The purpose of this paper was to use the Overall Equipment Effectiveness productivity formula in plant manufacturing and convert it to measuring productivity for forklifts. Productivity for a forklift was defined as being available and picking up and moving containers at port locations in Seattle and Alaska. This research uses performance measures in plant manufacturing and applies them to mobile equipment in order to establish the most effective means of analyzing reliability and productivity. Using the Internet of Things to collect data on fifteen forklift trucks in three different locations, this data was then analyzed over a six-month period to rank the forklifts’ productivity from 1 – 15 using the Operating Equipment Effectiveness formula (OPEE). This ranking was compared to the industry standard for utilization to demonstrate how this approach would yield a better performance analysis and provide a more accurate tool for operations managers to manage their fleets of equipment than current methods. This analysis was shared with a fleet operations manager, and his feedback indicated there would be considerable value to analyzing his operations using this process. The results of this research identified key areas for improvement in equipment reliability and the need for additional operator training on the proper use of machines and provided insights into equipment operations in remote locations to managers who had not visited or evaluated those locations on-site.
","abstract_html":"<p>The purpose of this paper was to use the Overall Equipment Effectiveness productivity formula in plant manufacturing and convert it to measuring productivity for forklifts. Productivity for a forklift was defined as being available and picking up and moving containers at port locations in Seattle and Alaska. This research uses performance measures in plant manufacturing and applies them to mobile equipment in order to establish the most effective means of analyzing reliability and productivity. Using the Internet of Things to collect data on fifteen forklift trucks in three different locations, this data was then analyzed over a six-month period to rank the forklifts’ productivity from 1 – 15 using the Operating Equipment Effectiveness formula (OPEE). This ranking was compared to the industry standard for utilization to demonstrate how this approach would yield a better performance analysis and provide a more accurate tool for operations managers to manage their fleets of equipment than current methods. This analysis was shared with a fleet operations manager, and his feedback indicated there would be considerable value to analyzing his operations using this process. The results of this research identified key areas for improvement in equipment reliability and the need for additional operator training on the proper use of machines and provided insights into equipment operations in remote locations to managers who had not visited or evaluated those locations on-site.</p>","abstract_has_math":false,"creators":["Hays, Carl D, III"],"institution":null,"degree_name":"MS in Agribusiness","degree_level":null,"degree_discipline":"Agribusiness","degree_department":null,"school":null,"contributors":["Tali Freed","Industrial and Manufacturing Engineering","College of Agriculture, Food, and Environmental Sciences"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-06-01T07:00:00Z","date_published":"2021-06-01T07:00:00Z","updated_at":"2026-07-24T01:32:42Z","subjects":["IoT","IIoT","Telematics","Big Data","OEE","Industry 4.0","Agribusiness","Business Administration, Management, and Operations","Business Analytics","Business Intelligence","Computer and Systems Architecture","Data Storage Systems","Hardware Systems"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.15368/theses.2021.110"],"render_values":[{"text":"10.15368/theses.2021.110","href":"https://doi.org/10.15368/theses.2021.110","code":true}]}]},"links":{"outbound_url":"https://digitalcommons.calpoly.edu/theses/2410","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Tali Freed","Industrial and Manufacturing Engineering","College of Agriculture, Food, and Environmental Sciences"]},{"key":"dc:creator","label":"Author","values":["Hays, Carl D, III"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2021-06-12T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Agribusiness"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS in Agribusiness"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["IoT","IIoT","Telematics","Big Data","OEE","Industry 4.0","Agribusiness","Business Administration, Management, and Operations","Business Analytics","Business Intelligence","Computer and Systems Architecture","Data Storage Systems","Hardware Systems"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.calpoly.edu/theses/2410","10.15368/theses.2021.110"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The purpose of this paper was to use the Overall Equipment Effectiveness productivity formula in plant manufacturing and convert it to measuring productivity for forklifts. Productivity for a forklift was defined as being available and picking up and moving containers at port locations in Seattle and Alaska. This research uses performance measures in plant manufacturing and applies them to mobile equipment in order to establish the most effective means of analyzing reliability and productivity. Using the Internet of Things to collect data on fifteen forklift trucks in three different locations, this data was then analyzed over a six-month period to rank the forklifts’ productivity from 1 – 15 using the Operating Equipment Effectiveness formula (OPEE). This ranking was compared to the industry standard for utilization to demonstrate how this approach would yield a better performance analysis and provide a more accurate tool for operations managers to manage their fleets of equipment than current methods. This analysis was shared with a fleet operations manager, and his feedback indicated there would be considerable value to analyzing his operations using this process. The results of this research identified key areas for improvement in equipment reliability and the need for additional operator training on the proper use of machines and provided insights into equipment operations in remote locations to managers who had not visited or evaluated those locations on-site.
"]},{"key":"dc:title","label":"Title","values":["A Method for Monitoring Operating Equipment Effectiveness with the Internet of Things and Big Data"]}]}],"canonical_facts":{"dc:contributor":["Tali Freed","Industrial and Manufacturing Engineering","College of Agriculture, Food, and Environmental Sciences"],"dc:creator":["Hays, Carl D, III"],"dc:date.available":["2021-06-12T07:00:00Z"],"dc:description.abstract":["The purpose of this paper was to use the Overall Equipment Effectiveness productivity formula in plant manufacturing and convert it to measuring productivity for forklifts. Productivity for a forklift was defined as being available and picking up and moving containers at port locations in Seattle and Alaska. This research uses performance measures in plant manufacturing and applies them to mobile equipment in order to establish the most effective means of analyzing reliability and productivity. Using the Internet of Things to collect data on fifteen forklift trucks in three different locations, this data was then analyzed over a six-month period to rank the forklifts’ productivity from 1 – 15 using the Operating Equipment Effectiveness formula (OPEE). This ranking was compared to the industry standard for utilization to demonstrate how this approach would yield a better performance analysis and provide a more accurate tool for operations managers to manage their fleets of equipment than current methods. This analysis was shared with a fleet operations manager, and his feedback indicated there would be considerable value to analyzing his operations using this process. The results of this research identified key areas for improvement in equipment reliability and the need for additional operator training on the proper use of machines and provided insights into equipment operations in remote locations to managers who had not visited or evaluated those locations on-site.
"],"dc:identifier":["https://digitalcommons.calpoly.edu/theses/2410","10.15368/theses.2021.110"],"dc:subject":["IoT","IIoT","Telematics","Big Data","OEE","Industry 4.0","Agribusiness","Business Administration, Management, and Operations","Business Analytics","Business Intelligence","Computer and Systems Architecture","Data Storage Systems","Hardware Systems"],"dc:title":["A Method for Monitoring Operating Equipment Effectiveness with the Internet of Things and Big Data"],"thesis:degree_discipline":["Agribusiness"],"thesis:degree_name":["MS in Agribusiness"]},"updated_at":"2026-07-24T01:32:42Z"}