Making AI Work for Equipment Manufacturers

Nov. 8, 2018
Artificial intelligence is fueling growth across many business sectors, including industrial equipment. But OEMs must learn quickly if they hope to adapt the technology to their operating capabilities and optimize its competitive benefits.

Achieving success has never been more challenging for industrial equipment manufacturers. Not only are they faced today with macroeconomic volatility,shifting demand for products as services, and the ever-greater pressure to innovate, they must also find new ways to grow. Harnessing the power of artificial intelligence (AI) is one of those ways.

AI is rapidly becoming the fuel for growth across business sectors, including the industrial equipment industry. Applying the right combination of AI technology—a collection of digital advances, such as machine and deep learning and analytics—can help companies grow by enabling them to operate at unprecedented speed and scale, reduce cost and enhance the customer experience. Accenture research suggests AI will add approximately $3.7 trillion to the manufacturing sector by 2035.

However, while OEMs surveyed say they plan to invest heavily in AI technology over the next three years, until now many have lagged behind companies in other industries, such as financial services, retail, media, and healthcare, that have already forged successful AI initiatives. OEMs’ hesitance could prevent them from fully realizing the value of AI, as faster-moving global, cross-industry companies are already snapping up required AI talent, patents and start-ups that might otherwise help Industrial equipment-makers become market leaders.

Some trailblazing OEMs are showing the sector the benefits of AI through real-world applications. Typically, these applications are underway across the value chain and in areas such as electrical and heavy equipment, automotive supplier parts and consumer durables. But, while these are positive developments, it will be up to companies that are trailing the competition to accelerate their AI efforts to win.

The key challenge for companies that aggressively pursue AI will be to achieve interoperability. This means creating seamless integration between AI and the organization’s IT infrastructure, forging optimal worker-machine and ecosystem partner collaboration, and adjusting the organization’s capabilities to accommodate a product-as-a -service business model.

Optimizing AI

OEMs will need to develop new operating capabilities in key areas of the value chain to make the most of AI.

  • Procurement and talent: OEM supplier strategies will need to become more technology-centric—enlisting the aid of telematics, onboard software, wireless connectivity and analytics providers. In addition to production engineers, more digital specialists and data engineers will need to be added—a significant challenge with these skilled resources in short supply.
  • Design and manufacturing: These areas will need the core algorithms behind machine and deep learning developed, and the creation of AI-embedded products. Generally, more complex design processes and sophisticated prototypes will also be required. Feeding captured data from AI-enabled products back through research and development in a continuous feedback loop will help improve the product development process. And as AI-enabled robots and machines transform industrial operations, the manufacturing workforce will need to be reskilled to collaborate with them, while legacy machines and equipment will require retrofit solutions.
  • Product-as-a-service capabilities: Leveraging smart, connected products capabilities into services will require the building of new capabilities. Solution configurations, pricing and quoting will need to be developed to support the multiple customization options these services will offer. Sales personnel will have to keep in mind customer usage and changing solutions over service lifecycles. Also, warranties and entitlement systems tailored to the as-a-service model will be needed. Information technology infrastructure support that analyzes customers’ ongoing service needs also will be required.
  • Sales and marketing: Accenture believes the role of marketing will become extremely important as companies shift from products to services.Moving away from the mindset of selling products to promoting AI-driven intelligent sales solutions will be essential. New approaches for identifying and training industrial equipment dealers will need to be developed, and this includes placing greater emphasis on technology upskilling for the sales and marketing workforce. Marketing messages also will need to focus more on smart digital and AI-enabled features and less on traditional areas such as engineering.
  • After-sales service: The entire field force will need to become embedded in their customers’ operations and much more hands-on with the goal of resolving issues well before the customer is aware of them.

Realize the possibilities

Industrial equipment manufacturers have barely scratched the surface in realizing the potential of AI. Those that do will have a chance to lead in the market and become what Accenture refers to as Industry X.0 businesses—organizations that can extract the full value of new technologies.

>>Brian Irwin, [email protected], is managing director, Industrial North America at Accenture. Eric Schaeffer, [email protected], is senior managing director and head of industrial and products Industry X.0 consulting at Accenture.

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