Manufacturers invest 69% of their IT budget into devices, software, and other technologies used to connect their frontline, gain greater asset visibility, and intelligently automate workflows, says a new research.

Zebra Technologies Corporation, a global leader in digitising and automating workflows to deliver intelligent operations, on Tuesday shared the key drivers that will shape the frontline in 2026, as manufacturers prioritise data-driven cultures to stay competitive and address market complexities.

Automated, on-Demand training 

Zebra’s Impact of Intelligent Operations report in collaboration with Oxford Economics found that augmentation of worker training tools is fast becoming a higher priority for manufacturing workforce leaders. They are investing in interactive kiosks, training videos, and smart devices to upskill workers and accelerate onboarding processes. 

These tools provide on-demand access to instructional content, safety protocols, and troubleshooting guides, enabling workers to learn and adapt quickly on the job.

By combining automation with comprehensive training support, they aim to bridge skill gaps, improve worker confidence, and reduce turnover rates. 

These investments are delivering in-the-moment access to training modules, digital work instructions and call, text, video and photo communication with plant floor and remote employees, including specialised technicians who can support multiple facilities with greater efficiency.

“Worker attrition remains a critical challenge across the manufacturing sector, driving the adoption of intelligent automation solutions to fill labour gaps and enhance productivity,” said Stephan Pottel, Manufacturing Strategy Director EMEA, Zebra Technologies. “Intelligent factories leveraging Industry 4.0 and 5.0 principles are automating tasks and empowering frontline workers through advanced tools and technologies.”

Edge intelligence to solve quality challenges

Localised data processing, reduced latency and enhanced responsiveness are driving edge computing strategy. This shift is impactful in addressing quality challenges on the production or shop floor, where real-time insights are critical for maintaining standards and minimising defects.

Machine vision systems used to detect defects during production are proving to be one of the most transformative applications of AI and machine learning. Equipped with high-resolution cameras and edge intelligence, these systems can analyse products in real time. They can also identify imperfections such as surface flaws, dimensional inaccuracies, or assembly errors.

By processing data locally at the edge, manufacturers can achieve immediate feedback and corrective actions, preventing defective products from advancing through the production line and reducing waste. 

AI-driven systems contribute to continuous improvement by identifying patterns in quality issues. Algorithms can analyse historical defect data to uncover root causes, enabling proactive adjustments to production processes and equipment settings.

“By leveraging machine vision and edge intelligence, manufacturers are transforming quality management from a reactive process to a proactive, real-time capability,” said Pottel. “This advancement equips production teams to address quality challenges as they happen, ensuring consistent output, enhancing customer satisfaction, and reducing costs associated with defects.”

Securing traceability 

Location-based technologies such as RFID and machine vision are becoming essential for ensuring product traceability, reducing waste, and enhancing quality control. These technologies are particularly impactful in high-tech manufacturing, such as semiconductor fabrication, aerospace, and advanced electronics, where the complexity and precision of production processes demand meticulous oversight.

The drive toward re-shoring, spurred by tariffs and geopolitical pressures, has further amplified the need for robust traceability solutions. As manufacturers relocate operations to domestic or allied regions, technologies like RFID and machine vision are being deployed to maintain visibility across the supply chain, ensuring compliance with quality standards and regulatory requirements.

Manufacturers are also embracing sustainable practices, particularly in sectors like food and pharmaceuticals. Technologies such as IoT and blockchain enhance traceability across supply and cold chains, minimising waste and ensuring quality. Sustainability goals are now integral to brand reputation and regulatory compliance.

“In a landscape increasingly shaped by economic policies and technological innovation, the adoption of RFID and machine vision is proving to be a critical enabler of sustainable, high-quality manufacturing practices,” said Pottel. -TradeArabia News Service