Mega Trends: Industrial Process Automation
Published on : Thursday 10-12-2020
The industrial automation industry is ready to take up new challenges and keep pace with increasingly high expectations/demands of the industry, says Snehal Patel.
The aspects like improvement in production efficiency, enhanced reliability, supply chain optimisation, better safety and security of assets/people, greater profitability and other relevant criteria are mostly common across various process industry verticals globally.
However the latest technological trends are revolutionary and have pushed the process automation industry to stay in tune with the ever increasing expectation in the process industry domain.
Some of the mega trends in industrial process automation are mentioned in the illustration.
The digital transformation of industrial processes is presently driving the next generation of futuristic solutions that are destined to be much smarter, further automated, and intelligent and cost efficient.
One such solution is digital transformation in Asset Management. Let’s briefly talk about it.
Overview
The industrial revolution today is driven by the development of new upcoming next generation technologies and is changing the industrial landscape on a scale never expected/foreseen before.
One amongst some of the trending approaches in Industry 4.0 implementation is ‘Digitalisation of Assets’ across industry verticals. However, the transition from current archetypes to Industry 4.0 compliant, fully digital approaches to Asset Management is a highly challenging task. It entails changes in the technological infrastructure, the asset management processes and the business management of industrial organisations.
Challenges and mitigation
Currently, many of the phases including the life cycle/maintenance phases are on standalone mode in most of the industries wherein many of them with less predictive/low analytical approach. In fact, most of the assets have never been digitally planned and/or designed. Furthermore, the current data formats do not fulfil the requirements of data storage in the maintenance phase. Such problems create challenges for implementation of digitalisation and hence need to be addressed.
Hence, the most important task is to understand and thereby put across the right digital strategy. This comes from a deep knowledge and understanding of the entire infrastructure including each of the assets and its functions, various processes and workflow, health, safety and environmental aspects and thereby the value chain in entirety.
In order to have a successful implementation, it is important to clearly understand the various life-cycle phases (as outlined below) of industrial infrastructure assets, considering the data needs and requirements:
-Planning/scheduling phase
-Design phase
-Construction phase
-Life cycle service/maintenance phase, and
-End of life/reconstruction/recycling phase.
The success of ‘Digitalisation of Assets’ necessitates the right use of digital information in all above phases.
Also, some of the below key aspects are integral part of the Asset Digitalisation solution/strategy:
1. Adherence to Asset Management Standards as per local and international regulations
2. Industrial infrastructure upgradation/modernisation (technological perspective) including IT Infrastructure. Technology enablers like:
a) Internet of Things (IoT) technologies
b) Cloud computing
c) Big data management, and
d) Critical Infrastructure Protection (CIP) including cyber security solutions.
3. Logical Perspective – Facilitating data driven predictive and prescriptive approaches.
4. Business Implications – Impact on business management aligned to digital strategies.
Benefits of digitalisation of assets
The benefits are strongly related to an efficient use of collected/stored/analysed data. Digitalisation includes the use of data in the assessment and analytical processes to the maximum possible extent.
Digitalisation of assets can bring various benefits to day-to-day operations and cost reduction. In an industrial environment, it will help create historical and real-time data that lets operators improve maintenance and inspection regimes.
Detecting anomalies during various activities and operations will enable more effective decision making that can deliver cost savings.
Also, there are latent benefits for health, safety, security and environment (HSSE) performance. Data-enabled smart implementation can provide a more accurate view of condition status, allowing better prediction and mitigation of equipment failure. Extensive data on efficiency and emissions provides for better monitoring of HSSE performance and compliance. Data analytics improves understanding of environmental risks, and authorities can create more effective regulations and monitoring.
Risks of digitalisation of assets
Digitalisation is not simple as defined wherein it includes complex processes and various risks that need to be managed at every stage of implementation. More specifically, the quality and quantity of data is often the critical factor for a successful implementation. Some of the major risks are as follows:
i. Quality of data
ii. Quantity/volume of data flow
iii. Velocity of data
iv. Accuracy/source of data using different formats and/or benchmarked systems
v. Data management of incorrect and incomplete data
vi. Accessibility of data
vii. Security of data
viii. Performance of asset management software solutions
ix. Data communication, visualisation of data, user interfaces, etc.
It is highly essential to investigate the overall risks before starting an implementation process.
Finally, let us look into one important solution supporting successful ‘Asset Management Implementation’ as a representative example.
Representative example
(Predictive Maintenance – Analytics Software Solutions)
Digitalisation can also be implemented for predictive maintenance to reduce asset downtime, real-time monitoring of assets to optimise processes, and cut down downtime for unplanned shutdowns.
Analytics Software Solutions plays an important role in the overall predictive maintenance. Kindly refer below certain benefits:
-Transforms large amounts of real-time data into Actionable Intelligence.
-Drives improvement in productivity, efficiency, quality, and sustainability.
-Can also be applied to solve common business intelligence (BI) challenges.
-Machine learning can be applied to Big Data, leading to visualisation and reporting solutions for energy optimisation, fault detection and diagnostics, predictive maintenance, SPC quality control, and equipment efficiency.
Many software OEMs and solution providers offer a suite of analytics solutions that leverages these technologies, providing immediate benefits for any application.
Conclusion
The Industrial Automation Industry is ready to take up new challenges wherein various stakeholders are proactively working to keep up with the pace of every increasingly high expectations/demands of the industry.
Snehal Patel is an experienced Automation Industry professional with almost 25 years of global experience including various leadership roles in reputed MNCs like Honeywell, General Electric, Emerson (Daniel Measurement India) and Sterling and Wilson (a Shapoorji Pallonji Group company). During this period he has worked closely with cross functional teams like corporate strategy, legal/contractual, finance & commercial, M&A, sourcing team, etc., for establishing/operationalising the relevant engagements.
