Most biopharma organizations invest heavily in training, yet struggle to answer a basic question: is that training actually improving performance?
In many labs, training success is still inferred from completion rates or informal feedback rather than tracked through R&D training KPIs that link learning to quality, speed, and cost. As pressure grows to improve lab reproducibility, reduce operational waste, and accelerate timelines, this gap is becoming harder to ignore.
Training directly influences outcomes such as time to independent work, deviation rates, rework, and throughput. However, without defined R&D training metrics and standardized instructional approaches, these effects remain difficult to measure.
In 2026, leading teams are moving beyond “training delivered” toward training measured against R&D performance indicators, increasingly supported by visual method-level instruction.
Why Measurement Matters: Reproducibility & Risk
Concerns about reproducibility in biomedical research are well documented. A large cancer biology replication effort found that most preclinical experiments could not be repeated due to missing or unclear methodological detail, with replication taking years and significant funding.¹ In day-to-day R&D environments, similar gaps in SOPs and training lead to rework, inconsistencies, and avoidable errors.
A 2024 survey of more than 1,600 biomedical researchers found that 72 percent believe the field is facing a reproducibility crisis, yet few institutions have formal procedures to address it.² Most researchers reported learning reproducible practices informally rather than through standardized training.
This leaves a clear operational gap. Training is rarely aligned with lab training metrics, making its impact difficult to measure, manage, or improve systematically.
Turning Training Into Measurable Impact
R&D training metrics shift training from a checkbox exercise into a controllable operational input. They allow teams to identify where methods routinely break down, where retraining is needed, and which SOPs or instructional materials lack sufficient clarity, often before issues lead to deviations or costly rework.
To be effective, these metrics must be embedded within a training framework that standardizes how methods are taught, assessed, and refreshed over time. Increasingly, biopharma teams are integrating visual method demonstrations into their LMS and SOP ecosystems to support consistent execution across operators, shifts, and sites.
Defining Core Lab Training KPIs
Once reproducibility is treated as an operational risk, measurement becomes essential. The KPIs below are commonly used to evaluate lab training effectiveness and link training to quality, speed, and cost. The examples shown clarify structure rather than define regulatory standards.
1. Time to Competency (TTC) ✔️
Time to competency measures the average number of days from first formal training exposure to a trainee’s first approved independent run.
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Example calculation: Days to independent run ÷ number of trainees - 🔸 Typical data sources: LMS assignment dates, supervisor sign-off records
- 🔸 Review cadence: Monthly during onboarding periods; quarterly for established teams
Improvement is seen when ramp-up time decreases without increased deviations or rework. Consistently long TTC often signals unclear SOPs or insufficient instructional detail rather than a need for extended shadowing.
2. Technique-Related Deviation Rate ✔️
This KPI captures the deviation rate in labs caused by how a method is executed or interpreted, rather than by equipment or materials.
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Example calculation: Days to independent run ÷ number of trainees - 🔸 Typical data sources: Deviation logs, quality systems, CAPA records
- 🔸 Review cadence: Monthly at the assay or method level
Improvement appears as a sustained reduction in execution-related deviations after training updates. Spikes after onboarding or method transfer often indicate training gaps rather than CAPA (Corrective and Preventive Actions) issues.
3. Rework Hours and Cost ✔️
Rework in R&D measures the time and financial impact of repeating experiments due to preventable execution errors. It often reveals inefficiencies that are not visible in high-level project timelines.
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Example calculation: Rerun hours × internal labor cost rate - 🔸 Typical data sources: Time-tracking tools, project management systems, and finance records
- 🔸 Review cadence: Quarterly, aligned with project reviews
Improvement appears as fewer reruns, targeted training, and cost saving. Persistent rework often points to unclear SOPs or sign-off-based competency.
4. Throughput ✔️
Lab throughput reflects the volume of successful assays or experiments a team completes over a defined period of time. While often attributed to staffing or automation, training quality has a direct effect on assay throughput.
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Example calculation: Successful runs per week or month - 🔸 Typical data sources: LIMS records, experiment logs
- 🔸 Review cadence: Monthly for active development programs
Improvement means higher output without increased deviations or rework. When throughput remains constrained, inconsistent technique or onboarding are common contributors.
5. Proficiency Retention ✔️
Proficiency retention tracks how well staff maintain correct execution over time, especially for infrequently used techniques.
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Example calculation: Correct executions after a defined time gap ÷ total assessed executions - 🔸 Typical data sources: Observed runs, periodic competency assessments
- 🔸 Review cadence: Semi-annual
Improvement is seen when performance stays stable and errors decline. Where proficiency decays, short, on-demand refreshers can reduce reactive retraining and troubleshooting.
Why Video Articles Support Measurable Training
Video articles address common R&D training failure points by showing how methods are actually performed, including hand positioning, timing, visual checkpoints, and common failure modes. This reduces interpretation gaps that text-only SOPs often leave unresolved.
In practice, video articles are most effective when embedded directly into existing training frameworks. They can be assigned through an LMS alongside SOPs, used during onboarding before supervised bench work, and revisited during retraining after deviations or method updates. When integrated with LMS and LIMS systems, video exposure, assessment results, and retraining events can be correlated with metrics such as time to competency, deviation trends, and rework frequency.
Research shows that visual instruction improves skill acquisition and retention compared with text-only formats, making video particularly valuable for complex or high-risk techniques.³
Designed deliberately, video-based lab training can support R&D KPIs in measurable ways:
- 📈 Shorter time to competency by making critical execution details visible before independent work
- 📈 Lower technique-related deviation rates through consistent, step-by-step guidance
- 📈 Reduced rework in R&D by helping operators avoid common failure points
- 📈 More reliable lab throughput by decreasing variability between operators and sites
- 📈 Stronger proficiency retention through quick refreshers for infrequently performed or high-risk procedures
From Training Activity to Measurable Impact
Training delivers real value only when it is measured against outcomes that matter. By defining clear R&D training KPIs, organizations can connect learning to reproducibility, cost control, and development speed.
As teams plan for 2026, a practical starting point is to map high-impact assays to relevant metrics, pilot measurement-driven training on a small, visible portion of the portfolio, and scale what demonstrably improves performance.
If you’re strengthening R&D training for 2026, JoVE’s visual resources can support clarity, consistency, and measurable skill development.
Start with a focused pilot to shape your 2026 lab training plan.
- Errington, T. M., et al. (2021). Investigating the replicability of preclinical cancer biology. eLife, 10, e67995. https://elifesciences.org/articles/67995
- Fierce Biotech. (2024). 72% of biomedical researchers think the field is facing a reproducibility crisis. https://www.fiercebiotech.com/research/72-biomedical-researchers-think-field-facing-reproducibility-crisis-survey
- Green, T. D., et al. (2019). Video-based learning in laboratory training. Journal of Laboratory Education, 8(2), 45–52. https://pmc.ncbi.nlm.nih.gov/articles/PMC6373476/