Only at the most advanced organizations do managers have an integrated, real-time data picture so that when an interruption occurs, they can be reasonably confident in prioritizing their actions among production lines to minimize financial damage. And even fewer organizations can translate what frontline workers know about the problems in their lines into the data that senior leaders need to make strategic decisions that affect prioritization choices.

That’s the promise of enterprise-level digital performance management, which extends 4IR technologies to provide the entire operations organization, from senior leaders to thousands of frontline workers, with actionable insights that enable faster, more accurate decisions about financial and operational performance. The objective: a single system that supports not only the performance-management reporting cycles that the top team needs in reevaluating strategy, but also the agile problem-solving systems that frontline workers use to identify plant and network-wide constraints, perform root-cause analysis, and ensure corrective actions are taken on the most important opportunities.

To develop a perspective on the implications of this breakthrough for manufacturers across industries, McKinsey’s Mike Coxon, a partner in the Cleveland office, and Christian Johnson, a senior editor in Hong Kong, spoke with three executives at the manufacturing software provider PTC: Howard Heppelmann, divisional vice president and general manager for smart connected operations; Craig Melrose, executive vice president for digital transformation solutions; and James Zhang, vice president for market development. Their discussion has been edited for brevity and clarity.

McKinsey: Companies have been trying to link their operations with technology for years—decades, arguably—so that the front line and the front office could act on the same data and make better decisions. What is different now?

Howard Heppelmann: A big part of the difference is simply in what the technology can do to bridge gaps that previously looked insurmountable. Historically, information technology (IT) (which underlies business systems and finance departments) and operational technology (OT) (which powers manufacturing) were mostly separate. Now there are IT–OT convergence technologies that unify business systems and operational systems.

Also, the longstanding belief was that to get something new, you had to discard and replace something you already had. Given existing investments in factory infrastructure, there isn’t an appetite (or, in many cases, even a possibility) to rip out what’s there and replace it with a single system.

With modern Industry 4.0 technologies, there isn’t a need to. Instead, you build on what you already have—your “brownfield” production networks—combining disparate IT and OT data sources, then homogenizing and normalizing the data to generate digitally charged operational insights and transform processes.

James Zhang: Rip-and-rebuild wasn’t scalable. Picture a global health-products manufacturer with roughly 100 production sites. At just one factory, installing a state-of-the-art traditional industrial software stack, including manufacturing-execution system (MES) and control and supervision software, was going to cost about $10 million and take 18 months. And that’s only part of the investment they would need. If you’re the head of manufacturing, you’re going to want a different path forward.

Craig Melrose: A leadership team will be tempted to say, “Let’s replace a system that does X with one that does half X and half Y,” and think that compromise will effectively balance cost against value generation. But what you usually end up with is a system that does neither X nor Y well.

McKinsey: That sounds like a gap in understanding the root cause of the problem.

Heppelmann: Yes, but we see this rapidly changing as companies rethink their IT architectures, and agile improvement methodologies become more widespread. Many of the cultural barriers in manufacturing are beginning to soften.

However, that’s a new way of thinking enabled by Industry 4.0. It runs into the reality that most production networks still operate over a patchwork of siloed data systems, and rely on manual processes to unify the enterprise.

Zhang: Companies struggle in accommodating a wide range of operating machines that are quite different from one another, and that need to be linked together with IT systems. The beauty of Industry 4.0, and its related digital technologies, is that they’re designed to address this exact challenge.

McKinsey: What does this look like in practice?

Heppelmann: Let’s give an example. At a fast-moving-consumer-goods manufacturer, the sites implementing this system are now able to integrate financial data and performance data into unified, standardized applications showing exactly how much money each plant is making, down to the level of a single production line—in real time. That’s despite the fact that the plants’ IT and OT back ends are quite different; metrics are now uniform, and the data are therefore comparable.

Because the data are standardized and normalized, internal benchmarking becomes much more powerful. Managers can see that plant X performs better than plant Y and can start to examine why.

Melrose: The inflection point happens when the culture embraces these changes. Senior executives can compare operations and make strategic decisions. Middle managers can make better choices to achieve higher productivity. Frontline operators can solve problems in real time and course-correct during the same shift.

McKinsey: What characteristics do the organizations that are achieving these types of results share?

Heppelmann: The companies that have managed to break through are the ones that have figured out the connections between use cases and P&L impact, so that they’re applying technology to the most critical constraints of their production network. It’s a use-cases-first approach.

McKinsey: That’s a consistent theme: use cases first, technology second.

Zhang: An industrial-equipment manufacturer illustrates this point well. Its top problem was unplanned machine downtime, which translated into significant cost and quality issues. The first step was to identify use cases. Rather than start with dozens, as is typical, it started with identifying and prioritizing use cases to address the most common (and highest-impact) business problems across its production network. It rolled out just four common use cases at the first plant it targeted, which proved so effective that they are being rolled out across the entire factory network to help manage inventory, asset performance, energy consumption, and quality.

Actionable insights into machine performance, people’s behavior, and process efficiency now empower managers to continuously optimize production. Improvements at one site can be replicated easily network-wide. Work-in-process has fallen by more than 15 percent, unplanned downtime by one-quarter, and annual energy savings are expected to be more than $10 million at enterprise scale.

Melrose: And the savings the company has earned are now going into product redesign. At the same time, the company is building on its wins, expanding its library of use cases across the network. It’s creating a continuous-improvement feedback loop.

McKinsey: Now that at least some companies are starting to achieve scale, what do you see as the next big opportunity in data and operations?

Heppelmann: To me, the single biggest gap is translating the operational outcomes that the digital-transformation teams are targeting into financial outcomes that the C-suite can understand. When that link is missing, there’s an understandable reluctance to move forward: if finance has only a bleacher view of what’s happening in operations, it holds back.

For transformation leaders, a crucial early goal is to deliver a first proof of value that’s significant enough for the C-suite to say, “This is the one thing to focus on. Drop everything else.”

Melrose: This is important from another perspective as well. Today, companies spend more time on trying to find problems than on trying to fix them. By creating a new dynamic of fixing rather than just finding problems, the IT-OT link can create massive value.

A big part of the problem is human. Even now, within most big organizations, industry verticals aren’t sharing data with one another, or with functions. These technologies can create a “digital thread” so that information can be shared and stitched together with total transparency by almost anyone in the company to fit the problems they need to solve. That helps organizations move focus and resources from finding to fixing.