Skip to main content Skip to Footer

Manufacturing the future

Artificial intelligence will fuel the next wave of growth for industrial equipment companies

OVERVIEW

Bombarded by a host of disruptive forces, manufacturers are under mounting pressure to innovate and find new sources of growth. What’s more, the whole industrial equipment sector stands on the cusp of massive change and reinvention. So, what does the future hold?

Based on in-depth analysis from Accenture Research, we’ve built up a detailed picture of the challenges facing industrial equipment companies and the way Artificial Intelligence (AI) technologies—now coming of age—can be adopted to overcome these challenges, support innovation and drive new growth. It’s a unique preview of tomorrow’s industry.

Take a look at the future of industrial equipment.

DOWNLOAD THE FULL REPORT [PDF]

DISRUPTION AHEAD

Life for industrial equipment manufacturers has never been more challenging.

Globally, the sector is being hit by multiple forces and trends. As if dealing with macroeconomic and political volatility wasn’t tough enough, they also must adapt to an ever-changing range of disruptive digital technologies—predictive analytics, additive manufacturing and the Industrial Internet of Things, to name just a few.

What’s more, they need to constantly reimagine how they function in the digital era, from creating a connected workforce to enabling predictive maintenance.

And all this must be done when industrial consumerism is on the rise, with consumer-style expectations permeating every part of the value chain.

The net effect of all this? Intense pressure to innovate. NOW. 85 percent of industry executives agree they must innovate ever faster just to keep a competitive edge.

It’s why AI is such a crucial capability.

WAKING UP TO THE POTENTIAL OF AI

AI’s rapid evolution is happening at the right time for the industrial equipment sector. Accenture’s research points to AI adding approximately US$3.7 trillion to the manufacturing sector by 2035. It has the potential to help companies operate at unprecedented speed and scale, reduce costs and transform customer experiences for the better.

Although industrial equipment companies say that they plan to invest heavily in the technology over the next three years, up to now they’ve lagged behind companies in other industries like financial services, retail, media and healthcare. Observing successful AI initiatives in other industries, they’re now starting to invest themselves. The key to success is a collaboration with the wider AI ecosystem.

For their AI investments and strategies to remain competitive and differentiated, industrial equipment companies will have to move their thinking beyond short-term gains and in-house siloed deployment plans. Instead, they’ll need to embrace a more holistic strategy based on identifying "best fit" AI partners, investing in broader ecosystem partnerships and collaborating efficiently within them.

71% of IE execs believe AI will have a significant impact on their organization
78% of IE execs believe AI will have a significant impact on the Industrial Equipment industry
Complete transformation

RECOGNIZING
THE CHALLENGES

The benefits for industrial equipment companies of using AI are not in doubt.

Whether it’s digitally focused innovation, enhanced user experiences, whole new levels of operational efficiency, or a completely new competitive edge, the technology holds huge potential for companies willing to make the jump to intelligent operations.

But the risk and challenges must be understood. Accenture Research analyzed key themes to reveal the most pressing concerns. The findings show that the uppermost concern is AI’s impact on employees’ jobs (38 percent)—and not just in entry-level positions. Concerns about security threats (24 percent), data privacy (19 percent) and maintaining compliance (17 percent) with an evolving regulatory environment also feature highly. Addressing these concerns is a top priority.

To overcome these challenges—along with issues like integration/compatibility between AI and current IT infrastructures, lack of expertise in AI technologies and data quality—companies need to develop a completely new set of operating capabilities.

THE WAY AHEAD

New operating capabilities are needed right across the value chain. Our report pinpoints these must-have new capabilities in:

Supplier strategies will have to be redefined to become more technology vendor-centric. New sets of suppliers will have to be identified, outside traditional supplier clusters. These will include providers of telematics, onboard software, wireless connectivity and analytics. And in addition to their focus on production engineers, companies will need to hire more digital specialists and data engineers –a significant challenge with these skilled resources in short supply.

Developing the core algorithms behind machine learning/deep learning and developing AI-embedded products will be a big ask for IE players. More generally, complex design processes with more sophisticated prototypes will need to be developed. And data captured from AI-enabled products will have to be fed back to R&D to further improve the product development process through a continuous feedback loop. As a range of AI-enabled robots and machines revolutionize industrial operations, the manufacturing workforce will need to be reskilled to work alongside them, while legacy machines/equipment will require "retrofit" solutions to give them a "second life".

Companies will have to move to a more consultative selling approach, moving beyond the mindset of selling products to working with AI-driven intelligent sales solutions. New approaches for identifying and training dealers will have to be developed, and there will be a greater emphasis on technology upskilling for the sales and marketing workforce. Overall, marketing messages will focus more on smart digital/AI-enabled features and less on traditional areas like engineering and safety.

Dealers and after-sales service shops will need to have technology engineers readily available to solve maintenance problems in embedded systems.

Data will hold the key to successful development of these capabilities. Companies must move now to be ready to capture information in numerous formats and from numerous sources – assets, employees, customers, weather conditions…the list goes on. They must have the right data management systems and processes in place, and the algorithms they’ll need to generate game-changing insights.

And they must be ready to digitally engage with their customers, dealers and employees via AI-powered AIs, workflows, apps and dealer interfaces.

AUTHORS