We’re on the verge of a manufacturing data revolution

Published on the 28/01/2021 | Written by Jonathan Cotton


Manufacturing data

But many manufacturers are still struggling to capture smart-factory value…

Manufacturing companies will soon be collaborating in ‘hyperconnected value networks’ that increase productivity, help them develop new customer experiences, and even ensure that they have a ‘positive impact on society and the environment’.

That’s the takeaway from a new report by the World Economic Forum, produced in collaboration with Boston Consulting Group, Data Excellence: Transforming manufacturing and supply systems.

The paper looks at the new value that is currently being unlocked by data and analytics applications in manufacturing, and what the factory of the near-future will look like.

“Many companies have become disillusioned because they lack the technological backbone required to effectively scale data-and-analytics applications.”

Simply put, to compete effectively manufacturers will need to employ a large variety of data‑and‑analytics applications, such as predictive maintenance, advanced robotics, and tracking and tracing in supply networks.

Data will be the lifeblood of these applications, thus, the successful transformation of manufacturing and supply systems today should be the focus for decisionmakers.

That’s understood by the industry: Nearly three‑quarters (72 percent) of manufacturing executives say they consider advanced analytics to be more important now than they were three years ago.

“The Covid‑19‑induced economic crisis has put an even stronger emphasis on the importance of data and analytics in manufacturing,” says Francisco Betti, report co-author and head of advanced manufacturing and production at the World Economic Forum.

“Emerging from the crisis, companies will need more resilient supply systems to prepare for future shocks as well as higher productivity in their operations to free up liquidity for future investments.”

Hyper-connection
As the shift towards hyperconnected networks of assets, factories and supply systems continues, data and analytics will play an ever more crucial role in unlocking value across productivity, customer experience and societal and environmental impact.

Applications driving value in these areas can sometimes be implemented using internal company data only. Many applications, however, require the exchange of data across corporate boundaries, which involves connecting multiple stakeholders in data ecosystems.

“As an example, consider equipment maintenance,” offers the report’s authors. “Connecting an asset to a data platform allows for the real‑time monitoring of an asset’s condition. A company can fully implement this application using only internal data.

“A more advanced application is the use of machine learning to predict and prevent failures. Such models need to be trained by a large amount of data. Companies can rarely provide this data alone, so they must share data with other asset operators.”

Challenges
Of course the extent to which a company needs to collaborate with other companies to share data depends on its size and the type of application, but the WEF estimates that data‑and‑analytics‑driven applications could potentially reduce conversion costs by up to 20 percent.

But while acknowledging the importance of data and analytics is easy, walking the talk is another matter, with many companies already disillusioned with their efforts to capture value from data-driven implementations.

So what stands in the way of full supply chain hyper-connectivity?

Manufacturers cite various challenges that have impeded their efforts to further scale and implement data and analytics solutions within their plants and across networks. Those challenges include struggling to prioritise the right value-adding use cases from the broad range of applications, data security and a lack of ‘critical organisational enablers’, such as skills and capabilities and effective internal governance.

The paper defines six organisational and technological priorities of data excellence in manufacturing to help companies capture value from new applications internally and within their larger ecosystems.

Those six priorities to capture value from data and analytics in manufacturing are:

  • Define a data-to-value strategy and roadmap
  • Incentivise internal and external ecosystem partners
  • Build capabilities to capture and use data
  • Implement an open platform to unlock data silos
  • Enable connectivity for low-latency, high-bandwidth data flows
  • Ensure data security and privacy

“Manufacturing is on the verge of a data‑driven revolution,” says Daniel Küpper, BCG managing director and partner and a report coauthor.

“But many companies have become disillusioned because they lack the technological backbone required to effectively scale data-and-analytics applications.”

“Establishing these prerequisites will be critical to success in the post-pandemic world.”

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