Integrating data analytics and simulation for defect management in manufacturing environments

Faisal Aqlan, Sreekanth Ramakrishnan, Abdulrahman Shamsan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Defect management in manufacturing environments requires effective identification of the defects and finding proper solutions to resolve them. Predicting and preventing the defects before they can occur is the focus of quality risk management. To effectively manage defects, companies need to analyze historical data to identify the causes and solutions for defects as well as study the impact the defect can have on the processes, priorities, and operations. This study integrates data analytics and simulation modeling to develop a system for defect management in manufacturing environments. Simulation is used to analyze the behavior of the system whereas data analytics is used to develop prediction models for defect resolution. A case study from high-end server manufacturing environment, which is characterized by extensive test processes to ensure high quality and reliability of servers, is provided. The proposed approach helps decision makers analyze and manage defects and develop proactive means to prevent them.

Original languageEnglish (US)
Title of host publication2017 Winter Simulation Conference, WSC 2017
EditorsVictor Chan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3940-3951
Number of pages12
ISBN (Electronic)9781538634288
DOIs
StatePublished - Jan 4 2018
Event2017 Winter Simulation Conference, WSC 2017 - Las Vegas, United States
Duration: Dec 3 2017Dec 6 2017

Other

Other2017 Winter Simulation Conference, WSC 2017
CountryUnited States
CityLas Vegas
Period12/3/1712/6/17

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Computer Science Applications

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