Smart factories: from OT to future-ready operations
Fujitsu / March 18, 2026
Manufacturing leaders are navigating sustained complexity. They are under pressure to modernize and stabilize operations while maintaining uptime, safety and compliance. At the same time, labor shortages, rising energy costs, supply chain volatility and sustainability targets are piling pressure on the factory floor.
Many production environments still operate with fragmented IT and operational technology (OT) systems and limited end-to-end visibility across assets, processes and sites. Critical knowledge often resides with experienced operators who instinctively recognize subtle performance changes. As workforces evolve and skilled employees retire, this dependency introduces operational risk.
When disruption occurs, teams frequently respond reactively, after failure, rather than proactively preventing it. The central challenge is closing the digitalization gap between machines, data and people to improve resilience, efficiency and long-term competitiveness.
Contents
- Closing the digitalization gap with AI-driven insight
- Where AI delivers the fastest operational impact
- Improving OEE in practical terms
- The minimum data foundation for reliable AI
- Security as a core principle of factory digitalization
- What success looks like after 6–12 months
- Real-world impact and the path to future-ready operation
- Ready to assess your smart factory readiness?
Closing the digitalization gap with AI-driven insight
Factories already generate significant volumes of operational technology (OT) data from machines, sensors, programmable logic controllers and control systems. The issue is rarely a lack of information. More often, data remains siloed by system or vendor, difficult to access across OT and IT environments, and analyzed retrospectively rather than in real-time.
Artificial Intelligence (AI) changes this dynamic by continuously analyzing real time-series operational data against defined use cases and performance requirements. It contextualizes this data with asset and process knowledge, historical data trends,enabling the detection of patterns and anomalies that are difficult for humans to identify at scale and speed.
Importantly, AI augments rather than replaces people, operating within a human-in-the-loop model. It captures and operationalizes tacit knowledge, making it consistently accessible across shifts and locations. Decision-making becomes more structured and less dependent on individual experience, and insight moves from reactive investigation to predictive guidance.
In practice, AI is first deployed as a decision-support capability, with humans retaining accountability for outcomes. As data maturity and confidence increase, organizations can progressively introduce higher levels of automation where appropriate.
Where AI delivers the fastest operational impact
In many manufacturing environments, predictive maintenance and asset reliability provide some of the fastest returns on investment. Early detection of failure indicators reduces unplanned downtime and improves asset availability.
Low-risk, high-value entry points also include intelligent shift handover supported by AI-generated summaries and automated compliance evidence collection in regulated environments. These use cases operate within existing OT landscapes rather than requiring a one-time, large-scale modernization program.
This incremental approach builds confidence while delivering measurable value.
For example, Fujitsu worked with REHAU Industries SE & Co. KG to develop an AI-based visual quality inspection solution that automates defect detection during manufacturing. Instead of replacing existing systems, the solution was integrated into REHAU’s production environment, improving visibility into product quality, increasing defect detection rates, reducing waste and strengthening overall operational performance.
Improving OEE in practical terms
Overall Equipment Effectiveness (OEE) has long been a core performance metric in manufacturing. Traditionally, it has been used to analyze past performance. With AI applied to OT data, OEE becomes a live operational tool.
Availability improves as AI reduces unplanned downtime through early failure detection. Performance improves as AI identifies bottlenecks, process anomalies and performance drift that may not be visible through manual monitoring, connecting previously siloed data and surfacing issues in time to act. Quality improves through real-time inspection and earlier defect detection, reducing the time and cost required for rework.
Digital twins extend these capabilities by modeling assets and processes in virtual environments. Manufacturers can anticipate performance degradation and coordinate semi-autonomous maintenance actions with human oversight. OEE shifts from reporting what happened to informing what should happen next.
The minimum data foundation for reliable AI
AI does not require perfect data to begin delivering value. Most manufacturers already have sufficient operational data to start with. What is required is structure, governance, and alignment of operating models and processes to support future performance.
Reliable OT data capture, including time-series data and equipment context, is essential. As PLC providers use different data models, hierarchies and tagging structures, data must be standardized and contextualized rather than treated as isolated signals. A unified access layer, underpinned by strong data governance and industrial data taxonomy, helps break down silos between OT and IT environments.
