Performance Prediction through OEE-Model
Prediction of an organization’s performance has become essential for any organization for its potential customers to place orders with confidence. Overall Equipment Effectiveness (OEE) is one of the acknowledged measures for performance monitoring. In this paper, two different techniques, a simple m...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Novi Sad, Faculty of Technical Sciences
2020-04-01
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| Series: | International Journal of Industrial Engineering and Management |
| Subjects: | |
| Online Access: | http://ijiemjournal.org/counter/click.php?id=vol11_02_03 |
| _version_ | 1818259205624168448 |
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| author | CH. Anusha V. Umasankar |
| author_facet | CH. Anusha V. Umasankar |
| author_sort | CH. Anusha |
| collection | DOAJ |
| description | Prediction of an organization’s performance has become essential for any organization for its potential customers to place orders with confidence. Overall Equipment Effectiveness (OEE) is one of the acknowledged measures for performance monitoring. In this paper, two different techniques, a simple moving average and Holt’s double exponential smoothing methods, are used to evaluate OEE and to predict the future performance of overall equipment effectiveness in R studio. Holts Double exponential smoothing method was found to result in minimum error measured by mean absolute deviation. Python program is to predict major losses to improve the productivity of the organization by management. |
| first_indexed | 2024-12-12T18:11:44Z |
| format | Article |
| id | doaj.art-bfcd2dca9c2947058e3eda5dfa36d75e |
| institution | Directory Open Access Journal |
| issn | 2217-2661 2683-345X |
| language | English |
| last_indexed | 2024-12-12T18:11:44Z |
| publishDate | 2020-04-01 |
| publisher | University of Novi Sad, Faculty of Technical Sciences |
| record_format | Article |
| series | International Journal of Industrial Engineering and Management |
| spelling | doaj.art-bfcd2dca9c2947058e3eda5dfa36d75e2022-12-22T00:16:23ZengUniversity of Novi Sad, Faculty of Technical SciencesInternational Journal of Industrial Engineering and Management2217-26612683-345X2020-04-0111293103http://doi.org/10.24867/IJIEM-2020-2-256256Performance Prediction through OEE-ModelCH. Anusha0V. Umasankar1Vellore Institute of Technology, School of Mechanical Engineering, Chennai, Tamilnadu, IndiaVellore Institute of Technology, School of Mechanical Engineering, Chennai, Tamilnadu, IndiaPrediction of an organization’s performance has become essential for any organization for its potential customers to place orders with confidence. Overall Equipment Effectiveness (OEE) is one of the acknowledged measures for performance monitoring. In this paper, two different techniques, a simple moving average and Holt’s double exponential smoothing methods, are used to evaluate OEE and to predict the future performance of overall equipment effectiveness in R studio. Holts Double exponential smoothing method was found to result in minimum error measured by mean absolute deviation. Python program is to predict major losses to improve the productivity of the organization by management.http://ijiemjournal.org/counter/click.php?id=vol11_02_03overall equipment effectivenesssimple moving averageholt’s double exponential smoothingpredictive analyticslosses |
| spellingShingle | CH. Anusha V. Umasankar Performance Prediction through OEE-Model International Journal of Industrial Engineering and Management overall equipment effectiveness simple moving average holt’s double exponential smoothing predictive analytics losses |
| title | Performance Prediction through OEE-Model |
| title_full | Performance Prediction through OEE-Model |
| title_fullStr | Performance Prediction through OEE-Model |
| title_full_unstemmed | Performance Prediction through OEE-Model |
| title_short | Performance Prediction through OEE-Model |
| title_sort | performance prediction through oee model |
| topic | overall equipment effectiveness simple moving average holt’s double exponential smoothing predictive analytics losses |
| url | http://ijiemjournal.org/counter/click.php?id=vol11_02_03 |
| work_keys_str_mv | AT chanusha performancepredictionthroughoeemodel AT vumasankar performancepredictionthroughoeemodel |