KPIs for Laboratory Management in Animal Nutrition: Operational, Analytical, and Regulatory Indicators

KPI stands for Key Performance Indicator. The concept is simple: a KPI is a metric tied to a specific decision or objective, monitored frequently enough that deviations are detected before they become problems. The distinction between a generic metric and a useful KPI is exactly this: the KPI answers a question that matters to someone who needs to act. "How many analyses were done this month" is a metric. "What percentage of receiving analyses were delivered within the deadline set for batch release" is a KPI — because when it falls below target, there is a specific action to take.

In animal nutrition quality-control laboratories and in analytical service laboratories, relevant KPIs are distributed across four dimensions: operational efficiency, analytical quality, regulatory compliance, and supplier performance. This article presents the specific indicators in each of these dimensions, differentiating when applicable between the context of an internal feed-mill laboratory and a service laboratory serving external customers.

Operational efficiency KPIs

Laboratory operational efficiency determines whether it sustains production-process speed or becomes a bottleneck. For an internal feed-mill laboratory, the time between receiving-sample collection and lot-release decision is the most critical operational KPI, because it is the time the truck is stopped at the scale waiting.

Turnaround time (TAT) by analysis type

TAT is the interval between sample check-in at the laboratory and result availability. The most common mistake in defining this KPI is treating it as a single value for all analysis types. In practice, each method has its own expected turnaround profile, and the KPI must be defined per analysis or per analysis group.

For NIRS screening at corn or soybean-meal receiving, expected TAT is under 15 minutes, because the lot is waiting for unloading decision. For Kjeldahl crude protein, expected turnaround is 3 to 4 hours in the same receiving shift. For aflatoxin ELISA, typical commercial-kit time is 90 to 120 minutes. For analyses sent to external labs, deadline is the one contracted with the partner laboratory, and the KPI monitors provider compliance with that deadline, not internal-lab execution.

This KPI is calculated as the percentage of analyses delivered within established category TAT for the period (daily or weekly for receiving analyses). A drop in this indicator for receiving analyses signals capacity overload, problem with a specific instrument, or unexpected increase in receiving volume. Each cause requires a different response, and the KPI makes it visible that something must be investigated.

Lot release time

Unlike individual-analysis TAT, lot release time is the interval between lot arrival at the scale and issuance of formal release or rejection decision. This KPI integrates all partial times, sample collection, analysis, result interpretation, and communication to operations, and is what production teams effectively see as laboratory performance.

For lots of critical ingredients with associated CCP (soybean meal for PDI and urease activity, fish meal for Salmonella, corn for mycotoxins), this release time directly impacts production planning. A reasonable target for releasing lots with internal screening analysis is four to six hours. For lots that depend on external analysis with one- to two-day turnaround, release process must include conditional-use or quarantine decisions while result is pending.

Sample volume per analyst and utilization rate

Productivity per analyst, number of analyses completed per shift or day, is a capacity-sizing KPI, not an individual performance evaluation. When monitored together with pending-sample backlog, managers can identify when analytical capacity is systematically below operational demand and needs reinforcement (hiring, overtime, peak outsourcing), versus when there is a temporary peak related to harvest arrival or high receiving season.

For service laboratories, volume per analyst has a second dimension: cost per analysis, calculated as total personnel and reagent cost divided by produced volume. This indicator informs service pricing and identification of analyses with margins that do not cover real execution cost.

Critical-equipment availability

Equipment uptime, percentage of scheduled working time in which critical instruments are operational, is an infrastructure KPI with direct impact on TAT and capacity. A Kjeldahl digestion block stopped for maintenance, an NIRS detector with expired calibration, or a PCR thermocycler out of operation creates a bottleneck that no increase in team productivity can solve.

Availability target for critical line instruments (NIRS, Kjeldahl, moisture analyzers) should be above 95% during useful working period. Drops below this level must be recorded with identified cause, equipment failure, scheduled preventive maintenance, or reagent shortage, so occurrence patterns can inform preventive-maintenance planning and critical-supply inventory management.

