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While labs have digitized the science — ELNs, LIMS, instrument software — the operational layer underneath (inventory, equipment scheduling, workflow coordination) often remains stuck in spreadsheets and sticky notes. IDBS’s Unjulie Bhanot walks through five common breakdowns, from uncertain inventory status to invisible bottlenecks, arguing that the fix isn’t collecting more data but connecting the data labs already have. The piece makes the case for unified lab operations platforms as the foundation for both day-to-day efficiency and future AI-driven optimization.
Lab operations refer to the systems, workflows and processes that support scientific work, including inventory management, equipment readiness, workflow scheduling and data handling. When lab operations are inefficient or disconnected, they can significantly delay experiments, reduce productivity and impact data integrity.
Why digitizing experiments isn’t the same as digitizing lab operations
It’s 9:12am on a Tuesday morning. A scientist pauses an experiment because the centrifuge they need is out of its maintenance window. They walk to another lab to find one that’s suitable. By the time they get back, the samples on their bench have warmed up. At 9:45am, a courier delivers a batch of acetonitrile, only for it to be from the wrong supplier. By 9:55am, the experiment is aborted and by 10:15am, they’re rearranging tomorrow’s planned work to accommodate today’s failure.
The science didn’t fail; the system did. The scientist lost the hour, then the day, and possibly even the week.
This kind of morning is more common than most lab leaders would like to admit. And it rarely shows up in any dashboard, because the data that would have prevented it― equipment maintenance status, inventory specifications, work scheduling―lives in spreadsheets, email threads, logbooks and, often, someone’s memory.
When time is the resource labs can least afford to waste, the systems that run the lab must work as hard as the science inside it.
The real cost of fragmented lab operations in modern laboratories
Labs have made significant progress digitizing experimental workflows. Electronic lab notebooks have replaced paper; instrument software captures raw data and LIMS platforms manage sample submissions. But step back from the bench and a different picture emerges. The operational layer i.e., the work that supports the work, remains stubbornly disconnected.
The numbers bear this out. A recent IDBS survey of 856 biopharma professionals found that data silos (26%), poor integration (30%) and limited scalability (34%) rank among the top frustrations with current lab informatics solutions1. Gartner estimates that poor data quality costs organizations at least $12.9 million per year2―a figure that, as lab data volumes continue to grow, is unlikely to have come down.
The challenge isn’t the lack of data, it’s a lack of connection. Traditional systems prioritize experiments over operations, creating fragmented silos, inconsistent capture and manual, error-prone effort. The impact? Reduced lab inefficiency, slower laboratory workflows, delayed insight and avoidable rework.
Think of lab operations as air-traffic control for your bench. Scientific work follows a continuous cycle: plan, execute, collect, analyze and repeat. Lab operations is the layer that keeps that cycle running safely, efficiently and in compliance: confirming materials are available, instruments are qualified, work is scheduled and capacity exists to meet demand. When that operational data is disconnected from the science, it’s like running an airport without a control tower. Flights may still depart, but delays, collisions and wasted fuel are inevitable.
A new generation of unified lab informatics platforms is designed to close these gaps, not by replacing what works, but by connecting it.
Here are five lab operations challenges where that connection matters most:
1. Inventory uncertainty: do we have it, is it in spec, and where is it?
Ask any scientist about their least productive hours and there’s a good chance it involves hunting for materials. Freezer logs are outdated. Spreadsheets disagree. By the time you’ve confirmed a sample’s location, status and chain of custody, you’ve lost time, or worse, started work with materials that shouldn’t have been used.
When lab inventory management is fully integrated into the lab operations workflow―sharing the same data model, permissions and audit framework as experiment execution―materials are tracked from receipt through consumption with real-time visibility. Scientists start every experiment knowing what’s available, where it is and whether it’s fit for purpose.
For teams managing bioanalytical or preclinical studies, that means confirming sample integrity (including freeze/thaw cycles and cumulative bench time) before an assay begins, not after a failed result triggers an investigation. In QC environments, it means verifying reagent lot numbers and expiry before a run starts.
Inventory becomes a connected, traceable part of how work is performed―not a static record maintained in parallel.
2. Equipment management in lab operations: which instrument is actually ready right now?
Labs depend on shared instruments, such as HPLCs, balances, spectrophotometers, centrifuges, but determining calibration and availability status is surprisingly difficult. Maintenance logs live in binders whereas bookings happen via shared calendars or, more simply, sticky notes on the instrument door. A failed calibration discovered mid-run means scrapped work, wasted reagents and a frustrated scientist.
