74% of manufacturers say they have a global production system. Only 29% say it’s fully used. Here’s how continuous, connected insights close that gap.
For companies that have already invested heavily in global production systems, the critical question is why operational excellence hasn’t always followed. Even organizations that have defined common standards, rolled out digital tools, and launched multiple improvement initiatives across their networks too often have little to show for it.
The reason is usually that the “system” is systemic in name only. A survey of more than 100 manufacturing COOs found that while 74 percent say their company has a global production system, just 29 percent report it’s fully implemented across all sites. Most organizations apply these systems only to certain plants or functions, leaving them partially adopted across the enterprise.
The digital story looks similar. While almost three-quarters of COOs report using standardized digital tools, many remain confined to specific locations rather than scaled organization-wide — and none of the surveyed companies say they’ve fully integrated advanced analytics, AI, or gen AI into decision-making. Companies end up trying to drive continuous improvement without the continuous, connected insight that technologies like AI can actually provide.
The Two Traps Organizations Fall Into
Most organizations land in one of two failure modes. Some bolt together disconnected initiatives — production elements, analytics pilots, capability programs — that never fully cohere. Others swing to the opposite extreme, imposing heavy centralization to force consistency, only to find that rigid top-down standards prove unworkable at the local level.
In both cases, the core problem is the same: feedback loops never form between performance insight and frontline action, so improvement stays episodic — gains appear in pockets but fail to scale or sustain across the network.
What actually makes a global production system a system isn’t the uniformity of its tools or standards. It’s whether it continuously links insight to action across the network — performance data creates transparency about what matters most, those conclusions translate into clear priorities and repeatable routines at the front line, and results feed back quickly so learning compounds across sites.
Done well, that cycle becomes self-reinforcing. For one global industrial manufacturer, this kind of change increased production capacity by 40 to 50 percent; for a European life sciences company, a redesigned production system cut costs network-wide by more than $60 million in a single year.
A Three-Step Plan That Actually Works
The first step is establishing a performance baseline. Without a shared fact base, local teams launch initiatives that can’t be compared or scaled, while central leaders impose standards without real evidence behind them. Leading manufacturers start with data-driven maturity assessments that benchmark where each site truly stands — operationally, culturally, and digitally — against a common definition of “good.”
One multinational consumer goods company ran a structured diagnostic across 15 sites and found wide variation in fundamentals, from inconsistent maintenance routines to unclear performance accountability — visibility that became the foundation for sequencing interventions and building a fact base to track progress. A global pharmaceutical company’s similar assessment revealed that performance differences were rooted not just in technology, but in daily management practices.
The second step is building capabilities flexibly. Classroom training and one-time playbook rollouts tend to fail because they don’t change how work is actually managed day to day — capability ends up depending on individual champions, and gains erode once attention moves elsewhere. The more effective approach embeds capability development in the flow of work, where operators and supervisors learn by applying new tools to real problems.
The consumer goods company’s playbook defined what “good performance” looked like across more than 20 operational dimensions, backed by a central transformation hub coaching local teams — pilot sites saw productivity rise by about 25 percent, which helped motivate other sites to adopt the same approach.
Two global consumer-packaged-goods companies took a similar tack when scaling operational excellence across 15 to 20 sites, onboarding more than 3,000 users onto a unified digital platform within months. An Asian pharmaceutical company went further still, launching a “digital accelerator” that coached more than 50 full-time employees across five sites on two continents, dramatically raising equipment effectiveness and operational-excellence maturity scores within a year.
The third step is applying digital and AI judiciously. Generative and agentic AI capabilities are accelerating quickly across manufacturing — 88 percent of organizations now report regular use of AI in at least one business function, up from around 78 percent a year earlier. Notably, 23 percent of respondents say they’re now scaling agentic AI somewhere in their enterprise, with another 39 percent experimenting with AI agents.
Enterprise-scale AI is still rare — only 2 percent of surveyed COOs say AI is fully embedded across operations — but the emerging use cases are telling. Leaders in sectors like automotive and defense are already using agentic AI for visual-anomaly detection and autonomous routing and scheduling, with smart workflow agents cutting cycle times from days to hours.
One multinational chemical company trained dozens of change agents through a multisite transformation program, linking diagnostics directly to implementation plans and moving three major sites through the full diagnostic-to-implementation cycle in about ten months — a journey historically measured in years.
The consumer goods company’s central platform, combining digital dashboards, best-practice libraries, and generative-AI-enabled assistants, delivered a 15 to 20 percent reduction in costs and more than 85 percent employee satisfaction with the new way of working.
Governance Is What Keeps It From Backsliding
Once a transformation takes hold, the real risk becomes regression. Companies that build mechanisms for peer exchange — regular maturity reviews, digital communities of practice, cross-site coaching — are better positioned to maintain progress as technologies evolve, leaders rotate, and priorities shift. Over time, that consistent reinforcement turns a production system from a set of tools into a living management framework that improves itself through data, collaboration, and human judgment.
For operations leaders, this changes the nature of the role: rather than optimizing individual sites in isolation, they end up orchestrating a network that can adapt and scale new practices rapidly, based on a clear sense of what needs to be common and what should stay local.
One consumer goods company built its sustaining infrastructure around quarterly maturity reviews, digital coaching sessions, and peer learning forums — its 30-plus global sites now continue to evolve together, guided by a shared language of performance.
As production networks grow more complex and AI capabilities mature, the cost of operating without a true system keeps rising, and the window to build one keeps narrowing. The manufacturers achieving long-term operational excellence are the ones creating a genuine virtuous cycle: data informs priorities, people translate insight into action, and the system itself becomes the source of continuous improvement.