Key points
Industrial automation and control systems, such as the distributed control system and programmable logic controllers, which are extensively used by manufacturing companies, have evolved over time to become information technology driven-systems.
Propelled by customer demands, suppliers’ initiatives, and technological development, the influence of information technology on automations systems will only increase in the times to come.
While in the past manufacturing companies were satisfied with automation systems that provided reliable and accurate descriptive information, now they want them to provide information that is more insightful and purposeful.
They would like automation systems to provide information that goes beyond describing the status to include possible reasons for the incident (diagnostic), when the incident could happen next (predictive), and outline available options to avert it (prescriptive).
Due to ever evolving business dynamics, the needs of manufacturing companies undergo changes. The pressure on them to become more efficient, productive, and agile is forcing the manufacturing companies to demand that automation systems provide much more holistic and comprehensive information that will help them take appropriate business, operational and production decisions.
This necessitates manufacturing companies to expect automation systems to generate the needed information that will help them manage production operations autonomously and exchange information, on need-based criteria and without constraints, with other operational technology systems & information technology systems, such as ERP, SCM, CRM, and MES.
Limitations of existing automation systems
Presently industrial automation systems essentially provide only basic information about the status of various process parameters, annunciate deviations when they occur, and control designated parameters based on first principles.
The automation systems, by accessing the data from sensors, provide descriptive information about parameter values, such as what is the water level inside the boiler drum, what is the steam temperature at the boiler outlet etc.
As regards the diagnostic information, they only provide information, for example that the drum level is high or steam temperature is low etc., and not why the drum level went up or why the steam temperature dropped? The control room operators, based on their knowledge about the process, have to use their cognitive skills to understand the causes leading to the rise in drum level or fall in steam temperature, anticipate the consequences, and initiate corrective actions.
As regards freer exchange of information, by initial design and the system availability functional requirement, automation systems work in the standalone mode and hence extra efforts are required to make them share information with other systems. Manufacturing companies now want automation systems to share information with other systems without compromising on the system availability criterion.
Enhancing automation system capabilities?
Automation systems, by incorporating into their architecture the rapidly evolving of digital technologies, such as the industrial internet of things (IIoT), artificial intelligence (AI), edge & cloud computing, big data analytics, etc., will be able to meet the above mentioned enhanced expectations of manufacturing companies.
Internet will improve the connectivity and data accessing & sharing capabilities of automation systems. It acts as an excellent means to achieve connectivity among almost everything. Declining prices, hardware miniaturisation, increasing processing power, and such others are making it technically and economically feasible to embed plant machinery, equipment, parts, final products, and such others with sensors, actuators, edge computing, and internet capabilities.
Embedded assets become industrial internet of things (IIoT) / cyber-physical systems (CPS). Thus they can be empowered with capabilities to generate, send, receive, and share data & information. The manufacturing facility can thus become a connected and information driven entity.
Information available from IIoT/CPS can be analysed using the data analytics and other technology enablers to generate comprehensive actionable information. With the help of embedded edge computing it is possible to even process data that demands lower latency and faster response. If necessary, cloud computing can be accessed to handle more complex tasks.
Artificial intelligence, data analytics, machine learning and other related techniques can be applied to study, establish correlations & analyse dynamic operational conditions, and predict trends and abnormalities. In other words, use of these technologies could help in generating not only descriptive but also diagnostic, predictive, and prescriptive insights.
Their power can also be leveraged to enhance the performance of advanced process controls. Various factors, such as engineering efforts involved in studying nonlinear behaviour or time-varying characteristics, have restricted until now the extensive use of advanced process control systems.
Application of artificial intelligence and related techniques can help the effective development of digital twin models, which can provide inputs for optimisation, by combining information from varied sources, such as first-principle models, historian data, and intuitive judgement.
Incorporation of these digital technologies will make the automation systems smarter and thereby meet the requirements of manufacturing companies. Automation systems that are functionality rich and more powerful will help industrial enterprises to enhance stakeholder value, customer satisfaction, and their revenues & profits through productivity improvements & efficient operations, while ensuring safe operation of the plants.
Multiple approaches to enhance automation system capabilities
Manufacturing companies that want to benefit from convergence of automation and information technologies are taking the path of working with suppliers/consultants/system integrators to build customised solutions. Technology companies, such as Amazon, IBM, and Microsoft, with strong competencies in enabling technologies and seeing tremendous new business and monetisation opportunities, are making large investments and establishing centres of excellence to develop and demonstrate their capabilities of these technologies and benefits of leveraging them.
Their business model is to offer platform-as-a-service; examples are Amazon Web Service (Amazon), Watson (IBM), and Azure (Microsoft).
Some of the automation market leaders, such as ABB and Emerson, are largely taking the path of joint working with technology companies so that their complimentary competencies in their respective domains can be marshalled to architect IIoT-embedded automation systems.
ABB has entered into agreements with IBM and Microsoft that envisages the companies to collaborate to jointly develop and sell new products. Emerson is collaborating with Microsoft to expand its Industrial IoT applications by integrating Microsoft IoT offerings.
Emerson’s announcement says that it is working with Microsoft to help industrial manufacturers realise the business impact and value of Industrial Internet of Things (IoT) with its Plantweb digital ecosystem.
Some entrepreneurial automation companies such as Inductive Automation, Kepware, Opto 22, and Hilscher have innovatively positioned themselves along with others in the industrial automation systems market and proactively creating opportunities for system engineers to architect automation systems using digital technologies.
Their offerings include among others edge gateways, edge communication nodes, etc. Different stakeholders are taking varying approaches to ultimately make automation systems functionality rich and more powerful.
