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TL;DR
Lab KPIs show whether your R&D lab is operating efficiently, reliably, and safely, not simply staying busy.
- 12 lab metrics spanning research, sample management, and safety.
The lab efficiency metrics we cover include experiment cycle time, approval rate, template adoption, sample turnaround and traceability, inventory accuracy, stockouts, equipment utilization, maintenance, training compliance, corrective-action closure, and administrative time saved.
- Start with 6-8 KPIs tied to your lab’s priorities.
Establish your own baseline, assign each KPI an owner, and balance speed or volume with quality and completeness.
- Digital lab platforms can help make success visible.
SciSure connects ELN, LIMS, inventory, equipment, and EHS records, giving you a reliable data foundation for identifying bottlenecks and tracking improvement.
- SciSure in action.
Averaged data from 30+ labs and organizations using SciSure showed everyday tasks becoming approximately 88% faster, complex reporting 99% faster, and administrative work falling by approximately 88%.
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Your lab can be extremely busy without necessarily performing well.
For example, you might have experiments running, samples moving, equipment calendars packed to the brim, and your scientists answering messages, updating records, and solving problems all day. From the outside, everything might appear productive. But if someone asked you, “How is the lab actually doing?” could you answer with confidence?
You might know that your team is working hard. You may also have a general feeling that inventory is improving, audits are becoming easier, or scientists are spending less time on administration. What's harder is showing that progress clearly and noticing early when something is heading in the wrong direction. That's where lab key performance indicators, or KPIs, can help.
You don't need to measure everything. In fact, you probably shouldn’t. You need a small, balanced set of indicators that reflects what success means for your lab. Here are 12 useful places to start.
What makes a good lab KPI?
The right lab KPIs give you a practical way to see whether research is moving efficiently, resources are being used well, records are reliable, and safety requirements are under control. They also give you a shared language for discussing progress with scientists, LabOps, EHS, and leadership.
A metric tells you what happened. A useful KPI helps you decide what to do next.
For example, “We completed 400 experiments this quarter” is a metric. On its own, it does not tell you whether those experiments were completed on time, documented consistently, reviewed promptly, or repeated because something went wrong.
A useful KPI should be:
- Connected to a goal that matters to your lab
- Defined clearly enough that everyone calculates it the same way
- Based on data you can collect consistently
- Reviewed at a sensible cadence
- Owned by someone who can respond when it changes
Targets will differ between labs. A small discovery team should not be compared with a high-throughput testing facility, and an academic research group will have different priorities from a regulated biopharma organization. Your own baseline is usually the most helpful starting point. Measure where you are today, decide what improvement would look like, and track the direction of travel.
Research workflow lab KPIs
1. Experiment cycle time
Experiment cycle time measures how long it takes work to move from a defined starting point to completion. Depending on your lab, that could mean the time between creating and closing an experiment, receiving a request and delivering a result, or beginning a workflow and reaching an approved outcome.
A simple calculation is:
Experiment cycle time = Completion date and time − Start date and time
Consider using the median rather than the average so that one unusually long experiment does not distort the result. The purpose is to identify avoidable waiting: delayed handoffs, missing materials, equipment bottlenecks, unclear approvals, or administrative steps that repeatedly interrupt progress.
Structured experiment records, statuses, timestamps, and configurable workflows in the SciSure ELN can provide the underlying information you need to examine cycle times and find recurring delays.
2. On-time review and approval rate
An experiment may be scientifically complete but remain operationally unfinished while it waits for review, sign-off, or clarification. Your on-time review rate shows whether those final steps are moving as expected. Here's a formula you can use:
On-time review rate = Reviews completed within the agreed timeframe ÷ Reviews due × 100
If this number begins to fall, the problem may not be scientist productivity. Reviewers may have too much work, responsibilities may be unclear, or records may be reaching review without the necessary information.
SciSure approval workflows, notifications, timestamps, witness signing, and audit trails help you see where records are in the review process and which actions are still outstanding.
3. Approved protocol or template adoption
Templates and controlled protocols can improve consistency, but only if people use them. A practical adoption KPI is:
Template adoption = Eligible experiments using the current approved template ÷ Total eligible experiments × 100
A low rate may indicate that scientists cannot find the right template, the approved version does not fit the real workflow, or teams have created unofficial alternatives. This information helps you spot these gaps early and take steps quicker. Sometimes the template itself needs to be simplified or updated.
SciSure supports reusable experiment templates, protocol management, version control, and standardized workflows, helping you understand whether approved methods are becoming part of everyday work.
Sample management lab KPIs
4. Sample turnaround time
Sample turnaround time measures how long a sample takes to move through a defined workflow.
You first need to agree on what starts and ends the clock. For one team, it may run from sample registration to analysis. For another, it might run from receipt to an approved result.
