Volume 35, Issue 3 (IJIEPR 2024)                   IJIEPR 2024, 35(3): 154-168 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Khan A, P S. “Transforming The Plastic Industry: Harnessing Machine Learning for Enhanced Efficiency”. IJIEPR 2024; 35 (3) :154-168
URL: http://ijiepr.iust.ac.ir/article-1-2036-en.html
“Transforming The Plastic Industry: Harnessing Machine Learning for Enhanced Efficiency”
Arifa Khan 1, Saravanan P2
1- SRM, Kattankulathur, Kanchipuram - 603203, Tamil Nadu, India , ak7641@srmist.edu.in
2- SRM, Kattankulathur, Kanchipuram - 603203, Tamil Nadu, India
Abstract:   (873 Views)
Optimizing production in the plastic extrusion industry is a pivotal task for small scale industries. To enhance the efficiency in today’s competitive market being a small-scale manufacturer over their peers is challenging. With the limited resources, having constraints on manpower, capital, space, often facing fluctuations in demand and production, simultaneously maintaining high quality became very important for the success. Among the plethora of KPIS used in manufacturing, Overall Equipment Effectiveness (OEE) stands out as corner stone. In this study, we collected real-world data from a plastic extrusion company. i.e., an HDPE Pipe manufacturing company. It serves as the backdrop for our study, this is based on the plastic extrusion sector and set out a goal of enhancing OEE through a comparative investigation of various ML models.  To forecast and estimate OEE values, we used various Machine Learning models and examine each algorithm’s performance using metrics like Mean Squared Error (MSE) and model comparisons using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), creating a comprehensive picture of each algorithm’s strength which enables the small businesses to make informed decisions and empowers them to stay agile and adapt to the changes in the manufacturing environment.
 
Keywords: Machine Learning, Overall Equipment Effectiveness, Deep Learning, Akaike Information Criterion, Bayesian Information Criterion.
Full-Text [PDF 646 kb]   (185 Downloads)    
Type of Study: Research | Subject: Productivity Improvement
Received: 2024/05/14 | Accepted: 2024/08/17 | Published: 2024/09/18
Add your comments about this article : Your username or Email:
CAPTCHA
Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.