcia

Advisor dc:contributor.advisor

• Daniel E. Whitney and Itai Ashlagi.

• Mechanical 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/1721.1/106726

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/106726