Determining Kanban Size Using Mathematical Programming and Discrete Event Simulation for a Manufacturing System with Large Production Variability

<p>In order to become more competitive and aggressive in the marketplace it is imperative for manufacturers to reduce cycle time, limit work-in-process, and improve productivity, responsiveness, capacities, and quality. One manner in which supply chains can be improved is via the use of kanbans in a pull production system. Kanbans refer to a card or signal for productions scheduling within just-in-time (JIT) production systems to signal where and what to produce, when to produce it, and how much. A Kanban based JIT production system has been shown to be beneficial to supply chains for they reduce work-in-process, provide real time status of the system, and enhance communication both up and down stream.</p> <p>While many studies exist in regards to determining optimal number of kanbans, types of kanban systems, and other factors related to kanban system performance, no comprehensive model has been developed to determine kanban size in a manufacturing system with variable workforce production rate and variable demand pattern. This study used Stewart-Marchman-Act, a Daytona Beach rehabilitation center for those with mental disabilities or recovering from addiction that has several manufacturing processes, as a test bed sing mathematical programming and discrete event simulation models to determine 2 the Kanban size empirically. Results from the validated simulation model indicated that there would be a significant reduction in cycle time with a kanban system; on average, there would be a decrease in cycle time of nine days (almost two weeks). Results were discussed and limitations of the study were presented in the end.</p>

Name thesis:degree_name

Master of Science in Human Factors & Systems

Level thesis:degree_level

Thesis - Open Access

Discipline thesis:degree_discipline

Human Factors and Systems

Year

2012

Author dc:creator

• Gaston, Abigail Michele

• kanban
• discrete event simulation
• manufacturing systems
• production
• Industrial Engineering
• Psychology

Repository record dc:identifier

https://commons.erau.edu/edt/69

OAI identifier oai:identifier

oai:commons.erau.edu:edt-1068