Integrating IoT and Big Data Analytics for Smart Paint Manufacturing Facilities

Abstract

Traditional manufacturing systems are currently undergoing digital transformation by integrating Identification, Sensing, and Communication technology. Mass Unstructured data from structured and unstructured data sources are generated by smart manufacturing equipment and applications in Internet of Things (IoT) powered smart factories. The rapidly changing manufacturing environment produces a variety of challenges in ensuring production and operation efficiency and delivering business values. There's an urgent need for businesses to harness, analyze and gain intelligence from these derived data. Big Data Analytics (BDA), a pioneer in the manufacturing field, focuses on dealing with these 4 V's of Big Data (Volume, Variety, Velocity, and Veracity) through advanced data processing, integration, analysis, machine learning, predictive and prescriptive modelling that assist with data-driven decision-making and optimization in the manufacturing process. While several BDA techniques such as preprocess data, build descriptive models, perform huge-scale data mining and run machine learning predictive models are developing in the manufacturing field, nowadays many manufacturers lag behind in adopting BDA into their operations. This paper, through a systematic literature review, aims to analyze the styles of the existing research and see if they can provide panoramic views toward the integration of BDA and smart manufacturing systems. Seven foundational perspectives based on which the reviewed papers are classified include definitions, applications, architecture, models, methods or techniques, implementations, and reviews or surveys.