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Top1. Introduction
From last decade the business empire is troubling through the colossal business challenges such as standard production outputs, virtual product design, simulation, online process optimization, diagnosing problems by itself, etc., to accomplish the objectives of business society. In responding to the business challenges, the business architectures bear the opportunities of IoT engines into traditional technology; targeted to drive the production database, cloud computing system, cyber-physical systems across the business society is called industry 4.0.
Industry 4.0 secure the business practices by introducing the digital advancement includes big data analyzers, internet servers, digital data interpreters and data compilers into industrial sectors. Introduction of hyper digital technology into the business operations for attending the Excellency in business networks is called Industry 4.0. It is defined as a bridge between the cyber physical production system and ICT. Industry 4.0 is an evolutionary process, includes the topics such as big data analytics, autonomous of machines, simulation, horizontal and vertical production integration, the industrial internet of things, cyber security, cloud computing, additive manufacturing, virtual reality etc. Industry 4.0 links the technical system with weave oriented digital wire/wireless network across the horizontal and vertical to add the value in industrial internet of things. The chief aim of industry 4.0 is to utilize technology and eliminate the humans’ interaction with machine, specific target to adopt the digital system-based business and production process for attaining the goal and direct the industry to sustain against the future competitive market.
It is found that around the world 90% companies promote the industry 4.0’s (IIoT) BRs for attaining the future goals. It is probed that the growth of research in the context of industry 4.0 is utterly depend upon the interest of industrialists and structure of industry 4.0 sectors. The evaluation of the research growth vs industry 4.0 BRs decides the continuous evolvement and future progress of industry 4.0’s. In order to quantify and classify the level of research growth of industry 4.0’s architectures, the authors preliminary focuses to evaluate very reliable and consistent research engine or Research Growth Evaluation Tools (RGETs). Recently, the authors present the many RGE tools i.e. Expert Opinion System (EOS), Forecasting System (FS) etc. These systems aid the researchers to potentially measure the research growth vs industry 4.0’s architectures (Kitchenham & Stuart, 2002). But, none of system is reliable. EOS deals with subjective or linguistic opinion of industry 4.0. researchers/professionals, therefore data varies from one expert to another, is less reliable and inferior in quantify the current research trends and growth vs industry 4.0’s architectures, while forecasting system delivers so least consistent result than EOS, as it considers the prior database of research documents. Therefore, it is investigated by literature survey; The Open (Google) Internet Based Research Search Engine (OIBRSE) supports the researchers to collect the database related to prior research manuscripts. OIBRSE is so popular due to its versatility and reliable result delivery than both systems (Kitchenham & Stuart, 2002). Later, a few significant literature surveys related to industry 4.0‘s BRs are conducted to evaluate the IIoT-BRs.
The research deals with a comprehensive literature survey specific to subjects’ i.e. intelligent production, Internet of Things (IoT), and cloud computing-based production. A few key technologies i.e. IIoT, Cyber-Physical Systems (CPSs), cloud computing, Big Data Analytics (BDA) and Information and Communications Technology (ICT), participate to the future business growth and intelligent production as well (Prause, 2015). The research work focuses upon two key learning thoughts of ERP. First targets to the business process integration that extends the business functions across the industry 4.0 and second addresses to the challenges of commodity supply to meet future demand (Mand & Uden, 2016). The research work split into two parts. The first part focuses on the recent design problems and solutions of MES. The second part deals with architecture design of smart MES in taking account the design and operations scenario (Jeon, Um, Yoon, & Hwan, 2017). The research discusses the various types of IoT electronic gadgets i.e. laptops, mobile phones, tablets, and smart phones etc, connect the employees with each other. Apparently, same devices add the value in industry 4.0 such as renting the future employees for cross functional fields of industry (Venkatesh, 2017)