Expert Opinion: Kevin Prouty

Group Vice President, Energy and Manufacturing Insights, IDC


3 March 2021

3 min read

For manufacturers seeking resilience, superior data management enabled by AI-driven virtual twins holds the key. IDC’s Kevin Prouty explains.

Over the past few months, one quality has separated successful manufacturers from struggling ones: resilience.

During one of the most unpredictable periods of our lifetimes, resilient manufacturers – those with the infrastructure and components necessary to turn on a dime and react to new market adjustments and demands – have been quick to adapt and bounce back even stronger.

As we head into a post-pandemic world, manufacturers are striving to build resiliency in all aspects of their business, and we’re already seeing progress. In May 2020, as the pandemic was deepening, our research found that only 5% of companies felt they were resilient enough to navigate an unknown future. However, our findings from October show that figure sitting at 40%.

While this is an incredible shift over just six months, the sobering fact remains that 60% of manufacturers still need to develop and execute a strategy for how to transform their business to achieve resilient decision making.

Ineffective data management is at the crux of the problem. Most organizations today struggle to manage the amount of data that is currently available to them, with only a fraction of it adequately utilized. Even the most operationally mature organizations can be very quickly overwhelmed, not just with the data being generated but by the demand from employees who need to consume it.

This problem will only accelerate going forward. The amount of data generated in industrial companies is expected to increase by as much as 400% over the next five years, thanks to more connected operational assets, video as a sensor, supplier information and connected product information. How do you deal with this? One strategy would be to reinvent the role of middle management. It’s important to understand that it can take days, weeks and even months for the information that senior decision makers need to make its way up through layers of staff, with each layer collating, analyzing and interpreting data before passing it up the chain. Information on the way up also has to pass through several organizational layers with biases shaped by personal goals, key performance indicators and incentives.

“The amount of data generated in industrial companies is expected to increase by as much as 400% over the next five years.”

Artificial intelligence (AI) represents the largest area of untapped potential to address the operational data issue and the inability of middle management to effectively operate in a data-intensive but insights-starved environment. AI-based automation can flip decision making from human-led to machine- supported, or even machine-led, processes. AI is the foundation for a more resilient decision-making process that is faster and more effective. Making this transition will require massive change. But it doesn’t have to be daunting and it doesn’t have to happen all at once.

The first step for manufacturers is to digitalize as many of their assets and processes as possible so that the necessary data is available for an effective decision-making framework. This will help inform an intelligent model representing the physical world through data, otherwise known as a virtual twin. A business innovation platform can take contextualized data to create virtual twins of any physical product, asset, design, process or operation, providing visual context that gives data instant meaning.

Virtual twins tie the physical world, which requires human interaction and institutional knowledge, to the digital world, where AI can support or lead decision-making, thus supplementing, informing and guiding middle management in the resilient decision-making framework. The AI dependency of the decision-making framework is related to the size of the data sets, the complexity of the multivariate relationships in the data and the speed at which data will be ingested.

Like nature, resilient manufacturers thrive in difficult conditions. (Image by Louisiana Photography/AdobeStock)

What’s important to recognize here is that no human-dependent process can ingest today’s massive amounts of data and generate results in the time needed for a decision to be effective. But a business innovation platform can do all this, and present decision options to senior management instantly via a real-time dashboard – facilitating the automation of the monitoring and troubleshooting processes.

While it may be a long road to success, we are already seeing operational organizations derive value from the initial stages of data development and the creation of these digital engineering organizations. Those that have refocused their efforts on resilient decision-making have been able to improve their throughput by as much as 40%, with minimal capital investment. They also are much better prepared for a future where survival of the fittest is linked not to size or strength, but to resilience and the ability to move quickly, seize opportunities and be ready for the next disruption

PROFILE: Kevin Prouty is Group Vice President for IDC Energy and Manufacturing Insights. He is responsible for managing a group of analysts that provide research-based advisory and consulting services that will enable executives to maximize the business value of their technology investments and minimize technology risk through accurate planning.

Before joining IDC, Prouty worked at many prestigious firms, including Aberdeen Group, Motorola, AMR Research and Gartner, where he established research specialties in a wide variety of areas including utilities, manufacturing, enterprise applications and product innovation research.


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