Sample turnaround time = Workflow completion time − Sample registration or receipt time
Tracking the median time (and the percentage completed within your expected timeframe) can reveal queues, handoff delays, storage problems, and overloaded equipment.
SciSure LIMS provides structured sample records, real-time statuses, configurable workflows, and lifecycle histories. When your start and completion points are captured consistently, that data can support turnaround-time reporting and bottleneck analysis.
5. Sample traceability completeness
A sample traceability KPI measures whether active samples contain the information your lab requires, such as identity, status, owner, location, lineage, movement history, and links to relevant experiments.
Traceability completeness = Samples with all required information ÷ Samples assessed × 100
You can also track exceptions separately: samples with an unknown location, missing parent record, incomplete chain of custody, or no linked experiment.
Inventory and resource lab KPIs
6. Inventory accuracy
Inventory accuracy compares what your system says you have with what is physically present. Hre's a formula you can use:
Inventory accuracy = Records matching the physical count ÷ Records checked × 100
You don't necessarily need to count the entire lab every month. Regular cycle counts of high-value, high-risk, or frequently used materials can provide an early warning that inventory quality is slipping.
Poor inventory accuracy creates problems well beyond inventory management. Scientists lose trust in the system, emergency orders increase, experiments are delayed, and compliance reports become harder to produce.
SciSure provides container-level and item-level tracking, barcode workflows, real-time inventory updates, searchable records, and audit trails to help teams maintain a reliable view of what is available and where it is located. For example, Arctic Therapeutics eliminated manual inventory counting, gained real-time visibility into stock and reagent availability, and saved approximately two hours each week on sample registration and inventory management with SciSure.
7. Critical-item stockout rate
Running out of a low-priority item may be inconvenient. Running out of a critical reagent can stop an entire workflow. You can track stockouts as a simple monthly count or as a rate:
Stockout rate = Critical items with at least one stockout ÷ Critical items tracked × 100
This KPI should be balanced with inventory waste. If you focus only on eliminating stockouts, teams may respond by overordering. Here are some useful companion measures include:
- Value of expired inventory
- Number of items discarded unused
- Emergency orders per month
- Percentage of inventory above its maximum desired level
SciSure supports real-time stock monitoring, low-stock alerts, customizable inventory categories, pending-order tracking, and usage reporting. These features help you make purchasing decisions using current demand rather than guesswork.
8. Equipment utilization
Equipment utilization shows how much of an instrument’s available capacity is being used. This formula can help you figure it out:
Equipment utilization = Hours used ÷ Hours available × 100
The goal is not automatically 100%. Very high utilization can create queues and leave no room for urgent work, maintenance, or unexpected demand. Very low utilization may indicate unnecessary capacity, limited training, poor discoverability, or an instrument that no longer matches your needs.
Context matters always, so make sure you review utilization alongside bookings, cancellations, downtime, experiment demand, and maintenance history.
SciSure provides equipment booking calendars and detailed usage logs showing who used an instrument, when it was used, and for what purpose. This can help you identify heavily constrained assets as well as equipment that is being underused.
9. Maintenance and calibration compliance
A busy instrument is not useful if it is unavailable, unreliable, or overdue for calibration. A straightforward lab KPI is:
Maintenance compliance = Maintenance and calibration tasks completed on time ÷ Tasks due × 100
You may also want to track:
- Unplanned downtime
- Number of overdue calibrations
- Time from issue report to repair
- Repeat faults by equipment types
Safety and compliance lab KPIs
10. Training compliance
Training compliance measures whether people have completed the current training required for their roles, activities, equipment, and hazard exposure.
Training compliance = Current completed assignments ÷ Required assignments × 100
A lab-wide percentage is useful, but it can also hide important risks. Segment the result by lab, role, training type, hazard, or location so you can see exactly where gaps exist.
SciSure connects training requirements with people, roles, equipment, and hazards. Automated assignments, reminders, escalation workflows, and real-time dashboards help you see who is current and where action is needed. For example, San Diego State University increased its reported training compliance from 56% to more than 80% after implementing SciSure across several EHS workflows.
11. Corrective-action closure time
Inspections, incidents, near misses, and safety observations only create improvement when the resulting actions are completed. Corrective-action closure time measures the period between opening and resolving an action:
Closure time = Corrective-action completion date − Opening date
You could also track:
- Percentage of actions closed by their due date
- Number of overdue high-risk actions
- Repeat findings
- Time to assign an owner
- Time between an incident and the start of an investigation
Be careful about treating a low number of reported incidents as proof that your lab is safe. It may mean fewer incidents—or it may mean people are not reporting them. Reporting activity, closure speed, severity, and repeat findings are more informative when considered together.
