Failure Analysis Lab Insights

FA-LIMS White Papers on Modern Lab Operations

  • 1. Capacity and Allocation Control

    Laboratory capacity is finite, but demand rarely is. Requestors submit work according to their own deadlines, not the lab's, and without a mechanism to govern intake, a lab absorbs whatever arrives. The result is familiar: queues lengthen, turnaround times become unpredictable, and the lab is judged on commitments it never had the capacity to meet.

    This paper makes the case for governing intake at the sample level through allocations and limits, rather than managing demand informally or after the fact. It explains how a quota mechanism that consumes capacity when work is submitted and returns it when work is withdrawn gives a lab predictable throughput and a fair, transparent basis for setting expectations with the people it serves. It closes with how Quartz LIMS implements this model.

    PDF IconGoverning Capacity

  • 2. Governing the High-Value Instrument

    The most expensive assets in an analytical lab are usually its instruments, and they are frequently its least-measured ones. An electron microscope or focused ion beam tool represents a large capital investment whose value is realized only when it is in productive use. Yet many labs cannot say how heavily their instruments are used, who used them, or how much time was lost to idle periods, conflicts, and maintenance, because access is governed by a sign-up sheet and a shared understanding rather than by the system.

    Governing a high-value instrument well requires two things that are often treated as opposites. The first is control: access must be enforced, conflicts prevented, and maintenance protected. The second is flexibility: qualified users must be able to step up to an idle instrument and do quick, legitimate work without wrestling a workflow built for something else. A system that delivers only control wastes capacity and drives skilled users around it. A system that delivers only flexibility loses the record and the discipline. High-value instruments deserve both.

    This paper makes the case for governing instrument access as a single system that enforces who may use an instrument and when, ends sessions automatically, accommodates unscheduled walk-up use within limits, reserves time for maintenance without conflict, and lets qualified users serve themselves without sacrificing the controls that protect data and equipment. It explains how enforced access and governed self-service reinforce rather than undermine each other, and how both produce the data a lab needs to manage its instruments as the assets they are. It closes with how Quartz LIMS implements this.

    PDF IconGoverning the High-Value Instrument

  • 3. In-Context Image Review

    In an image-intensive laboratory, the deliverable is largely visual, and a great deal of skilled time is spent reviewing images: confirming that a job's images are complete and correct, comparing an image against its siblings, and acting on what the review finds. This work is valuable and unavoidable, but the way review tools are often built makes it slower than it needs to be, forcing the reviewer to jump between pages, lose their place, and navigate away to take simple actions.

    This paper makes the case for an in-context image review experience: a direct path from a job or sample to the right image, the ability to move among related images without leaving the review screen, and the relevant actions available where the reviewer already is. It explains why context switching is a hidden tax on expert time, and how reducing it improves both speed and quality. It closes with how Quartz LIMS implements this.

    PDF IconReview Without the Detours

  • 4. Operational Metrics and Turnaround Tracking

    A laboratory that tracks each sample as it moves through the workflow accumulates a valuable byproduct: a precise record of where time is spent, where work is repeated, and who carried the load. Most labs never use this data, because their systems capture status changes for operational purposes but do not turn the underlying timestamps into metrics that management can act on.

    This paper is about closing that gap. It shows how the same granular tracking that gives a lab real-time visibility can be extended into three categories of operational intelligence: turnaround time measured at the stage level, rework measured by frequency and duration, and effort measured and fairly attributed to the people who performed it. Together these answer the questions every lab manager is asked but few can answer with data: where is the bottleneck, why are we redoing work, and is the load balanced across the team.

    The guiding principle is one that operations leaders already accept. If you cannot measure it, you cannot manage it. This paper explains what to measure, how a modern LIMS captures it without adding manual effort, and how Quartz LIMS turns that record into reports and dashboards.

    PDF IconMeasuring the Work

  • 5. Physical Specimen Inventory

    Most laboratory systems organize work around the job sample, the record created when work is requested and retired when the job closes. But in many laboratories the physical object on the bench has a life far longer than any single job. A wafer is received once and examined across many jobs over months. A coupon is cut from it, a smaller piece is cleaved from the coupon, and each of these physical objects may be referenced by analysis after analysis. The job sample is transient; the physical specimen persists.

    This paper makes the case for managing physical specimens as first-class inventory records, distinct from the transient job samples that reference them. It explains why conflating the two loses critical history, how a parent-and-child inventory model preserves lineage as specimens are subdivided, and why registering a specimen once and referencing it many times is the foundation on which a lab's physical-world capabilities rest. It closes with how Quartz LIMS implements this.

    PDF IconThe Specimen Outlives the Job