Laboratories rely on instruments that must perform consistently, but the information needed to manage those instruments is often scattered across spreadsheets, maintenance records, and local files. When data is fragmented, risks remain difficult to identify, maintenance becomes harder to prioritize, and long-term decisions rely too much on incomplete information. A data-driven framework developed for laboratory instrument management shows how centralized, traceable data can make risks more visible and support better decisions.

Laboratory instruments are often treated mainly as technical devices that must be calibrated, maintained, and kept running. In practice, many management problems begin before any technical failure occurs. The challenge is not always the instrument itself, but the way information about it is stored and used.

If maintenance history, fault records, documentation, responsibilities, and life cycle information are spread across multiple locations, the laboratory loses visibility over its instrument base. It becomes harder to see which instruments are performing well, which ones are causing repeated problems, and where the biggest operational risks are building up.

LabDevs was developed to solve this problem in a non-accredited industrial laboratory environment. Its main idea is simple: when instrument data is centralized, structured, and traceable, the laboratory can make more informed decisions about maintenance, risks, and future investments.

What changes when instrument data is brought together?

LabDevs is a browser-based information system that centralizes instrument data, maintenance records, fault documentation, and related life cycle information. Built from user requirements and supported by principles from quality management, information governance, and reliability engineering, it treats instruments as managed assets whose condition, history, and risk profile should remain visible over time.

The system supports:

• a centralized instrument registry
• preventive maintenance scheduling
• direct fault alerts to administrative users
• defined user roles and responsibilities
• auditability and traceability through system logs
• reliability monitoring through selected operational metrics

This changes the role of data in instrument management. Instead of being a collection of disconnected records, instrument data becomes a usable foundation for daily decisions and long-term planning.

Reliable operations depend on reliable data

Reliable laboratory operations do not depend only on equipment performance. They also depend on the quality of the information that supports decisions. According to recent literature on laboratory quality management, information management, traceability, defined responsibilities, and controlled records are essential parts of a well-functioning laboratory system (Pillai & Fox, 2025). Reliability engineering adds another important perspective: reliability is not just performance at one moment, but performance over time (Levin et al., 2019).

When instrument-related information is incomplete or difficult to access, preventive maintenance becomes more difficult to coordinate. Repeating failure patterns are more difficult to see, and life cycle decisions rely too much on fragmented records or informal knowledge. As a result, operational risks may remain hidden, and accountability remains weak if responsibilities are not clearly defined.

In this context, poor information governance is not just administrative inefficiency. It directly weakens the laboratory’s ability to manage reliability in a systematic way.

A data-driven framework improves visibility

The developed framework addresses this challenge by bringing instrument-related information together into a structured system. It combines instrument data, maintenance history, fault records, life cycle information, and user responsibilities. This improves the accessibility, consistency, and traceability of data throughout the instrument life cycle.

The framework also introduces practical operational reliability indicators that can be calculated from existing maintenance and fault history data. These include availability, mean time between failures (MTBF), mean downtime (MDT), operating time, downtime, service life, and total number of failures.

These indicators help answer questions such as:

• Which instruments fail most often?
• Which ones stay out of service too long?
• Which instruments appear stable, and which require closer attention?

This is where the framework becomes especially valuable. Many laboratory information management systems (LIMS) focus mainly on sample handling, analytical workflows, and result reporting. Those functions are essential, but they do not automatically reveal how instruments perform as assets over time. A data-driven instrument management framework fills this gap by turning maintenance and fault data into information that supports decisions.

Risk-based maintenance needs more than failure counts

An instrument that fails often may cause inconvenience, but the real concern arises when that instrument is also critical to laboratory operations. That is why the framework goes beyond failure history alone and brings instrument criticality into maintenance decision-making. To support this, a criticality classification for laboratory instruments was developed by adapting the PSK 6800 standard to the laboratory environment.

When criticality is combined with reliability performance in a data-driven risk evaluation model, the laboratory gains a clearer view of where attention is needed most. This supports more effective maintenance prioritization and provides stronger justification for repair or replacement investments.

In practice, this helps laboratories use their time and resources where they have the greatest operational impact. It does not replace technical expertise or maintenance work. Instead, it strengthens both by providing a clearer and more evidence-based foundation for decision-making.

Better information governance leads to better decisions

The value of the framework is therefore not only technical. Its broader value lies in strengthening the quality of management. Centralized and traceable data supports clearer responsibilities, stronger auditability, and more consistent documentation practices. These are core elements of good information governance, and they are also preconditions for reliable decision-making (Bernardo et al., 2024).

This is the central message of the work: many challenges in laboratory instrument management are not primarily caused by a lack of technical competence or maintenance effort. They arise because the information needed to manage instruments systematically is not always accessible, consistent, or traceable.

A data-driven framework addresses this problem by making instrument reliability visible in a structured and actionable form. Once the information base is strengthened, the laboratory is better positioned to move from scattered follow-up toward more proactive and transparent management.

The framework demonstrates an important principle: laboratory reliability begins with better information governance. When instrument data is treated as a strategic asset instead of a collection of disconnected records, maintenance, risk management, and long-term planning can all become more systematic and evidence based.

References

Bernardo, B. M. V., Mamede, H. P. S., Barroso, J. M. P., & Santor, V. (2024). Data governance & quality management — Innovation and breakthroughs across different fields. Journal of Innovation and Knowledge, 9(4), 1–35. https://doi.org/10.1016/j.jik.2024.100598

Levin, M. A., Kalal, T. T., & Rodin, J. (2019). Improving product reliability and software quality: Strategies, tools, process and implementation (2nd ed.). John Wiley & Sons. https://doi.org/10.1002/9781119179429.ch17

Ojala, M. (2026). LabDevs: A Data-Driven Framework for Managing Laboratory Instruments and Operational Reliability. Master’s Thesis, Turku University of Applied Sciences. https://urn.fi/URN:NBN:fi:amk-2026052818490

Pillai, S. P., & Fox, E. (2025). Laboratory quality management system fundamentals. Frontiers in bioengineering and biotechnology, 13, 1578654. https://doi.org/10.3389/fbioe.2025.1578654

PSK Standardisointiyhdistys ry. (2008). Criticality classification of equipment in industry (PSK 6800).

Image source

Microsoft. (2026). Analysis. [Stock image]. Microsoft 365.