Overview

Beamex CMX Analytics Dashboard is designed to manage and optimize calibration activities efficiently. It provides a comprehensive solution for planning, managing, analyzing, and documenting all calibration work and assets. The software is particularly suited for highly regulated industries, ensuring compliance with stringent standards and regulations.

Features

Visual summary of calibration KPI’s The Beamex CMX Analytics Dashboard provides an easy-to-use visual summary of your key calibration performance indicators such as the percentage of calibrations failed or performed after the due date, reference standards usage, and calibrator reverse traceability. Overview of your team’s workload Get an overview of your team’s workload over time to better predict workload during a given month or week – and act accordingly together with technicians to address any issues. This will help you use resources more efficiently, save costs, and improve productivity. Advanced history trend Enable preventive maintenance based on the output of the dashboard and see the device manufacturer and model maintenance history over time. With history trending based on device manufacturer or model, you can see if a given sensor type performs better or worse than another model. Calibration history analysis Get predictions of drift based on a forecast of the last calibrations performed. If the forecast is good and the trend is well within out-of-tolerance limits. For example, you can extend the calibration interval from 6 to 12 months – saving time and money. Impact of uncertainty on calibrations The uncertainty page allows visualizing the impact of uncertainty on calibrations. For example, a calibration point may be passed but when taking the uncertainty in account then there is a risk that it is failed. It also helps you to ensure that your reference standard is accurate enough for the calibration, taking the test uncertainty ratio TUR into account. Predict the risk of failed calibrations Analyze the historical behavior of instruments and predict the risk of failed calibrations.