5 takeaways from the world’s largest dataset on industrial transformation

Industrial transformation has been occurring piecemeal over the last few years, with emerging technologies, new business models and data-driven processes deployed to improve the efficiency and capability of operations and supply chains across sectors.

However, industrial transformation is no longer a series of one-off experiments. Lumina, the World Economic Forum’s new AI-powered platform for lighthouse transformation, developed by the Centre for Advanced Manufacturing and Supply Chains, unites eight years of data from the Global Lighthouse Network – a community of the world's most advanced operational sites.

Drawing from more than 1,000 real-world cases across 32 countries, the evidence is clear: companies are moving beyond pilots, deploying multiple technologies together and delivering measurable impact. Factories are now tech companies.

What these cases reveal is not just the scale of change but the patterns behind it. Why do some organizations break through while others remain stuck? The answer lies less in single technologies and more in how processes, people and systems evolve together.

Here are five recurring insights from the data that show what really scales and how leaders can turn transformation into resilience and growth.

Across Lighthouses, transformation rarely begins with visionary strategies but with operational pain points such as downtime, scrap or missed delivery windows. Nearly half of all successful transformations originated from cost, quality or safety issues, generating 1.3 times the measurable impact than top-down programmes.

For example, a metals producer once debilitated by daily furnace failures used AI-driven predictive maintenance to turn instability into insight. Within six months, downtime fell by 42%, conversion costs by 20% and the same approach scaled across logistics and energy systems. Across 254 paired cases, similar patterns emerge: supply-chain resilience and productivity rise together, with a correlation of 0.32.

Predictability compounds efficiency. When volatility is tamed, every metric – from throughput to cost to sustainability – accelerates. Stable supply, synchronized scheduling and right-sized buffers create a self-reinforcing flywheel of performance.

One electronics assembler exemplified this by connecting supplier inventory data to an AI-powered production scheduler, cutting inbound volatility 28%, lifting line utilization from 68% to 84% and improving on-time delivery by 22%.

The most advanced organizations aren’t eliminating risk; they’re institutionalizing resilience, building agile supply and production networks that adapt as conditions evolve. Every supply shock, therefore, becomes a learning event and every recovery, a system upgrade.

Technology doesn’t scale in a vacuum; it scales inside process symmetry. Lighthouses that mature in at least three core processes – quality, maintenance and workforce development – are twice as likely to scale pilots successfully.

Mature sites that have standardized quality, maintenance and workforce capability show median productivity gains of 18%, versus 9% for less mature peers. Sustainability improvements follow the same pattern (11% versus 5%), confirming that maturity amplifies stability and impact.

The global average lighthouse productivity gain sits around 40%, lifted by a frontier group that has scaled multi-technology architectures, combining AI, automation and workforce transformation. Sites that combine maturity breadth with multi-technology adoption achieve up to 1.3 times higher productivity, demonstrating that it takes multiple connected steps to drive transformation.

Why? Because structure, not urgency, accelerates transformation. Once a process reaches repeatable discipline, the next innovation deploys faster, cheaper and with less resistance. The data confirms that organizations with three or more mature process reduce the time from pilot to scale by 50%.

For example, one consumer-electronics plant standardizing its quality and maintenance systems is layering digital workforce augmentation on top. Adoption took weeks, not months: defects fell 47% and resilience metrics improved in parallel.

The data highlights one particularly powerful multiplier: changeover time. Plants that reduced it achieved lead-time and field-failure improvements with correlations above 0.7. Standardizing this single routine often unlocked cross-process gains faster than any digital tool could.

For years, sustainability was treated as a cost; now, the evidence shows it’s a competitive advantage. Across all Lighthouse transformations, initiatives targeting energy, water and waste reduction delivered 25-40% greater cost savings than other programmes and improved on-time delivery by 15-30%. The most sustainable factories are, in fact, the most reliable.

Across 217 paired cases, productivity and sustainability rose 21% in tandem, with a positive correlation of 0.146. The reason is operational: better resource control enables better throughput control. When material, water and energy flows are optimized, variability declines, planning precision improves and every cut in waste or rework feeds directly into higher productivity.

