Comparative analysis of machine learning algorithms for predicting standard time in a manufacturing environment

Abstract

Determining accurate standard time using direct measurement techniques is especially challenging in companies that do not have a proper environment for time measurement studies or that manufacture items requiring complex production schedules. New and specific time measurement techniques are required for such companies. This research developed a novel time estimation approach based on several machine learning methods. The set of collected inputs in the manufacturing environment, including a number of products, the number of welding operations, product's surface area factor, difficulty/working environment factors, and the number of metal forming processes. The data were collected from one of the largest bus manufacturing companies in Turkey. Experimental results demonstrate that when model accuracy was measured using performance measures, k-nearest neighbors outperformed other machine learning techniques in terms of prediction accuracy. “The number of welding operations” and “the number of pieces” were found to be the most effective parameters. The findings show that machine learning algorithms can estimate standard time, and the findings can be used for several purposes, including lowering production costs, increasing productivity, and ensuring efficiency in the execution of their operating processes by other companies that manufacture similar products.

Mental Workload Assessment Using Machine Learning Techniques Based on EEG and Eye Tracking Data

“…Artificial neural network (ANNs) is a mathematical technique that utilizes the principles of the human brain's nervous system to analyze data and make predictions [52][53][54][55]. These networks are capable of efficiently processing large and intricate datasets [56,57].…”