Industrial Automation Magazine India

Industrial Automation Leveraging Advanced Technologies

Published on : Monday 07-02-2022

Experts debate the impact of advanced technologies on industrial automation and supply chains.

Artificial Intelligence, Machine Learning, Cloud, Edge, Analytics and 5G are among technologies driving industrial automation to the next level. These innovations in industrial automation, powered by interconnectivity, enable operators to monitor all the parameters, listen to the slightest aberrations, see things not visible to the naked eye and initiate remedial measures in real-time. However between availability of technology and deployment at the shop floor, there are many questions that need to be answered. For the Cover Story this month, we asked a panel of experts their views on some of these questions, starting with the obvious – which forthcoming advances in technology will impact industrial automation?

“The convergence of IT/OT has been the basis of rapid progress for successful Digital Transformation for the manufacturing industry. This has led to increasing demand for tighter integration and more information that leverages Industrial IoT, Industry 4.0, 5G, Cloud, Edge, Additive Manufacturing, Advanced Analytics, Digital Twin, AR/VR, AI, ML and other emerging technologies, says Ramnath S Mani, Managing Director, Automation Excellence. A serial entrepreneur and one of the pioneers in India in the field of Industrial Automation in India, Mr Mani has considerable experience in implementing the transition of Industrial Automation from Analog to Digital Technology. “Integration of Power and Automation along with Convergence will lead to getting information about electrical assets and the production process to help improve sustainability across the entire lifecycle of increasing demand for tighter integration and more information,” he adds.
 

“Devices with Cyber Physical Interface will impact industrial automation, meaning it is having features of IoT and connectivity, data collection, knowledge base, modelling, edge intelligence, Visualisation and many more advanced features,” opines S Gangadhararao Boppana, Co-Founder & CEO at STABILITY. Gangadhararao Boppana is a Techno Entrepreneurial & Data Science professional with remarkable capacity for innovation, product development, leadership, and business growth, working toward enabling AI & ML based technology solutions in manufacturing eco systems. 

To Namrita Mahindro, Chief Digital Officer, Aditya Birla Group, the shift from operational excellence to operational resilience has become a key priority for businesses as business risks continue to multiply be it the pandemic, cybersecurity threats, additional regulatory changes or enhanced safety requirements in the wake of Covid over the past two years. A digital evangelist, Namrita’s forte lies in creating competitive advantage for organisations by re-defining business models, re-imagining customer experiences, re-engineering business processes, building people capabilities and orchestrating shifts in mindsets and organisation culture. According to her, Industry 4.0 technologies like IIoT, AI/ML, Advanced Analytics, AR/VR, Robots, Additive Manufacturing, Blockchain, Cloud, and 5G are at the heart of the current industrial automation. “The vision is to shift from a hybrid automated plant to a self-optimising, connected, autonomous plant, which can change the competitiveness of companies and nations by increasing productivity and fostering greater growth. This will be enabled by gathering and analysing data across connected machines with an integrated production line, enabling faster, more flexible, and more efficient processes to produce higher-quality goods at reduced costs and lower error rates,” she explains.

There is a broad consensus among the panellists regarding technologies most likely to impact industrial automation in the near future. For Mihir Punjabi, Director – AI/EI Solutions Architect, these are digital twin, advanced robotics, embedded/edge AI and 5G. Mihir is a solutions architect and a techno-business strategist who is passionate about solving business problems. He has presented papers at various industry forums and is currently responsible for AI solutions in Manufacturing within the Capgemini Engineering group. “5G is very interesting and could completely transform the automation possibilities by enabling near edge real-time use cases, which were not possible to implement earlier due to latency issues. This will be very critical to ‘intellify’ brownfield deployments. For instance, a server in the factory could handle vision-based quality inspection and recommend actions in real-time over 5G. Currently, this is being validated in lab environments by various organisations,” he emphasises. “While all these could impact the industry, I believe digital twin and edge AI would be the two interesting ones to watch out for,” he adds.  

Leveraging AI/ML
How are manufacturing industries leveraging AI/ML? How do automation controllers provide the necessary platform?

“With data becoming increasingly accessible, more companies in the manufacturing industry are leveraging AI and ML to enable scenario planning, demand forecasting, and inventory positioning, and also increase accuracy and reduce time to complete a task. Companies within the manufacturing sector too are now looking to leverage technology – like chatbots or other AI-driven comms tools – to help minimise response-time delay without added stress on the already-depleting and overwhelmed staff,” says Raj Ravuri, Director, Industry Advisor – Manufacturing, Salesforce India. He is responsible for Customer CXO advisory for transformational initiatives, and the Go-To-Market strategy for Manufacturing sector across India, and Asia Pacific region. Raj has 25+ years of experience in the IT industry across Salesforce, Wipro, KPMG Consulting, etc., advising Fortune 500 organisations across the Manufacturing sector in USA, Europe, and Asia Pacific. “One crucial aspect that businesses are discovering fast in a post-pandemic world, is that there are serious knock-on effects across all industries, especially with regard to supply chains. The ongoing crisis calls for constant communication across customers, dealers, distributors, logistics providers and part suppliers to help understand and address supply chain and operations issues as they arise,” he adds.