SciSure supports configurable inspections, incident and near-miss reporting, notifications, investigation workflows, corrective-action tracking, and real-time dashboards. This helps you follow an issue from the initial report through resolution.
Productivity and value KPIs
12. Administrative time returned to science
Some of the most meaningful improvements are measured in time your scientists and operational teams no longer spend searching, correcting, reconciling, or rebuilding information. You can calculate time returned using a simple before-and-after comparison:
Time returned = Baseline task time − Current task time
To estimate the wider impact:
Total time returned = Time saved per task × Number of times the task is completed
This works particularly well for repetitive activities such as:
- Finding samples, chemicals, or SDSs
- Correcting inventory records
- Reconciling a storage location
- Preparing compliance reports
- Reconstructing experiment histories
- Identifying people working with a particular hazard
- Chasing information across teams
In an analysis of averaged workflow data from more than 30 organizations using SciSure, everyday tasks became approximately 88% faster, complex reporting work fell by approximately 99%, and administrative work fell by approximately 88%.
Specific examples included identifying everyone working with a high-risk material falling from 14 hours to 14 minutes, complex inventory reporting falling from 21 hours to seven minutes, and preparing a leadership safety summary falling from three days to under 30 minutes.
Those figures are examples, not universal targets. Your workflows, starting point, configuration, and usage will affect your results. The most credible way to demonstrate value is to establish your own baseline before making a change and measure the same task again afterwards.
You can explore the complete findings in Lab Software Adoption: How 30+ Labs Improved Efficiency with SciSure.
KPIs that can accidentally mislead you
A KPI can change behavior simply because people know it is being measured. That makes thoughtful selection important.
Watch out for measures such as:
- Total experiments completed.
This may reward volume without accounting for complexity, quality, or value.
- Total samples processed.
Higher throughput is not necessarily better if errors, delays, or incomplete records also increase.
- Low incident numbers.
This can reflect under-reporting rather than improved safety.
- Maximum equipment utilization.
Running equipment at full capacity leaves little room for maintenance or unexpected work.
- Software logins.
Logging in does not prove that someone completed a meaningful workflow or received value.
Whenever possible, pair a quantity metric with a quality, timeliness, or completeness metric. For example, review sample throughput alongside turnaround time and traceability completeness.
How many lab KPIs should you track?
Start with 6-8 KPIs connected to the problems your lab most wants to solve. For each one, document:
- The question you want the KPI to answer
- The exact calculation
- The data source
- Your current baseline
- Your initial target
- The person responsible for reviewing it
- How often it will be reviewed
- What action should follow if the result moves in the wrong direction
A KPI with no owner or response plan tends to become dashboard decoration. Make sure you review operational indicators monthly or quarterly, depending on how quickly the underlying process changes. More urgent safety or compliance information may need weekly or real-time review.
Targets should also evolve. Once a process is stable, you may need a more ambitious target, or you may decide that the KPI no longer deserves a prominent place on your scorecard.
How SciSure helps make lab performance visible
Many labs struggle to measure performance because the necessary information is scattered across paper records, spreadsheets, inboxes, point solutions, and people’s memories. SciSure brings research, samples, inventory, equipment, and safety workflows into one connected Scientific Management Platform. That gives you a stronger data foundation.
- ELN records can show how experiments are documented, reviewed, and approved.
- LIMS workflows can show how samples move through their lifecycle.
- Inventory records can reveal stock levels, usage, locations, and purchasing needs.
- Equipment records can show bookings, usage, maintenance, calibration, and repairs.
- EHS dashboards can provide visibility into training, inspections, incidents, corrective actions, and compliance status.
- Reports, exports, APIs, and integrations can help you combine operational data with other business or financial information when needed.
Not every definition of success comes directly from software. Scientific quality, employee confidence, cost per experiment, portfolio progress, and business impact may require additional qualitative, financial, or project data.
But when your operational records are connected and reliable, you can stop building every answer from scratch. You have a clearer view of what is working, where friction is accumulating, and where your next improvement will have the greatest effect.
Make progress easier to see
Your lab’s success is the combination of reliable research records, traceable samples, available materials, ready equipment, responsive safety processes, and scientists who can stay focused on their work. The right KPIs make those conditions visible.
Start with the questions your team is already asking. Where are we waiting? What are we repeatedly fixing? Which risks are becoming overdue? Where are scientists losing time? Then choose the smallest set of measures that will help you answer those questions and take action.
When you can see how your lab is performing, improvement becomes much less abstract and success becomes something you can demonstrate, not just feel.
Ready to get a clearer view of your lab?
SciSure connects research, lab operations, and safety data so your team can identify bottlenecks, track progress, and make decisions with greater confidence.
Talk to a SciSure specialist to explore how your existing workflows could support a practical lab performance scorecard.
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