Open AccessPredicting Production Cycle Time in a Real Disposable Medical Device Manufacturing System Using Semi-Supervised Deep Learning
Author(s) -
Himansh Chandani,
Mayank Tyagi,
Rajiv Chaudhary,
Ranganath M. Singari
Publication year - 2020
Publication title -
international journal of advanced production and industrial engineering
Language(s) - English
Resource type - Journals
ISSN - 2455-8419
DOI - 10.35121/ijapie202007341
Subject(s) - computer science , convolutional neural network , deep learning , production (economics) , artificial neural network , artificial intelligence , process (computing) , industrial engineering , product (mathematics) , reduction (mathematics) , production cycle , machine learning , manufacturing , manufacturing engineering , process engineering , engineering , geometry , mathematics , law , political science , economics , macroeconomics , operating system
Manufacturing lot cycle time is the period required by a manufacturer for completion of a production process. It is an essential factor for determining the success of most manufacturing organizations, yet most research is based on studies made almost exclusively in the semiconductor industry and does not attempt to utilize the complete potential of recent breakthroughs in computational learning. Using real data collected from a medical device manufacturing company, this paper demonstrates the applicability of a semi-supervised deep learning framework for highly accurate cycle time prediction, using stacked Denoising Autoencoders to form fully connected deep neural networks and Convolutional Neural Network models. The proposed strategies for cycle time prediction can have a significant impact on product design decision optimization within the system which, in turn, facilitates reduction of costs, energy use, and the overall environmental impact.