Analytical quality KPIs

Laboratory analytical quality determines whether generated results are reliable enough to support decisions that depend on them. Analytical-quality KPIs answer the question: is the laboratory producing precise and reproducible results?

Rework and reanalysis rate

Percentage of analyses that had to be repeated, due to technical failure, result outside internal quality-control acceptance range, or justified challenge to the result, is a sensitive indicator of laboratory analytical health. A reanalysis rate consistently above 5-8% per method deserves investigation: it may indicate outdated equipment calibration, insufficient reagent quality, inadequate sample preparation, or training gap for a specific analyst.

This KPI must be segmented by method and analyst to be diagnostic. A general 10% rate can hide that 90% of reanalyses are concentrated in a single method on the night shift, information that points to a specific and addressable cause.

Internal quality-control (IQC) compliance

Periodic analysis of a certified reference material (CRM) or internal control sample with known value is the central mechanism for verifying analytical quality. The KPI is calculated as percentage of IQC analyses whose results fall within defined acceptance limits (typically +/-2s relative to reference value).

For laboratories accredited under ABNT NBR ISO/IEC 17025, this indicator is not optional, it is a normative requirement to demonstrate statistical control of analytical process. IQC compliance below 95% per method, per period, signals analytical drift that must be investigated before continuing to report results for that parameter. Shewhart charts built with IQC results are the visual representation of this KPI over time.

NIRS confirmation rate versus reference method

In laboratories that use NIRS as a screening method, percentage of NIRS results that required confirmation by reference method (Kjeldahl, Soxhlet) informs two distinct aspects. If confirmation rate is too high, NIRS calibration may be outdated or calibration-sample bank may not represent current variability of received ingredients, reducing value of rapid screening. If rate is too low (near zero), it may indicate confirmations are being omitted even when result is close to specification limit, which is an analytical risk.

A confirmation rate between 10% and 20%, corresponding to lots close to limits or with divergence between NIRS and supplier report, is typically the expected profile in a laboratory that properly operates screening protocol.

Regulatory compliance and HACCP KPIs

For laboratories in feed mills registered with MAPA, regulatory KPIs answer the question: is the self-control program being executed with established frequency and scope?

SCP analytical coverage

The Self-Control Program defines which analyses must be performed, at what frequency, and at which process points. SCP coverage KPI is the percentage of planned analyses effectively performed in the period. Coverage below 100% for CCP analyses, such as mycotoxin screening in corn, which is a Critical Control Point in many feed mills, is a nonconformity that must be documented with justification and corrective action before any inspection.

This KPI must be monitored at least monthly and compared with approved SCP plan. Real-time coverage management, with alerts when a mandatory analysis is near due date without execution, is a feature that differentiates laboratory management systems from simple control spreadsheets.

Nonconformity rate per CCP

Percentage of Critical Control Point analyses that generated results outside critical limit, and therefore triggered corresponding corrective action, is a process-risk KPI. Historically high values for a specific CCP indicate that associated risk is real and recurring, and may justify reviewing HACCP plan or increasing monitoring frequency.

For aflatoxin CCP at corn receiving, for example, a nonconformity rate that grows consistently throughout harvest signals deterioration in purchase-origin quality, information that must reach buyer and formulator, not only remain recorded in the laboratory.

Supplier performance KPIs

The connection between laboratory analytical data and supplier management is one of quality control's highest strategic-value points, and supplier KPIs are the instrument that makes this connection visible and measurable.

Compliance rate per supplier

Percentage of lots from each supplier that met all receiving-analysis specification criteria is the most immediate supply-quality indicator. Calculated monthly and compared among suppliers of the same ingredient, it directly reveals which purchasing sources are more reliable, information that should feed every contract negotiation.

Criticality-based classification makes this KPI more actionable: suppliers with compliance rate above 95% deserve differentiated treatment (reduced analysis frequency, preferred negotiation conditions); suppliers between 80% and 95% require intensified monitoring; suppliers below 80% for consecutive periods should trigger formal qualification or suspension process.

