The hi-tech manufacturing industry is exactly that – one that employs a high degree of technology that is often complex, asset-heavy with lots of assembly line activities that lends itself well to adopting new and evolving technologies. It is no surprise that it is one of the highly automated industries. In a survey conducted by Infosys titled, ‘Leadership in the Age of AI’, 77% of the respondents from the manufacturing/hi-tech Industry said they were using AI to automate their business processes.
Yet, there is scope for more.
To know about the promise of automation in the Industry and where it is headed, read the Infosys Point of View, Machine Learning and AI: The Now and Next in High-tech Manufacturing Landscape
What’s the Challenge
While we have seen dramatic changes on the shop floor with routine jobs like assembling or high-risk jobs like repairs in a hostile environment being taken over by robots, three out of four organizations feel there is a lot more they could automate. 84% of the respondents in a research commissioned by Infosys titled, ‘Human Amplification in the Enterprise’ did not accomplish their digital transformation goals.
What’s stopping organizations from achieving the full potential of automation?
The hindrances vary from lack of necessary skills and in-house knowledge to drive automation projects, to what is the value proposition of the new technologies. From lack of collaboration between various teams to financial constraints. And lastly, there is the challenge of resistance to change and fear of unemployment.
What’s More to Automate
While routine, repetitive and labor-intensive jobs have been hijacked by automation in most hi-tech manufacturing industries, the more aspirational automation opportunities lie in areas of customer support, processing of complex data (both structured and unstructured), creation of simulations for better decision making, creation of systems that self-learn and take decisions and, lastly in streamlining enterprise knowledge.
Automation has evolved over the past few years with a new generation of robots that are powered by AI and Machine Learning. These new age robots can be trained, meaning they are much more flexible than they were earlier and can be used for a variety of purposes. They are smarter. With Machine Learning, they are teaching themselves better ways to do things and address exceptions and anomalies. They are cheaper. Automation is becoming cheaper as technology evolves making it more feasible than the current wage rates.
A Successful Approach to Automation Using AI and Machine Learning
Considering that technology is ever-evolving for the hi-tech industry, one of the primary considerations for adopting automation is the technical feasibility. However, organizations also need to ask questions around – how expensive or complex is the development and deployment of AI-based automation, is labor substitution worth the cost, does it meet ethical requirements, is it sustainable, will it increase productivity, reduce errors, improve quality, and save costs?
A successful approach to adopting these new technologies includes:
Draw a roadmap for the AI-based automation at the organizational level and provide guidance to all implementation activities that tie up to it.
Define clearly the roles and responsibilities of people who are part of the automation initiative along with training plans for re-skilling and upskilling. The management plays a key role in visualizing the value proposition of adopting technology that results in job losses.
Identify roles and skills that need to be reworked. Facilitate and empower employees to move to more value-add or cognitive roles.
Establish interoperability and integration between legacy systems and new systems. Implement the required compliance and security measures.
Lastly, make sure the automation plan aligns with the larger digital strategy of the business.
