The Analytical Intelligence™ of Things - Improving productivity through AIoT technology for analytical laboratory

Significance of the Topic

Analytical laboratories worldwide encounter challenges such as high operational costs, limited skilled personnel, and loss of institutional knowledge. The integration of Artificial Intelligence and Internet of Things technologies (AIoT) offers a pathway to enhance productivity, ensure data quality, and optimize instrument utilization within modern laboratory environments.

Objectives and Overview of the Study

This article reviews Shimadzu Corporation’s AIoT approach, Analytical Intelligence of Things, which aims to streamline laboratory workflows, enable predictive maintenance, and support consistent, high‐quality analytical results. The study highlights key system functions and real‐world applications that demonstrate improvements in lab efficiency.

Methodology and Instrumentation

Laboratory instruments are connected via M2M Routers to a cloud platform (Shimadzu LabTotal Smart Service Net), where operational metrics, error logs, consumable status, and chromatographic data are collected. Advanced AI algorithms perform peak integration (PeakintelligenceTM), detect pre-failure signs (Predictive Failure Function), and monitor mobile phase volumes using gravimetric sensors (MPMCheckerTM). The LabSolutions™ software on PCs and mobile devices visualizes data and delivers alerts.

Key Results and Discussion

Through AI-driven peak processing, data analysis time was reduced by one third compared to conventional methods, achieving a 90 percent match with expert integration. Automated diagnostics predicted maintenance dates, enabling proactive dispatch of service engineers and reducing unplanned downtime. Continuous monitoring of solvent levels prevented column damage and sample loss. Remote access and visualization of instrument status simplified consumable management and ROI assessment for lab assets.

Benefits and Practical Applications

• Enhanced reproducibility: High-quality data acquisition independent of operator skill.
• Labor savings: Automated peak integration and error recovery free up analysts for higher-value tasks.
• Reduced downtime: Predictive maintenance and auto-recovery minimize disruptions.
• Consumable management: Real-time tracking of solvents and parts lowers waste and prevents shortages.

Future Trends and Opportunities

Ongoing advancements in AIoT will enable deeper integration of big data analytics, remote laboratory management, and self-optimizing workflows. Expanded predictive models may forecast analytical outcomes, drive autonomous method development, and support regulatory compliance through comprehensive audit trails. Collaboration between cloud platforms and laboratory information management systems will further elevate efficiency and transparency.

Conclusion

Shimadzu’s AIoT framework demonstrates that coupling analytical instruments with cloud-based intelligence substantially improves laboratory productivity, data reliability, and asset utilization. As AIoT technologies mature, laboratories can expect continual enhancements in automation, remote support, and decision-making capabilities.

References

1. The White Paper on Information and Communications in Japan, MIC (2018)
2. S. Kanazawa et al., Deep learning methods applied to the analysis of metabolomics data, 67th ASMS Conference on Mass Spectrometry and Allied Topics, June 5, 2019