Open AccessImproving the Production Process for a Medical Device Manufacturing Company using Monte Carlo Simulation
Author(s) -
Jazmin Furtado,
Taiylar Mastey,
Sara Menke,
Gerardo González,
Jesse Pietz,
Joseph Wilck
Publication year - 2016
Publication title -
industrial and systems engineering review
Language(s) - English
Resource type - Journals
ISSN - 2329-0188
DOI - 10.37266/iser.2016v4i1.pp45-53
Subject(s) - process (computing) , production (economics) , computer science , product (mathematics) , quality (philosophy) , salary , work (physics) , operations management , order (exchange) , manufacturing engineering , business , operations research , engineering , mechanical engineering , mathematics , economics , philosophy , geometry , epistemology , finance , macroeconomics , operating system , market economy
This work studies a medical product development, assembly, and packaging company that uses a paper-based method to track jobs through their assembly process. The paperwork often has errors, which must be corrected before distribution, causing delays in manufacturing and shipment and resulting in lost time and money. The project team analyzed the company's production process to identify areas for improvement. Through statistical analyses of the company’s process data, the team established error categorization, location, and probability of occurrence. In order to address sources of error, the project team diagrammed the company's manufacturing floor and created a list of issues within each step of the manufacturing process as well as potential solutions to these problems. The team created a simulation of the manufacturing process and used this tool to analyze potential process changes to decrease the number of employee hours wasted in order to fix discrepancies. The simulation proved to be an invaluable tool that helped the company better understand their process. It helped to identify which jobs create the most errors, how many errors occur per month, and how much money the company loses on time spent correcting the errors. Eleven potential solutions were considered, but two of them appeared to yield the best results. Implementing a total quality management (TQM) system would conservatively reduce error counts by 71.3%. Implementing a start quantity to the company's electronic system would conservatively reduce mean hours wasted from 22.60 to 21.73 hours per month and mean salary lost from $519.82 to $499.80 per month. Using insights from the simulation, the project team then coordinated with management to decide whether error rates or time and salary spent to correct errors were more important to address. As of this writing, as a result of this study, the company is taking steps to implement a TQM system in order to decrease errors in their job tracking process.