Automation Present and Future By Giridhar S. Chavan


Automation Present and Future By Giridhar S. Chavan

Faculty NMIMS and Mumbai University



Automation is the technology by which doing work becomes very easy with minimal human efforts.

Automation has applications from a smart house to a large industrial advanced control system works on huge input singles convert to output signals.

Such a complex range from on-off control to multi-variable algorithms.


To design industrial systems, to adapt to a service-oriented stream in cloud computing and Internet-of-things .


Keyword: smart aautomation,  Internet-of-thingscloud computing



Smart automation to customised customer-based automaton. Smart automation in agriculture business assisting farmers. Artificial intelligent system makes us touchless automation. Prosses are becoming more and more human centric or human less? Wireless network making us ; at home or industry smart to avoid human intervention. Sometimes it make us think ; is technology increasing gap between human or assisting. This paper is based on both future technology update  advantages and dis advantages.

First of all we will focus on Flexible manufacturing system to research and development based system.

  1. Craft Production (1850)- tailor-made product ; sell then design and produce in very small quantity. (Machine shop)
  2. Mass Production (1913)- steady demand product ; design and produce then sell according to demand. (Assembly Line)
  3. Flexible Production (1980)- variety of demand ; design and produce then sell according to choice .  (FMS -Robotics)
  4. Mass Customization and Personalization (2000)- customised comitative product ; sell ,design and produce. Reconfigurable Manufacturing System (RMS), that have a production capacity that can be rapidly adapted to market demand.
  5. Sustainable Production (2020 onwards) – Design for environment-sell -make.


Future road mapping activity was carried out by some country i.e. USA and European Union . Initially it was carried out in 1995 by Next-Generation Manufacturing (NGM) then in 1997 by integrated Manufacturing Technology Road mapping (IMTR). They worked upon  their interaction with  different  stakeholders. [1]


Following  areas are to be define :

  • Data collection ;
  • Modelling & Simulation ;
  • pre and post Processing ;
  • intelligent design for zero waste;
  • innovative & intelligent control processes;
  • R&D based manufacturing freedom;


The future R&D based process of manufacturing  is socio-commercial in the years 2015-2020, are as :

  • global governance policy;
  • consumer demand behaviour;
  • transport / energy infrastructure;
  • innovation & sustainable development;
  • priorities in higher education & nanotechnology;
  • labour market & social security;


Towards the future competition for resources, the mechanisms to acquire them and thus important for universities and research institutes and centres involved in manufacturing R&D based technology  to make a wide analysis and the output of which will be beneficial for them to build sustainable product.


  1. Migration of industrial automation systems to cloud computing and Internet-of-things.


Due to cloud computing and Internet-of-things technologies, many industrial automation and manufacturing companies have designed future development roadmaps.

The main goal of future factory concept is to integrate systems with product lifecycle . Eliminate current data and information system and industrial automation control hierarchies to modernised industrial software systems in order to compete in global market.

Industrial Interacting system changed to information based system by interconnecting enterprise systems, actuating devices, control systems, human-machine interface (HMI), and cloud services.[2]

  1. device level : sensing and manipulating the production process , Internet-of-things (IoT) technologies provide an opportunity to deploy intelligent business analytics and optimisation services, e.g., fault diagnostics, predictive maintenance, and performance improvement, based on data captured from diverse sources such as industrial field devices.
  2. process control and supervision level: with controllers and supervisory control and data acquisition systems (SCADA) monitoring and managing automated control of the production process; In this study, a device gateway is introduced to process the PLC data in the Internet Protocol (IP) network without changing the internal structure of the PLC. (1) running the SCADA application on site, connected to the control network, and delivering information to the cloud for storage; and (2) running the SCADA application in the cloud, remotely connected to the control network.
  3. manufacturing execution level: with Manufacturing Execution Systems (MES) optimising the production process; he proposed cloud manufacturing system has a layered architecture, comprising (1) manufacturing resource layer, with resources required for the product development lifecycle; (2) virtual service layer to virtualise manufacturing resources that are packaged as cloud manufacturing services; (3) global service layer, responsible for the cloud operational activities; and (4) application layer, offering an interface between the user and manufacturing cloud resources.
  4. enterprise management level: with Enterprise Resource Planning systems (ERP) for business planning, plant production scheduling, and business (1) re-hosting an application on an IaaS (Infrastructure-as-a-service) environment without making changes to its architecture; (2) refactoring to run applications on PaaS (Platform-as-a-service); (3) revising existing code for IaaS or PaaS; (4) rebuilding applications on PaaS; and (5) replacing existing legacy application with SaaS (Software-as-a-service).
  5. Migration process support methodologies

Some commonly adopted core migration activities are incremental, as described by Razavian and Lago (2012). These activities include:

  1. planning increments, such as deciding which components are to be migrated first;
  2. understanding and eliciting relevant information about the current legacy systems as well as understanding the intended target states of the system;
  3. gap analysis of the legacy systems and the target service-oriented system;
  4. forward engineering steps, i.e., analysis, design and implementation of services; and
  5. transforming legacy assets to services, either by wrapping the whole application to new services or decomposing the legacy system into components that can be wrapped and migrated to new services.

The migration process includes:

  1. identifying business requirements, such as the motivation and objectives for migration, as well as functional and non-functional requirements;
  2. identifying potential cloud hosting environments based on the identified business objectives;
  3. analysing applications’ compatibility with the potential cloud environment, and identifying the changes required to solve incompatibility issues if any;
  4. identifying potential architecture solutions with respect to the non-functional requirements identified earlier, and analysing possible trade-offs;
  5. evaluating cloud environments for cloud-specific quality attributes that the migrated application can utilise upon deployment, e.g., functionality, scalability, and performance;
  6. evaluating proposed solutions and effected components against target platforms to assess deployment feasibility;
  7. implementing and refactoring the application for cloud environment deployment.