Regional patterns reinforce this convergence.

Asia leads in energy efficiency through competitive energy markets and digital retrofits. Europe excels in circularity and material substitution, with over 60% of advanced sites running closed-loop or secondary-material systems. North America leads in traceability, linking supplier emissions and logistics data for end-to-end optimization.

Real-world examples show how sustainability accelerates competitiveness. One precision-engineering site introduced closed-loop material tracking, cutting scrap by 35%, energy use by 19% and raising productivity by 18%.

Elsewhere, a consumer-goods manufacturer linked water-usage sensors to machine-learning models that optimized cleaning cycles, reducing water use by 27%, cleaning frequency by 40% and raising throughput without capital expenditure.

In practice, sustainability has become a design principle for competitiveness. Plants that hardwire energy, materials and emissions data into daily operations are building adaptive, self-optimizing systems that thrive under stress.

Another key insight from the data is that transformations scale because people do. While technology defines what’s possible, human capability determines how far and fast progress unfolds.

Across the dataset, 75% of sites addressing talent priorities – from safety and skills to worker experience – achieved above-median performance. Those who invested in structured skill programmes scaled initiatives 2.5 times faster than others.

The correlation between talent and productivity (0.43) is the strongest in the dataset, confirming that capability is the true multiplier of impact.

In many leading sites, humans and intelligent systems learn together. Augmented-reality guidance, AI-supported diagnostics and interactive digital twins are compressing learning curves by 30% to 50%, while new operational data continuously improves algorithms. The feedback loop between human judgment and machine insight creates a living network of knowledge.

At one automotive supplier, operators co-designed their dashboards to visualize daily metrics, uncovering inefficiencies invisible to management. Within three months, absenteeism fell 30%, safety observations doubled and throughput increased by 12%.

Across hundreds of documented transformations, one pattern repeats: change propagates through capability density. When workers understand how a process works, why it evolves and how their actions influence outcomes, adoption strengthens and replication accelerates.

Finally, as is core to the Global Lighthouse Network, the data shows that the industry is entering a multi-technology era. Single-tool deployments are becoming rare; 94% of all transformations use at least two technology domains.

This convergence defines the new industrial stack. Value no longer comes from any single technology but from how data, algorithms and workflows interlock across functions.

A pharmaceutical manufacturer integrated digital twins for process validation with generative AI-based documentation for compliance – cutting product-launch time by 38% and saving thousands of engineering hours. Meanwhile, a packaging company combined IoT with computer vision and AI to track defects and energy patterns in real time, achieving 27% energy savings and 33% waste reduction.

The question isn’t, “Which tool should I adopt?” It is, “How do my technologies learn from one another?” In the next frontier, we observe human-machine collaboration becoming the true competitive edge, where systems don’t just execute but co-create intelligence with people.

Industrial transformation has become a contest for orchestrated learning.

The organizations winning this race treat data not as hindsight but as shared foresight – a collective intelligence that compounds over time.

The Centre for Advanced Manufacturing and Supply Chains' Lumina platform crystallises eight years of shared learning into a single living network, reflecting the collective leadership of the Lighthouse community and its Advisory Board. It shows that when information is structured, shared and refined within a community of practice, transformation scales faster than any single company could achieve on its own.

To learn more and engage with the Centre for Advanced Manufacturing and Supply Chains, click here.

Note: The Lumina dataset aggregates 1,085 unique transformation solutions across 32 countries and 35 industries from the Forum’s Global Lighthouse Network and partner submissions (2017–2025). Each solution was coded by: technology taxonomy (AI, IoT, robotics, sustainability tech, etc.); operating system attributes (quality, maintenance, workforce, supply-chain); key performance indicator deltas (baseline/final or declared impact %); narrative linkage (“day in the life” stories); correlations were computed using median per-solution deltas, Spearman and Pearson coefficients and pairwise deletion for missing data.