Finding the return on investment in smart manufacturing, digital transformation

Smart manufacturing and digital transformation continue to gain traction in factories, process facilities and other applications.

By Matt Newton September 7, 2021
Image courtesy: Brett Sayles


Learning Objectives

  • Digital transformation is about advancing and uncovering new opportunities for sustainability, efficiency and productivity.
  • The overall tactical objective in achieving digital transformation is creating a real-time operational control loop that manages based on information and analytics.
  • An open, system-agnostic approach provides long-term values and lowers total cost of ownership (TCO) for the user.

The COVID-19 pandemic forced radical lessons on companies about how to run and optimize systems in unpredictable times. Global organizations have been compelled to put technology at the heart of their business, speeding up digital transformation. As companies and industries work to hire and retain the appropriate staff after a disruptive year, questions around return on investment for future projects are not unexpected.

IDC estimates that spending on Industry 4.0 technologies, such as augmented reality (AR), virtual reality (VR) and robotics will surpass $1 trillion in revenue, led by the manufacturing and transportation industries.

Looking beyond the buzzwords, digital transformation, at its core, is about advancing the business strategy, improving operations, and uncovering new opportunities for sustainability, efficiency and productivity.

To be successful, a company needs to improve profitability and return on capital across the asset and operations value chains.

Supporting the larger business strategy

By outlining clear financial and operational aspirations, teams can demonstrate how the investment creates real business value. A McKinsey & Company report looked at a select group of industry-leading manufacturers using digital transformation to enhance operations. The benefits recorded by these companies include 30 to 50% reductions in machine downtime; 15 to 30% improvements in productivity; and 10 to 20% decreases in the cost of quality. By aligning technology solutions with business needs, it can clarify how the transformation supports company-wide priorities and brings into focus why investments should be made.

For example, Duke Energy implemented predictive analytics software as part of its program to avoid catastrophic failures at power plants. The software leverages high fidelity data from more than 30,000 sensors to develop more than 10,000 models to catch asset failures long before they occur. More than 500 finds over three years has helped the company, conservatively, avoid more than $100 million in repair costs.

Data is a priceless and strategic asset for operations, customers

Every digital transformation journey needs to begin with the critical understanding that information and data have become a priceless and strategic asset to the enterprise. The faster a team can collect, visualize and analyze data, the faster it can take action that benefits operations and customers. The overall tactical objective in achieving digital transformation is to create a real-time operational control loop that manages an enterprise based on information and analytics.

For example, one of the world’s largest industrial gas manufacturers closed its data loop with predictive asset analytics. Prior to a scheduled maintenance outage, the plant identified a vibration sensor anomaly. This allowed technicians to investigate a turbo engine compressor further and discover a cracked impeller. This early catch prevented reactive maintenance and unplanned downtime for a total savings of $500,000.

Catching industrial asset failures before they occur

As supported by the examples above, a study of common failure patterns by ARC Advisory Group found 82% of failure types are random. Only 18% are predictable and can be prevented using traditional maintenance methods.

Machine learning (ML), for instance, helps identify inefficiencies and abnormalities in equipment operation long before regular inspection. Engineers can reference operational models and digital twins for recent abnormalities in design versus operational performance. This capability becomes increasingly powerful when combined with advanced visualization and control technologies, such as web-based human-machine interfaces (HMIs), supervisory control and data acquisition (SCADA) systems and AR/VR.

In a large regulated and non-regulated utility with more than 60 plants in six states, including coal, simple cycle combustion turbines, combined cycle and integrated gasification plants, predictive analytics software was applied to helps monitor and optimize the maintenance of critical power generation. An early warning of a crack in a turbine rotor saved the utility more than $34.5 million.
Digital solutions enable companies to enhance capabilities, increase reach and maximize returns. An open, system-agnostic approach drives long-term value and lower total cost of ownership.

Matt Newton is director, asset performance portfolio, Aveva. Edited by Chris Vavra, web content manager, Control Engineering, CFE Media and Technology,


Keywords: digital transformation, asset analytics


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Author Bio: Matt Newton, director, asset performance portfolio, AVEVA