Industrial Automation Magazine India

Adoption of AI does not need to be limited to large corporations

Published on : Tuesday 08-02-2022

Vic Gupta, Executive Vice President – Digital, Coforge.

Which forthcoming advances in technology will impact industrial automation?
Artificial intelligence (AI) enables industries to collect data from the instrumentation, learn and identify patterns and make sense of this data, and help drive informed decisions. This enables adapting to the new situations much quickly and leads to better production outcomes.

One of the important steps helping this adoption of AI in industrial production is the progress in intelligent sensors and instrumentation that provide the ability to push the data to the cloud without adding significant capital and engineering cost. This improvement in the availability of the data to the Machine Learning (ML) algorithms, has improved the performance of the AI tools and thus enabling better decision making.

How are manufacturing industries leveraging AI/ML? How do automation controllers provide the necessary platform?
AI/ML is finding increased use in manufacturing industries. Some of the important use cases where AI/ML is being used are:
a) Predictive maintenance – It 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.

b)  Prescriptive AI – 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.

AI and ML need massive amounts of data to be gathered by IoT devices. What strategies do industry plan to collaborate in data collection?
One of the important aspects of adopting AI in manufacturing is to define a data strategy. That is how and where the data is going to be stored, the communication protocols to be used by devices to talk to each other, exchange data and the data protocols that define how the information is going to be sent and received. The manufacturing companies should push for open standards of interoperability in consultation with the technology companies.

How can AI and ML help companies create predictive models, analyse operations, make accurate forecasts, and automate supply chains?
AI driven tools/devices can help analyse production data, customer data, suppliers’ inventory, use predictive modelling to predict supply and demand and help find the right balance that leads to the most profitable production run.
 
The full potential of AI and ML is realised only when scale of operations is big enough. How can the average SME benefit with their limited resources? 
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 purpose. 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.

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?
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. 

(The views expressed in interviews are personal, not necessarily of the organisations represented)

Vic Gupta is a technology, people and transformation leader with experience in cloud sales, marketing, management and operations. Consistently built strategic, sustainable & mutually benefiting Alliances & Partnerships with ISVs and SIs in multiple geographies. Extensive C-Level executives exposure to lead a virtual and cross-functional disparate teams.