“Within a plant, data is everywhere – terabytes of data are generated every second. But data has never been a problem – what to do with it has been the question which has been of primary interest to manufacturing companies for over a decade,” says Sanjay Guhathakurta, Industry Leader, Industrial Products, IBM India. An alumnus of London School of Business, Sanjay has several years of experience in working with various global clients in the Manufacturing space, helping them in their journey on Digital Transformation. Sanjay is a passionate public speaker, having represented in various platforms from Chamber of Commerce and Universities, and holds two patents in the field of Applicability of Artificial Intelligence in Manufacturing. “We are living in the times where data, and its usage through predictive modelling is helping manufacturers to make decisions with far more accuracy and without manual dependency,” Sanjay elaborates.
 
 

Vic Gupta, Executive Vice President – Digital, Coforge, shares the view that AI/ML is finding increased use in manufacturing industries, and cites a couple of important use cases where AI/ML is being used, viz., Predictive Maintenance and Prescriptive AI. “Predictive maintenance can help determine the condition of in-service equipment and estimate when the maintenance should be performed. It can help raise alerts of potential issues before they occur and thus, reducing the downtime. By analysing historical instrumentation data, and data for all the variables that impact the product quality and production efficiency, Prescriptive AI can identify the conditions that lead to the most profitable production run. The automation controllers can provide real-time feedback for any anomalous behaviour of the equipment that can help prevent production defects. These controllers that are equipped with AI can help support the use cases mentioned above,” he explains. As a technology, people and transformation leader with experience in cloud sales, marketing, management and operations, Vic Gupta has consistently built strategic, sustainable & mutually benefiting Alliances & Partnerships with ISVs and SIs in multiple geographies. He has extensive C-Level executive exposure to lead virtual and cross-functional disparate teams.
 
Data gathering
AI and ML need massive amounts of data to be gathered by IoT devices. What strategies do industry plan to collaborate in data collection?
 
According to Ramnath S Mani, the matter of collecting data from Edge Devices and legacy systems that include all types of Sensors, PLC, Motors, Drives, etc., and other devices that control the production parameters of the machines that are used in production lines, has always been a challenge for the OT-IT integration. The more common way of getting data out of smart sensors is to use a bridging gateway, which receives data from the devices and sensors, and makes it usable. “The process of collecting and processing data locally at the Edge with IoT technology saves storage space for data, processes information faster and meets security challenges. One of the efficient methods of collecting Data and using it effectively is to de-link Devices from Applications. This can be done through a MQTT broker that works in a Publish – Subscribe mode. Any number of Devices and any number of applications can be connected for AI and ML applications without loss of any data in this model,” explains Ramnath Mani.
 
“AI and ML help manufacturing companies for creating prediction models for machine predictive maintenance, production, orders, and consumption forecast, which can help to increase production rate and reduces machine down time and help to improve machine performance, and using data visualisation tools will help to visualise data to make corrective decisions and analyse operations using real time data, with the help of real time data will increase forecast efficiency,” says S Gangadhararao Boppana. Also, with the above methods we can automate the supply network, i.e., without human intervention orders can be taken and proceed and delivered to customers,” he adds.
 
“In the manufacturing sector historically a lot of data is being captured at plants. A lot of this has now been digitised. More importantly all these disparate systems are now being connected to speak with each other,” says Namrita Mahindro, and points out that the self-optimising connected plant will have digital twins at the heart of the operations to enhance productivity and a shift from scheduled to predictive maintenance. “The data centralisation and central asset repository will allow users to have access to the data and assets they require ‘on demand’ across plants to ensure that there is a single view of any form of development and reuse to existing assets and data is maximised. This can subsequently be opened to the ecosystem of partners (customers and suppliers). There is also an opportunity for creating industry level platforms with private and public sector partnerships which can be a win-win for all stakeholders,” she adds.
 
Automating supply chains
How can AI and ML help companies create predictive models, analyse operations, make accurate forecasts, and automate supply chains?
 
“AI/ML can learn from huge amounts of historical data and can adapt to dynamic situations. Benefits from historical data can be leveraged if operations are repetitive in nature. As part of Industry 4.0, industries are focusing on making operations repeatable, optimised and autonomous. This is enabling AI/ML models to predict and forecast accurately and bring significant value. AI/ML is already being leveraged for use cases like predictive quality, predictive maintenance, smart scheduling, and forecasting sensor values,” says Mihir Punjabi. This, in his view, enables organisations to make decisions based on insights rather than intuitions.  
 
