In today's competitive manufacturing environment, effective and practical statistical quality control approaches are essential. A successful process control approach needs to provide on line real time monitoring of quality related characteristics.
No longer acceptable is an approach that analyzes quality related data only after the product is produced. A statistical process control approach that monitors the process during production and that reports trouble spots before bad products are made is necessary in an automated manufacturing environment.
An automated manufacturing environment is characterized by high volume production runs and short production cycles. Traditional statistical process control approaches are not capable of dealing with these challenges and cannot keep up with the pace of automated manufacturing.
In this research, a statistical process control approach for automated manufacturing systems is developed. The research demonstrates and evaluates a multivariate cumulative sum control scheme (CUSUM) to a set of standardized data collected from an actual production line.
Results indicate that the statistical process control approach developed is able to detect small variations in the process quickly and effectively. Furthermore, the approach is capable of monitoring several quality characteristics simultaneously in real time. Quality control for short production runs is also addressed in this research.
","abstract_html":"<p>In today's competitive manufacturing environment, effective and practical statistical quality control approaches are essential. A successful process control approach needs to provide on line real time monitoring of quality related characteristics.</p> <p>No longer acceptable is an approach that analyzes quality related data only after the product is produced. A statistical process control approach that monitors the process during production and that reports trouble spots before bad products are made is necessary in an automated manufacturing environment.</p> <p>An automated manufacturing environment is characterized by high volume production runs and short production cycles. Traditional statistical process control approaches are not capable of dealing with these challenges and cannot keep up with the pace of automated manufacturing.</p> <p>In this research, a statistical process control approach for automated manufacturing systems is developed. The research demonstrates and evaluates a multivariate cumulative sum control scheme (CUSUM) to a set of standardized data collected from an actual production line.</p> <p>Results indicate that the statistical process control approach developed is able to detect small variations in the process quickly and effectively. Furthermore, the approach is capable of monitoring several quality characteristics simultaneously in real time. Quality control for short production runs is also addressed in this research.</p>","abstract_has_math":false,"creators":["Ashur, Hisham Naim"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Engineering Management & Systems Engineering","degree_department":null,"school":null,"contributors":["Renit Unal","Laurence D. Richards","Derya Jacobs","Larry Lee"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":1993,"date_issued":"1993-07-01T07:00:00Z","date_published":"1993-07-01T07:00:00Z","updated_at":"2026-07-24T03:35:08Z","subjects":["Manufacturing processes","Automation","Quality control","Time monitoring","Industrial Engineering","Systems Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.odu.edu/emse_etds/153","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Renit Unal","Laurence D. Richards","Derya Jacobs","Larry Lee"]},{"key":"dc:creator","label":"Author","values":["Ashur, Hisham Naim"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2019-09-13T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering Management & Systems Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Manufacturing processes","Automation","Quality control","Time monitoring","Industrial Engineering","Systems Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.odu.edu/emse_etds/153"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In today's competitive manufacturing environment, effective and practical statistical quality control approaches are essential. A successful process control approach needs to provide on line real time monitoring of quality related characteristics.
No longer acceptable is an approach that analyzes quality related data only after the product is produced. A statistical process control approach that monitors the process during production and that reports trouble spots before bad products are made is necessary in an automated manufacturing environment.
An automated manufacturing environment is characterized by high volume production runs and short production cycles. Traditional statistical process control approaches are not capable of dealing with these challenges and cannot keep up with the pace of automated manufacturing.
In this research, a statistical process control approach for automated manufacturing systems is developed. The research demonstrates and evaluates a multivariate cumulative sum control scheme (CUSUM) to a set of standardized data collected from an actual production line.
Results indicate that the statistical process control approach developed is able to detect small variations in the process quickly and effectively. Furthermore, the approach is capable of monitoring several quality characteristics simultaneously in real time. Quality control for short production runs is also addressed in this research.
"]},{"key":"dc:title","label":"Title","values":["A Multivariate Quality Control Approach for Automated Manufacturing Systems"]}]}],"canonical_facts":{"dc:contributor":["Renit Unal","Laurence D. Richards","Derya Jacobs","Larry Lee"],"dc:creator":["Ashur, Hisham Naim"],"dc:date.available":["2019-09-13T07:00:00Z"],"dc:description.abstract":["In today's competitive manufacturing environment, effective and practical statistical quality control approaches are essential. A successful process control approach needs to provide on line real time monitoring of quality related characteristics.
No longer acceptable is an approach that analyzes quality related data only after the product is produced. A statistical process control approach that monitors the process during production and that reports trouble spots before bad products are made is necessary in an automated manufacturing environment.
An automated manufacturing environment is characterized by high volume production runs and short production cycles. Traditional statistical process control approaches are not capable of dealing with these challenges and cannot keep up with the pace of automated manufacturing.
In this research, a statistical process control approach for automated manufacturing systems is developed. The research demonstrates and evaluates a multivariate cumulative sum control scheme (CUSUM) to a set of standardized data collected from an actual production line.
Results indicate that the statistical process control approach developed is able to detect small variations in the process quickly and effectively. Furthermore, the approach is capable of monitoring several quality characteristics simultaneously in real time. Quality control for short production runs is also addressed in this research.
"],"dc:identifier":["https://digitalcommons.odu.edu/emse_etds/153"],"dc:subject":["Manufacturing processes","Automation","Quality control","Time monitoring","Industrial Engineering","Systems Engineering"],"dc:title":["A Multivariate Quality Control Approach for Automated Manufacturing Systems"],"thesis:degree_discipline":["Engineering Management & Systems Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T03:35:08Z"}