Integrated equipment management within lab operations software provides real-time visibility into availability, maintenance state, calibration dates and utilization history. Work is planned around reality, not assumptions.
In GxP environments, whether that’s a process development lab scaling up a formulation or an analytical lab running release testing, a digital record of instrument qualification status supports audit readiness without additional documentation effort. Audit trails are built in, not bolted on.
3. Lab workflow scheduling challenges: what’s planned, what’s in progress and who’s doing what?
Work orchestration in most labs is a patchwork of LIMS worklists, whiteboard schedules, email threads and verbal handoffs. It’s difficult to see what’s queued, where bottlenecks are forming, or whether turnaround commitments, such as stability pull-point schedules or method transfer milestones, are at risk. When stakeholders ask for a status update, the answer typically requires a round of messages and a walk through the lab.
A built-in request management capability transforms this visibility gap. Teams create, assign, track and prioritize work in a single system, linked directly to the samples, methods and instruments involved. Managers gain real-time insight into workload distribution, queue depth and turnaround times.
If priorities shift (as they often can), re-sequencing becomes a deliberate action, not another chain of emails.
4. Manual data entry in lab workflows: a hidden productivity drain
Every time a scientist copies a result from an instrument readout into a notebook, then into a spreadsheet, then into a report, two things happen: errors creep in and time evaporates. In analytical and development labs alike, manual data transcription accounts for more of total cycle time than most teams realize, with each handoff presenting a potential point of failure.
Connected testing workflows that integrate directly with instruments capture results at the source, auto-populate structured records and maintain a complete audit trail from acquisition through reporting.
In GxP-regulated environments, this isn’t just a lab workflow efficiency gain, it’s a critical component of data integrity and compliance. Fewer manual steps means fewer deviations, fewer investigations and faster batch release. Data is immediately available for review, trending and decision-making, supporting faster out-of-specification investigations and tighter development cycles.
5. Lack of visibility in operations: you can’t optimize what you can’t see
Even when individual experiments are meticulously documented, operational data often lives in disconnected silos; one system for notebooks, another for inventory, another for test results, another for work orders. Lab leaders can’t easily answer fundamental questions. How long does this assay actually take end-to-end? Where are we losing time between sample receipt and result? Is the bottleneck in method development turnaround or in instrument availability? What’s our true lab utilization rate?
A connected lab operations data backbone enabling dashboards and analytics that transform lab activity into actionable insight. Bottlenecks become visible. Capacity planning shifts from guesswork to evidence.
This is also the foundation for AI-enabled optimization, but even before AI enters the picture, simply having connected, contextualized data in one place enables a step-change in how labs understand and improve their own performance.
Data connection, not just data collection in lab operations
The common thread across all five challenges is the same: not a lack of data, but a lack of connection between systems, workflows and teams. Labs don’t need more data collection. They need data connection.
Modern lab operations platforms and lab operations software solutions are designed to close these gaps: connecting sample management, inventory, equipment, work orchestration, testing and operational analytics into a single lab operations layer alongside the scientific record. Context is captured as work happens, not reconstructed later.
A shared data model links sample identity, methods, inventory and instruments, with full audit trails and bidirectional traceability.
The result isn’t just a faster lab. It’s a lab that operates with confidence, where teams know what’s available, what’s planned, what’s in progress and what the data is telling them. Where managers see bottlenecks before they become crises, and compliance is built into the workflow, not added afterward.
Platforms such as IDBS Polar enable this connected approach to lab operations, helping BioPharma organizations make time a competitive advantage by improving efficiency, strengthening GxP compliance and accelerating time to insight.
That’s not a technology upgrade. It’s an operational transformation―and it starts with a clear runway.
To learn more, contact IDBS
About the author
Unjulie Bhanot, Product Marketing Manager, Process Development & Manufacturing, IDBS
Unjulie Bhanot is the Product Marketing Manager for Process Development and Manufacturing at IDBS. With over 10 years of experience in the Biopharma informatics space, she has led the strategy and development of IDBS’ Bioprocess solutions and was instrumental in the launch of IDBS Polar to market.
References:
- IDBS. Unveiling key insights from IDBS’ latest survey on BioPharma’s lab data management (2025).
- Gartner. Data Quality: Best Practices for Accurate Insights, accessed on June 10, 2026.
