Use of Big Data and Knowledge Discovery to Create Data Backbones for Decision Support Systems

Abstract

The objective of this research is to provide decision support to assembly line planners when they perform assembly time estimations. There is a lack of consistency in the assembly time analysis performed by planners. The decision support system that was developed in this research is based on mapping controlled language assembly work instructions to Methods-Time Measurement (MTM) tables. Automated analysis of historical work instructions and their related time study analysis were performed by employing knowledge discovery and data mining (KDD) algorithms through the Waikato Environment for Knowledge Analysis (WEKA) interface. As a result of this automated analysis, forty-six mapping rules were created that related work instructions to MTM tables and the data backbone for the decision support system that was created. Analyzing large sets of historical data is crucial while creating decision support systems. KDD provides a sustainable method of analyzing big data. Future work of this research includes applying the KDD process to create data backbones for decision support systems to aid in ergonomic evaluations.