There are in total six phases involved in the migration process:

  1. cloud assessment phase to build a business case for moving to the cloud, including an assessment from financial, security and compliance, technical and functional perspectives to identify gaps between the current and future architectures;
  2. proof of concept phase to ensure that the assumptions and judgements made in the previous phase are valid;
  3. data migration phase to map the different storage options to meet the specific needs in redundancy and data management;
  4. application migration phase to move a part of or the entire system to the cloud without disrupting or interrupting the current enterprise business;
  5. leverage the cloud phase to utilise the available features and services from the selected cloud provider while ensuring that the migrated solution works as expected; and
  6. optimisation phase to optimise the cloud-based application to decrease costs in system deployment.

This migration approach from legacy to SOA-based systems comprises several steps:

  1. identifying suitable wrapping information technologies;
  2. specifying and implementing service composition, orchestration and choreography mechanisms;
  3. defining and implementing control and monitoring mechanisms that would benefit from the advantages offered by the SOA-based systems; and
  4. identifying migration strategies and migration paths that address top-down and bottom-up architectural and functional views, and analysing their implications and interdependencies among migration paths.

According to the study, the real-time SOA support of process control and monitoring is still an open issue.

5.    Migration assessment methodologies

To help engineers and architects better evaluate the risks and effort required for building or migrating an enterprise application to the cloud, Asmus, Fattah, and Pavlovski (2016) present a method that consists of:

  1. examine the current physical deployment;
  2. evaluate operational risks associated, such as mission-critical, regulatory control, data sensitivity, application volatility, and integration paradigms such as batch, real-time, and human workflow;
  3. evaluate application complexity attributes, such as function point scope, a number of technologies and interfaces required.

5. Industry practices

  1. The E-factory (E-factory) is a factory automation initiative founded by Mitsubishi Electric in 2003. This initiative aims to:
  2. create smart factories and optimise manufacturing through shop floor big data utilisation and connectivity to IT systems;
  3. increase the performance of existing plant infrastructure with legacy system architecture through edge computing to enable; and
  4. improve information processing between the automation hierarchy layers.

b. Industrial internet consortium

The industrial internet consortium (IIC) was initially founded by AT&T, Cisco, General Electric, IBM and Intel in 2014.

For traditional plant manufacturing, a guideline provided by the consortium is to revamp, upgrade, and retrofit in order to prepare the existing legacy systems for their integration into the digital automation.


c. Industrial value-chain initiative

The industrial value-chain initiative (IVI) is a forum launched in Japan in 2015 for smart manufacturing with the goal of connectivity across industry sectors.

d. Industry4.0

The Industry 4.0 initiative (Industrie4.0) was launched in Germany by major companies in industrial sectors. he notion of the smart factory in this initiative represents the ‘flexibility to enable machines and plants to adapt their behavior to changing orders and operating conditions through self-optimization and reconfiguration’.

e. Made in China 2025

China introduced the ‘Made in China 2025’ plan (MiC) in 2015. This manufacturing strategy focuses on the entire manufacturing process and has defined several major manufacturing indicators, i.e., innovation capability, quality and value, integration of IT and industrialisation, green manufacturing such as energy saving and new energy vehicles.

include industrial robots, industrial software, cloud computing and big data, and 3D printing.

f.  Manufacturing USA

This program (Manufacturing USA), also called National Network for Manufacturing Innovation program, was launched in 2014, with the mission to connect people, ideas, and technology to solve industrial challenges in advanced manufacturing. in the fields of additive manufacturing, power electronics and hybrid electronics manufacturing, digital manufacturing, etc.

[2] Realising the characteristics of the future factory





two domains have been considered:

  • man’s life cycle needs/man focused services; Emerging Transformation Processes future only through the development of innovative Enabling Technologies ETs in knowledge based manufacturing areas like new materials, nanotechnologies and micro & hybrid devices.


The competition for resources, the mechanisms to acquire them, the presence of new competitors on the research market, is a strong challenge for universities, research institutes and companies.


A strong selection may take place and change, in the next five to ten years, the international research landscape and market.


This calls for the Sustainability of actors, as a fundamental prerequisite for the competitiveness and sustainability of industry.


Finally, the decreasing attention to manufacturing –while new needs and requirements are “springing up” which call for new RTD based “answers” from manufacturing –may lead to a strategic disaster [110].


New strategic RTD programmes should be launched.

In this paper, we describe a comprehensive overview of existing studies in the techniques, experiences, and methodologies used for migrating legacy industrial systems in the cloud computing and Internet-of-things context, as well as industry practices to achieve the vision of smart factories. We have found that many existing studies are concept proposals, and most migration techniques are still in the early phase of maturity, only validated with experiments and controlled case study scenarios. Further exploration and enhancement of these techniques and migration methodologies are needed to ensure their applicability in real-world cases. In addition, the migration challenges with respect to, e.g., device data confidentiality and integrity, ensuring hard real-time performance, and fulfilling industrial requirements on reliability and availability after migration call for further exploration efforts from both researchers and practitioners.


  1. F. Jovane1 (1), Y. Koren2 (1), C.R. Boër1 (1) 1 ITIA-CNR, Institute of Industrial Technologies and Automation – National Research Council of ITALY Viale Lombardia 20/A, 20131 Milano, Italy
  2. 2 ERC/RMS, College of Engineering – University of Michigan 2350 Hayward St., Ann Arbor, Michigan, USA Hongyu Pei Breivold, Journal Enterprise Information Systems  Volume 14, 2020 – Issue 4: Towards factories of the future: migration of industrial legacy automation systems in the cloud computing and Internet-of-things

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