How AI has made industrial manufacturing industrial again

By Shankar Narayanan, Head of UK&I at Tata Consultancy Services (TCS).

Industrial manufacturing has a come a long way in the last century to shake off its image of noisy production lines and oily machines. For an industry that pioneered the use of robotics, it's no surprise to see technology pushing this sector ahead in the 21st century.

Today, there is one innovation set to have an even greater impact on how industrial manufacturing operates in the digital era: Artificial Intelligence (AI).

AI has already helped industrial manufacturers automate the work on the factory floor, improve production quality and productivity.

For instance, Procter & Gamble is successfully reaping the rewards of integrating AI into its factory operations. The company, operating 130 plants worldwide, has seen AI cut unplanned downtime by 10 percent.

And there's more to come, according to industry leaders.

With AI set to continue revamping industrial manufacturing, it's vital that leaders think carefully about where the impact will be most acutely felt and how they can make AI's influence a genuinely positive development for the sector.

AI in Practice

With this influence in mind, TCS recently conducted a major study into AI across 13 industry sectors. Across industrial manufacturing, 85% of business leaders said they were already using this technology. Fast forward a few years and every executive responding believed they will incorporate AI into their operations at some point along the value chain by 2020. It seems clear that AI has the ability to continue to influence industrial manufacturing.

The sector has historically been quick to adopt new ways of delivery. It was first to recognise the power of robots on the assembly line. This heritage continues today.

For example, Donnelly Custom Manufacturing, a maker of injection moulding for thermoplastics, is one of the many manufacturers deploying robots on the factory floor. In its US plant, the robots do jobs like removing parts from a conveyor belt and stacking them, counting finished products and packing products for shipment.

Beyond the factory floor output, AI has the ability to shape products by mining data to maximum effect. For instance, aircraft maker Boeing and Carnegie Mellon University invested $7.5 million to apply AI to improve the company's products. Boeing wanted AI to help its customers improve their maintenance of planes.

A typical 787 Dreamliner aircraft has thousands of onboard sensors, text issued by mechanics and pilots, and databases on the condition of the plane that all provide data for preventive maintenance. AI enables the customer to use machine learning to extract data that ultimately improves the maintenance, and reduces potential costs and downtime, of future product design.

ROI on the Production Line

Investment in AI is not just about designing innovative aircrafts of the future. There is an even stronger business case the industry can't afford to ignore. The main driver for this is return on investment. In fact, based on the TCS research, manufacturing executives found that investment in AI helped them to reduce production costs by 8%. Additionally, industrial manufacturers reported a 12% average revenue increase when using AI for specific projects.

Although industrial manufacturers are not spending as much as some other sectors, such as technology, bigger spending is forecasted for the future. For example, industrial manufacturers spent an average of $62 million in 2016 on AI initiatives – representing a 24% increase from 2015.

Beyond cost savings, the industry must also consider how AI affects job retention and creation. Factory employment in roles such as assembly line work has been shrinking for years, and the reality is that automation continues to influence this trend.

The other side of the coin is that manufacturing companies expect to create new jobs as a result of deploying AI technology. Our study found that using AI in certain business functions could increase the number of jobs by on average 10%. Manufacturing executives predict a gain of 13% in jobs in 2020 and 16% in 2025. With regard to these new jobs – many that don't yet exist today – companies believe they could fill 55% with current employees and 45% with new hires.

Industrial manufacturing executives agree widely on the overall importance of AI technologies to company competitiveness by 2020. In this group, 24% of respondents call it highly important, and 45% say it is important. Some 24% say moderately important, while just 7% say slightly important. No industrial manufacturer that we surveyed believed AI was not at all important.

The Future

With global supply chains now the reality for major multi-national businesses, industrial manufacturers have been forced for several years to think about their sector internationally. Competition can come from every corner of the world, as China's dramatic growth in recent decades and the development of other BRIC nations attests. Staying ahead of the curve, competing not on cost, but on quality, innovation and value is where AI investment can have a major impact.

There is evidence to suggest that AI is improving industrial manufacturing output, especially in developed markets where is it driving cost-savings and, to a degree, creating new jobs. However, there are challenges ahead that could disrupt the assembly line.

Making IT systems robust enough against the risk of security attacks is of paramount importance for the industry, as highlighted by our study. Other issues like the willingness of workers to learn new skills and practices due to AI integration was also reported as being a challenge to the technology's development.

The ability of AI to improve business processes is clear. Our study shows that about a quarter of industrial manufacturers use AI in finance and accounting (27%), corporate-level decision making (26%), marketing (25%), and strategic planning (22%).

As the production line gears up to maximum speed, manufacturers must make the decision to further invest in AI or ignore its potential. The latter option could see the industry going back to practices associated with previous eras and will only see competitors from new manufacturing economies take the lead. There is a strategic choice to be made about where and how to invest in this new technology trend, but it's clear that standing still is not an option.