Coefficient of variation per critical parameter per supplier

As detailed in article on supplier qualification with laboratory analyses, CV of a critical parameter, crude protein, ether extract, moisture, calculated over latest N lots from each supplier, is a formulation-risk metric. Two suppliers with identical average but CVs of 1.5% and 3.5% impose very different safety-margin costs in formulation. Monitoring supplier CV over time allows identifying when a historically consistent supplier begins showing increasing variability, a signal of changes in origin, process, or material quality.

Divergence between supplier report and internal analysis

Average delta between value declared in supplier's certificate of analysis and value measured internally at receiving is an indicator of supplier reliability. Systematic divergences, supplier declares 46% protein and internal analysis consistently measures 45.3%, indicate methodology or sampling problems, or in more serious cases, declaratory adulteration. This KPI, monitored by supplier and parameter, justifies receiving analyses regardless of incoming report: not to duplicate analytical effort, but to verify whether supplier declaration is reliable enough to reduce analysis frequency in the future.

Specific KPIs for service laboratories

Analytical service laboratories that serve feed mills, integrators, and other external clients have, beyond internal KPIs described above, a set of indicators oriented to service performance as perceived by the customer.

On-time delivery rate per contract

Percentage of analyses delivered within TAT contracted with each client is the main service-quality KPI for an external laboratory. Unlike internal TAT, which measures process efficiency, this KPI measures commercial-commitment fulfillment, and it is the indicator clients use to evaluate the laboratory at contract renewal. On-time delivery rate below 95% per client is a warning sign that the laboratory must address proactively before clients begin evaluating alternatives.

Dispute and reinvestigation rate

Percentage of results challenged by clients that required formal reinvestigation, whether due to discrepancy with client's internal analysis or unexpected result that raised methodology doubts, is a perceived-quality indicator. Rates above 1-2% per method deserve investigation: they may indicate sample-traceability problems, methodological differences between laboratory and client, or real analytical-quality issues that only become visible when compared with external results.

Proposal conversion rate and client retention

For the commercial dimension of service laboratories, KPIs for proposal conversion (how many sent proposals become service orders) and active-client retention (percentage of clients with a contract in the prior period that renew in the current period) are long-term commercial health indicators. As explored in the article on commercial-proposal management in laboratories, conversion rates persistently below 30% generally indicate a problem in the proposal process or commercial follow-up, not necessarily in price.

How to set targets that guide rather than distort

A real risk in using KPIs in laboratories is setting targets that create perverse incentives. An overly aggressive TAT target for Kjeldahl analyses may induce analysts to end digestion before the complete time to meet the deadline, compromising precision in the name of speed. An IQC compliance target of 100% may induce omission of out-of-control records instead of root-cause investigation.

Useful laboratory KPI targets come from three sources, never from generic sector benchmarks. The first source is real operational requirements: how long can the production line wait for lot release? The second is regulatory requirements: which analyses must be performed and at what frequency for SCP compliance? The third is the laboratory's own historical performance: what was average TAT over the last six months, and which target represents an achievable improvement?

Monitoring frequency must also be calibrated to the KPI. Lot release TAT must be monitored daily or by shift because it impacts immediate operations. IQC compliance can be monitored weekly. Supplier compliance rate is a monthly or quarterly KPI. Monitoring all KPIs at the same frequency creates noise and diverts attention from what needs it at a given moment.

KPIs only exist if data exists

All laboratory KPI logic presupposes a condition that is frequently not guaranteed: that data required to calculate each indicator is recorded in a structured, consistent, and accessible way. Analysis TAT requires recording sample check-in time and result issuance time, information that in paper-based systems simply does not exist in a recoverable form. Reanalysis rate requires every reanalysis to be recorded as such with identified cause. SCP coverage requires the plan to be registered and every performed analysis to be linked to it.

The laboratory management system is the infrastructure that makes KPIs possible, not because dashboards are modern, but because the KPIs that truly matter for managing an animal-nutrition laboratory cannot be reliably calculated with data scattered across notebooks, individual spreadsheets, and emails. Data must be in the same place, with the same metadata, produced consistently over time. When it is, KPIs become the language the laboratory uses to communicate with production, procurement, formulation, and management, rather than a monthly report nobody reads.