Smart manufacturing moves beyond Industry 4.0, forcing a rethink of industrial strategy

Manufacturing technologies have been the first domain to experience this transformation. The review documents how artificial intelligence and machine learning have overtaken conventional statistical approaches in process optimization. Data-driven models now capture complex, nonlinear relationships between process parameters and output quality, enabling manufacturers to balance competing objectives such as surface quality, productivity, energy consumption, and defect reduction.

Manufacturing is moving deeper into the era of intelligent systems. What was once defined by automation and efficiency is now evolving into a complex ecosystem where production, energy use, human oversight, and environmental performance are tightly interconnected. 

A new study "A Review of Recent Advanced Applications in Smart Manufacturing Systems", published in the Journal of Manufacturing and Materials Processing, maps how smart manufacturing systems are redefining production technologies, system management, sustainability, and human-centric design in the transition from Industry 4.0 to Industry 5.0.

From automated factories to intelligent production ecosystems

The study shows that smart manufacturing systems represent a decisive break from traditional factory models. Instead of isolated machines executing predefined tasks, modern manufacturing environments function as adaptive, data-driven ecosystems. At the center of this shift is the integration of cyber–physical systems, artificial intelligence, and real-time data flows that allow production systems to sense, analyze, and respond to changing conditions.

Manufacturing technologies have been the first domain to experience this transformation. The review documents how artificial intelligence and machine learning have overtaken conventional statistical approaches in process optimization. Data-driven models now capture complex, nonlinear relationships between process parameters and output quality, enabling manufacturers to balance competing objectives such as surface quality, productivity, energy consumption, and defect reduction.

These methods are widely applied across machining, welding, and additive manufacturing. Hybrid approaches that combine machine learning with evolutionary algorithms allow simultaneous optimization of multiple parameters, a task that was previously impractical with rule-based or linear models. As a result, production systems can adapt dynamically rather than rely on static configurations.

Equally significant is the evolution of process monitoring. Smart manufacturing systems increasingly rely on continuous sensor data streams, analyzed through machine learning and deep learning models, to detect anomalies and predict failures in real time. This enables predictive maintenance strategies that reduce downtime and extend equipment life. The study highlights a growing reliance on multi-sensor fusion and hybrid monitoring frameworks that balance predictive accuracy with interpretability, a critical requirement for industrial deployment.

The review makes clear that these advances are not confined to individual machines. They scale upward to production lines and entire factories, creating environments where operational decisions are increasingly data-driven and automated. However, the authors stress that technological sophistication alone is insufficient. Without integrated system design and robust management frameworks, these tools risk remaining isolated solutions rather than drivers of systemic change.

Digital twins and AI are redefining systems management

The study identifies the rise of digital twins as a foundation for smart systems management. Digital twins are no longer static simulations but continuously synchronized digital representations of physical assets. By linking real-time sensor data with predictive models, they enable forecasting, optimization, and proactive intervention across manufacturing systems.

When combined with artificial intelligence, digital twins support predictive maintenance, adaptive scheduling, and scenario-based planning. Reinforcement learning and advanced control strategies allow systems to move beyond detection toward autonomous adjustment of process parameters. This marks a shift from reactive quality control to predictive and adaptive decision-making.

The review situates these developments within the broader evolution of smart systems management. Modern manufacturing increasingly depends on reference architectures, interoperability standards, and IT–OT integration to coordinate data and decision-making across heterogeneous environments. While these frameworks provide structural foundations, the study notes persistent challenges in deployment, particularly when integrating legacy systems and ensuring cybersecurity.

Edge computing and federated learning are identified as key enablers of scalability and privacy preservation. By moving intelligence closer to machines, edge AI reduces latency and supports real-time control. Federated learning allows collaborative model training without sharing raw data, an approach that is especially relevant for privacy-sensitive industrial contexts and small and medium-sized enterprises.

The study also highlights the growing role of operational frameworks such as MLOps and AIOps. These approaches treat AI models as managed assets, enabling continuous deployment, monitoring, and updating in dynamic production environments. This reflects a maturation of industrial AI from experimental prototypes to operational infrastructure.

Despite these advances, the authors caution that many smart systems remain at proof-of-concept stages. Challenges related to data availability, model validation, interoperability, and energy cost continue to limit widespread adoption. The review argues that future progress will depend less on algorithmic novelty and more on architectural integration and semantic alignment across systems.

Sustainability and human-centric design move to the core

The review documents how intelligent manufacturing systems increasingly embed environmental and energy considerations into operational decision-making. Life-cycle assessment, energy management standards, and ESG metrics are becoming integral to how production systems are designed and optimized.

Artificial intelligence plays a key role in this shift. AI-enabled optimization supports reduced energy consumption, material efficiency, and waste minimization across manufacturing processes. Additive manufacturing, in particular, demonstrates strong potential for sustainability gains through reduced material use and support for circular economy practices such as recycling and reuse.

Sustainability is not limited to environmental performance. Social and human dimensions are equally important. Smart manufacturing systems must ensure safe working conditions, transparency, and long-term resilience. This perspective aligns with the Industry 5.0 paradigm, which prioritizes human-centric and sustainable industrial development alongside technological advancement.

Human–machine interaction is therefore a critical focus of the review. As manufacturing systems become more autonomous, maintaining human oversight and interpretability becomes essential. The authors highlight the growing importance of explainable AI and human-in-the-loop approaches that allow operators to understand, audit, and intervene in automated decisions.

This emphasis reflects broader concerns about trust and accountability in AI-driven systems. Black-box models may deliver high predictive accuracy, but their opacity can undermine confidence and hinder adoption in safety-critical environments. The study argues that future smart manufacturing systems must balance performance with transparency, ensuring that intelligence remains aligned with human values and organizational goals.

The review also connects smart manufacturing to broader sustainability transitions beyond the factory floor. Applications in logistics, energy systems, and smart infrastructure demonstrate how intelligent production systems interact with supply chains, power grids, and urban environments. Digital twins and AI-driven optimization enable coordinated decision-making across these domains, supporting decarbonization and resilience at scale.