Raj Ravuri, believes a major factor in building supply chain resilience is digital transformation. Across all industries, Cloud-based applications AI/ML, IoT and automation are coming together to transform how businesses operate and inter-operate. “At Salesforce, we have many of the tools you need for a holistic view of the supply chain – whether it’s remote customer engagement and service support, data analytics for customer insights or collaboration tools we can help manage any supply chain issues in near real-time as they arise. Organisations need to act decisively now to integrate resilience into your supply chain to ensure against disruption from the next shock whenever it arrives. It must become second nature for manufacturers, pharma and other industries to have end-to-end insight across the entire supply chain,” he elaborates. This ensures that manufacturers can respond to any risk of disruption before it happens. 
 
Realising the full potential
The full potential of AI and ML is realised only when the scale of operations is big enough. How can the average SME benefit with their limited resources? 
 
According to Sanjay Guhathakurta, while large manufacturers operate with larger budgets, manufacturers in the MSME segment can leverage predefined AI and ML frameworks. “Intelligent Cloud offerings developed for the manufacturing industry and built leveraging industry benchmarks, can easily get manufacturers in the MSME segment to get started in AI/ML adoption,” he suggests.
 
Vic Gupta, also believes adoption of AI does not need to be limited to large corporations. SMEs can also leverage AI/ML to effectively delegate routine tasks to be done more effectively, freeing up their critical resources for better business purposes. “It is always important to look at the Return on Investment rather than just the cost of investment while deciding what processes can be automated with the help of AI/ML,” he emphasises.
 
Ramnath Mani takes a more nuanced view. “The scale of operations has to be big enough to justify the investment in AI and ML as the Capex needed to implement is large. SMEs in India are either dedicated multiple tier vendors to large manufacturers forming a ‘Hub & Spoke’ model or independent direct-to-market vendors,” he says. According to him, in the former case, it is possible to be a ‘Spoke’ partner to the ‘Hub’ manufacturer and use the Asset of the main manufacturer to form an ecosystem. This will entail a much smaller investment on part of the SME mainly at the Edge while taking advantage of the investment of the main manufacturer. Such a Model can be possible between the manufacturer and a dedicated SME vendor with a long term relationship. However, in the latter case the only way seems to a Subscription based SaaS model where the Service Provider has the major investment and he is able to provide multiple SME vendors a Subscription based Service for the usage of various functions like Track & Trace, OE and  solutions based on AI and ML technologies.     
 
Skill development
The human element remains critical in deployment of new technologies. How is skill development to be planned in a scenario of not yet mature technological advances? 
 
Agree, says S Gangadhararao Boppana. “The human element is very critical in developing and deploying new technology solutions, there is hype in the outside market, i.e., job opportunities will reduce due to AI and ML, practically it is not true due to technology advance job and learning opportunities will increase in the market. AI and ML will not replace humans, and it will help humans for faster decision making,” he explains.
 
“Over the last decade that I have spent in the transformation space, the focus has been predominantly on following a ‘Build Operate Transfer’ model,” says Namrita Mahindro., referring to the kill development aspect. “Whilst a specialist team initiates the transformation journey the key to transforming the DNA of the organisation lies in ensuring that the existing business teams are re-skilled and upskilled to embrace the new environment and champion the new way of working. This happens through a three pronged approach: Re-skill, Re-imagine and Re-invent,” she emphasises.
 
“For working executives, reskilling is a must. Organisations need to promote and support the right executives (in planned batches) to explore new technologies and encourage them for training/certification. But I believe all this should be in a pure hands-on mode. The executive needs to build a working and presentable demo/asset as part of this exercise which should position their organisation as an early adopter,” suggests Mihir Punjabi. 
 
According to Raj Ravuri, the decisions companies make now to solve the digital skills gap will echo for a generation. “A commitment to bridging the widening digital skills gap is fundamental to our world’s future success and prosperity. And doing so in a manner that promotes equity and effectively leverages untapped talent within the workforce,” he states.
 
“While there is still a significant difference in the factory automation factors between the manufacturers in Europe and China, versus India, the gap is reducing. Keeping that as an observation, it is important that manufacturing companies invest heavily in training their human workforce towards adapting to factory automation and 'factory of the future'. Understanding the nature of data, the study and intelligent decision-making based on predictive suggestions, are still key manual interventions that every manufacturer would rely upon,” sums up Sanjay Guhathakurta. 
 
To Vic Gupta, conceptualising, designing, building and maintaining AI enabled systems requires people with the right skills, a big-picture perspective and the skills to collaborate towards the common goal. This may require upskilling the experienced process and automation engineers who have been working in manufacturing for a long time and hiring experienced people in the area of data science, data engineering, cloud engineering from outside. “Putting together this team itself requires a mature thought process of the leadership and the right technology partners who can guide the team towards their common goal," he concludes.
 
(Note: The responses of various experts featured in this story are their personal views and not necessarily of the companies or organisations they represent. The full interviews are hosted online at https://www.iedcommunications.com/interviews)