Lab QM

Overview

Lab QM is a component within the SABLE® solution, purpose-built to support the quality control (QC) of assay results in data acquisition workflows. Serving as the central point for laboratory data reception and validation, Lab QM integrates seamlessly with various external data sources, including laboratory results, planned and surveyed coordinates, downhole survey data, diglines, blast patterns, and mining observations.

The module provides a standardised ETL (Extract, Transform, Load) data warehouse framework, along with customised, user-friendly tools for importing and managing laboratory and field data. Lab QM ensures the accuracy and consistency of assay results, enforces industry-standard QA/QC protocols, and enables users to accept or reject laboratory data with confidence.

Key Features

Assay Results Import

  • Simplified process for importing assay results.
  • Supports batch imports to streamline large datasets.
  • Compatible with a wide range of external formats and data sources.

QA/QC Clearing Views

  • Provides clear views to support audit and validation of results.
  • Highlights mismatches between assay data and sample records.
  • Supports unique sample ID checks and traceability.

Statistical Analysis and Reporting

  • Built-in tools to perform statistical checks on assay data.
  • Enables visualisation of trends and anomalies to inform decisions.
  • Generates standardised QA/QC reports for compliance and documentation purposes.

Why Lab QM Is Needed

In exploration and mining operations, samples are routinely collected for purposes such as exploration, resource modelling, grade control, and metallurgical testwork. These samples are sent to laboratories for analysis, where variations in practices and processes can introduce inconsistencies.

Lab QM provides a structured, digital interface between the laboratory and the SABLE database, supporting analytical QA/QC and ensuring that returned results are matched to known samples. The module ensures results are loaded into active branches only when they meet project and industry standards.

Lab QM ensures that assay results are only matched against existing samples in the SABLE database, maintaining the integrity of the sample inventory.

Current Industry Practices and Challenges

The operational practices around sample collection, tracking, preparation, and analysis differ widely between projects and organisations. While these broad categories remain consistent—sampling, logging, transporting, and laboratory analysis—the execution often depends heavily on human actions, which introduces the potential for inconsistency and error.

For example, a sampler operating a diamond saw underground, a technician handwriting a ticket ID on a manilla tag, or a geologist manually maintaining a sample register in Excel (or on paper) are all common scenarios. Even transporting samples to the laboratory is typically a manual task, and each of these steps relies on the attention to detail and consistency of the individual involved.

This human dependency means that despite the best intentions, variability can creep in—whether through mislabelling, data transcription errors, or misplaced samples.

However, there are several critical points in the workflow where a computerised system can significantly reduce the likelihood of human error. Lab QM addresses this by embedding checks, validations, and auditability into the system, reducing the risk of discrepancies, and improving confidence in the integrity of the sample data throughout the lifecycle of a mining or exploration project.

Lab QM Value

Lab QM brings tangible value to projects by:

  • Performing QA/QC on assay results.
  • Performing sample ID checks between assay results and samples stored in active branches to detect mismatches early.
  • Allowing controlled acceptance or rejection of assay data.
  • Providing analytical tools for data-driven decision-making.
  • Supporting traceability, auditability, and reporting in line with regulatory and operational expectations.

Client Feedback

“SatQC helps with the visual indication of how tested standard values differ from the expected value for the standard.” Senior Sampler, Marula, 2025