How can manufacturers turn data analytics into actual revenue?

Can generative AI really solve the manufacturing labor shortage and supply chain crisis?

Overcome lingering supply chain and labor challenges with the latest insights from West Monroe’s 2024 outlook. Discover why integrating generative AI and machine learning is the only long-term strategy for manufacturing profitability. Stop sitting on valuable data—read the full analysis to learn how to monetize your analytics and future-proof your manufacturing operations against labor shortages.

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Manufacturers cite supply chain issues and a strained labor market among their continuing areas of concern as overall production returns to pre-pandemic levels. This report from an expert team at West Monroe, a digital services company, advises manufacturers to look toward the integration of generative artificial intelligence (AI) and machine learning (ML) to help ensure future profitability. Although creating a solid foundation for existing data collection initiatives takes effort and time, the outcome will prove crucial as new capabilities become available. This report finds that the long road manufacturers are undertaking to make the most of these tech advances is the path to the future.

Take-Aways

• Ongoing labor and supply-chain issues concern manufacturers, but continuing their AI initiatives can help build future profitability.
• Connect your “data and analytics strategy” to potential revenue opportunities.
• Digital transformation is a long-term strategy.

Summary

Ongoing labor and supply-chain issues concern manufacturers, but continuing their AI initiatives will help future profitability.

Among manufacturers surveyed, 65% report continuing concerns about the industry due to the difficulty of retaining skilled workers and the need to solve continuing supply chain issues. These concerns explain the lack of confidence seen at the start of the pandemic.

Fortunately, generative artificial intelligence (AI) and machine learning (ML) initiatives that began in 2023 continued into 2024. Digital technology has significantly changed manufacturing, and integrating AI and ML capabilities will enhance it.

Most manufacturers possess sufficient data, but they must learn to cultivate value from it. Generative AI is compelling companies to reevaluate their current AI strategies. They must define what information to collect, determine its future uses, and change their current programs if necessary.

“Like any new skill added to your organization’s repertoire, it’s important to walk before you run.”

Most companies report that they’re still creating a base for their data collecting and analytics programs. Organizations have time to accomplish these foundational steps because the hype of AI implementation far exceeds the reality of its immediate use. In some areas, more traditional approaches still prove more effective than Gen AI.

Moving toward AI and ML implementation requires top-level, organization-wide executive coordination to prevent people from pursuing isolated initiatives within different sections or silos of an organization.

Connect your “data and analytics strategy” to potential revenue opportunities.

The vast majority of manufacturers surveyed believe that technology will further transform the industry in the next 10 years, but they have challenges to surmount along the way. Defining how to monetize that transformation means, among other things, that manufacturers will need to generate fresh value streams.

Companies may collect large amounts of data, but to achieve a good ROI they must develop clear metrics to connect it to their revenue and EBITDA. Focusing on lower-level, “near-term deliverables” is paramount, even as companies define their end goals.

“Your data strategy must be a cross-functional effort used to unlock revenue and profitability.”

Data and analytics programs must provide relevant information division leaders can use to take action. This requires defining the correct metrics to fit each manufacturer’s specific endeavors. Without this first step, faulty analysis – or analysis derived from measuring the wrong parameters – could do more harm than good.

Digital transformation is a long-term strategy.

Although macro-economic factors in the supply chain still affect manufacturing, the industry is returning to pre-pandemic production levels. Leaders who have been focusing on short-term cost reductions during this recovery period need to recognize that digital transformation is a long-term strategy. Companies that see it as a “flavor-of-the-day” will lose out on its potential benefits.

Dealing with the changes that AI and ML will provoke requires leaders to show the members of their workforce, especially those unfamiliar with “generated data,” how it will ultimately benefit them and the company. Although leaders also will need to integrate AI and ML into their overall operations, the timeline isn’t immediate.

Companies that continue to rely on manual supply-chain processes risk being unable to keep up with the needs of an “always-on world.” As they begin to modernize processes, leaders must make sure that their implementation plans identify the right areas in which to incorporate new technologies so they can realize improved ROI and create future value.

About the Authors

Randal Kenworthy, Jeff Pehler, Kris Slozak, Kirsten Lentz, Barkat Syed, Brian Pacula, and Jeremy Tancredi of West Monroe, a digital services company, prepared this report.