Secure, high-quality data pipelines underpin trust and scalability. Edge aligned cloud infrastructure enables real-time data flows and analysis. Clear data ownership and governance roles across people and processes ensure accountability.
AI can also help identify data gaps and, with appropriate governance and human oversight, support the prioritization and remediation of those gaps. In doing so, it reduces manual analysis effort and enables teams to focus on higher-value, strategic decision-making. Transformation is iterative, strengthening as architecture matures and insight deepens.
Security as a core principle of factory digitalization
As factories become more connected, the attack surface across OT environments expands. Manufacturing is now one of the most targeted sectors for cyber attacks, making secure digitalization a foundational requirement rather than an afterthought.
This requires unified visibility, relation and correlation across OT and IT environments, protection of intellectual property and production continuity, and careful management of both cyber and physical risk. Worker safety and high-risk operations must be safeguarded as part of the same integrated approach.
AI can strengthen safety and security by detecting abnormal behavior patterns and identifying potential threats earlier. In this context, OT-IT cyber visibility, relation and correlation become an embedded foundational capability across hardware, software and firmware layers rather than a bolt-on control.
What success looks like after 6–12 months
Within weeks, targeted use cases should begin delivering early value, with structured transformation programs driving measurable, scalable impact over six to twelve months. Structured organizational change management ensures that frontline teams understand the purpose of new digital capabilities, receive appropriate training and are supported as these tools become embedded in daily operations.
Clear governance frameworks, executive sponsorships, a defined roadmap and an operating model provide direction and accountability. Early AI use cases demonstrate tangible value, while foundational OT connectivity and data architecture establish the technical base required for scale. At the same time, process harmonization progresses across planning and manufacturing operations management, strengthening alignment between strategy and execution.
For example, Fujitsu worked with SUBARU Corporation at its Gunma Oizumi Plant to deploy AI-driven quality assurance for engine camshaft grinding, improving the consistency and accuracy of component quality while reducing production line downtime and waste. This initiative illustrates how structured transformation, combining AI model deployment with operational governance and lifecycle management, can deliver measurable operational improvements within months rather than years.
Frontline workforce engagement is critical to success and advances as intuitive interfaces and accessible insight reduce reliance on deep specialist knowledge. As key performance indicators trend positively, confidence builds across leadership and operations teams. Transformation becomes structured, transparent and achievable rather than disruptive.
Drawing on experience across global automotive and industrial manufacturers, Fujitsu combines OT integration expertise, AI capability and governance-led transformation frameworks to help organizations move from pilot initiatives to scalable, measurable performance improvement.
Real-world impact and the path to future-ready operations
When AI is applied effectively to OT and supply chain data, measurable outcomes follow including reductions in unplanned downtime, improvements in OEE and quality performance, optimized inventory and working capital, and faster recovery from operational disruption.
In a recent initiative with Panasonic Connect Co., Ltd., Fujitsu supported AI-driven supply chain optimization that contributed to approximately $10 million in annual inventory savings and reduced emergency recovery times from days to hours.
This example is explored further in Fujitsu’s AI Agents in Production Supply Chain video, which highlights how AI-enabled orchestration can deliver measurable resilience and efficiency gains.
AI Agents in Production Supply Chain video: https://www.youtube.com/watch?v=BdjkVb4MvqA
These results were not achieved through large-scale system replacement. They stemmed from incremental, continuous transformation. AI acts as an engine for visibility, prioritization and decision support, while human-in-the-loop design builds trust and enables safe progression toward greater autonomy.
The smart factory is not a fixed destination, but an evolving capability.
By closing the digitalization gap between machines, data and people, manufacturers can transition from reactive operations to intelligent, resilient and adaptive production environments, built to thrive in an increasingly complex global landscape.
In doing so, they can maximize existing asset performance and capital efficiency while responding faster to market volatility and supply chain disruption.
Ready to assess your smart factory readiness?
Fujitsu’s Digital and Smart Factory Assessment tools provide a structured starting point to evaluate OT-IT maturity, identify high-impact use cases and prioritize next steps toward scalable transformation.
EU Digital Factory Self-Assessment Tool:
https://mkt-europe.global.fujitsu.com/digital-factory-self-assessment-tool
US Smart Factory Self-Assessment Tool:
https://mkt-americas.global.fujitsu.com/smart-factory-self-assessment-tool
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