Unlocking Factory Capacity with AI-Driven Efficiency

Tariffs, labour shortages and energy costs are creating global supply chain and cost pressures that are impossible to ignore. Instead of shrinking footprints to save money, manufacturers could unlock hidden capacity and boost employee productivity with new technologies like AI.  

Some manufacturers scaling smart technologies are reporting 10% to 20% improvements in production output and up to a 20% gain in employee productivity, according to Deloitte’s 2025 Smart Manufacturing and Operations Survey. Companies implementing these technologies are realising up to 15% in unlocked factory capacity. A significant majority of surveyed executives now see smart manufacturing as their primary engine for global competitiveness over the next three years. 

Operational efficiency on a global scale may no longer be about doing more with less, but doing more with data. Using digital architecture can help to fundamentally raise the ceiling on production output. 

Failing to scale

According to Rockwell Automation’s 10th Annual State of Smart Manufacturing Report, an overwhelming 95% of global manufacturers are currently investing in, or plan to invest in, AI and machine learning over the next five years. 

However, many are seeing difficulties scaling beyond pilot programmes. According to Boston Consulting Group (BCG), despite heavy investments in AI, most manufacturers are not able to report meaningful returns. While most organisations make substantial investments in advanced planning systems, relatively few have successfully converted these investments into sustained performance improvements. Just 20% of organisations report meaningful value gained from AI and as few as 7% report value from generative or agentic AI use.

BCG’s research found that organisations reporting higher maturity levels achieve 25% greater forecast accuracy than businesses with low-level maturity. Maturity levels also vary by region and sector, with companies with global operations demonstrating the most substantial advances, followed by organisations based in Europe, the Middle East and Africa.

Value from AI

BCG's research suggests that companies need to redesign processes, establish cross-functional working methods and embed new behaviours at scale to deliver excellence. Without the capacity to undertake structural redesigns, organisations may be unable to benefit from new tools such as AI.

"AI can be a powerful catalyst in manufacturing supply chains, but its impact depends on how it is integrated," says Andres Garro, Managing Director and Partner at BCG. "The companies achieving the strongest results are embedding AI into disciplined planning processes and reliable data foundations, using it to accelerate decisions and improve performance at scale."

Rather than using AI to address fundamental issues, leaders are layering it onto existing planning systems that demonstrate inefficiency. This approach results in expenditure on tools that may not provide meaningful assistance. 

To overcome these issues, BCG suggests that leaders choose AI approaches that follow designed use cases and develop a single version of truth by ensuring high data quality and cross-functional planning. Moving toward exception-based workflows that reduce firefighting and accelerate decision cycles can also help to bring value.

Unilever’s digital twins

Unilever has partnered with Microsoft to deploy digital twins across its global manufacturing network, using IoT and intelligent edge services within the Azure platform. Rather than just monitoring operations, these AI models actively ingest massive volumes of real-time data on temperature, motor speeds and cycle times to predict and optimise the manufacturing process.

At its Valinhos facility in Brazil, handing process control over to the digital twin yielded immediate ROI. The implementation saved the company US$2.8m in energy costs and drove a 1% to 3% increase in overall productivity at the site level.

The true value of this architecture lies in its scalability. By standardising this data foundation, Unilever is moving beyond isolated gains. Replicating a 3% productivity bump and equivalent energy savings across its global factories translates into massive, systemic capacity unlocking without the need to expand physical footprints or increase headcount.

P&G’s supply chain AI

Procter & Gamble (P&G) is using AI and advanced analytics to synchronise its supply chain. At its Rakona plant in the Czech Republic, a World Economic Forum Lighthouse facility, the company deployed a web-based analytical simulation model. 

By unifying its operations and integrity data as part of a holistic Industry 4.0 rollout, the Rakona facility achieved a massive structural unlock. This reliable data foundation allowed P&G to reduce its total inventory by 43%, reduce plant changeover times and ultimately boost overall productivity by 160% over a three-year period.

P&G captured these outsized returns because it executed a fundamental structural redesign alongside its technology investments. By establishing cross-functional planning and a rigorous, unified data architecture first, P&G ensured that its AI tools translated directly into sustained, enterprise-wide performance improvements rather than isolated operational tweaks.

Hitachi’s Lumada AI

Hitachi has turned its own manufacturing footprint into a proving ground for IT/OT convergence. Using its proprietary Lumada AI and IoT platform, the company integrates real-time operational data directly into its high-level enterprise planning systems. Rather than relying on static production schedules, this architecture enables dynamic, autonomous routing that adjusts instantly to supply chain variables and machine availability.

At its Omika Works facility in Japan, a site that produces complex, customised control systems, this AI-driven approach yielded massive efficiency gains. By allowing the algorithm to dynamically optimise the production sequence, Hitachi successfully reduced the lead times for its core products by a full 50%.

By using a unified data architecture to move complex products through the facility twice as fast, Hitachi effectively expanded its output potential without pouring a single yard of concrete or expanding its physical footprint.

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