SIMCA® — Multivariate Data Analysis Software
Explore, model, and understand complex bioprocessing data
SIMCA® multivariate data analysis software helps scientists and engineers explore complex process and scientific data using statistical modeling, machine learning, and powerful visualization.
Part of the Umetrics® Data Analytics Ecosystem, SIMCA® software analyzes many related variables together using proven multivariate methods. This helps users reveal patterns and relationships, build interpretable models, identify deviations, and support data-driven decisions across drug development and manufacturing.
The business value of multivariate data analysis
Complex datasets can make important patterns and relationships difficult to identify. SIMCA® software analyzes multiple variables together to reveal relationships that support deeper process understanding, reducing time spent on manual analysis, repeated investigations, and trial and error.
With SIMCA® software, unusual batch behavior, outliers, and sources of variation can be identified earlier. This supports more proactive risk management before process or quality issues affect development, scale-up, or manufacturing outcomes.
Maintaining consistency during scale-up and tech transfer can be challenging when equipment, operating conditions, or sites change. With SIMCA® software, these differences can be visualized and interpreted more clearly, supporting faster investigation and reducing downtime.
Visual outputs in SIMCA® software help create a shared view of process behavior across development, manufacturing, quality, and tech transfer teams. This can make complex process information easier to explain, discuss, and use in cross-functional decision-making.
Multivariate models developed in SIMCA® software can define expected process behavior and be deployed in SIMCA®-online software for real-time monitoring. This helps teams prepare for manufacturing by putting process knowledge into a format that can be used to monitor performance, detect deviations, and support continued process verification.
SIMCA® software helps teams focus investigations on the variables and patterns most likely to affect performance. This can reduce unnecessary analysis, repeated troubleshooting, and inefficient use of expert time across development and manufacturing.
Answer complex bioprocessing questions with multivariate data analysis
How do batches, sites, or operating conditions differ?
Compare batches, scales, sites, formulations, samples, or operating conditions to reveal patterns and differences that are difficult to see one variable at a time.
Which variables are contributing to process deviations?
Identify the variables most strongly associated with an observed difference or deviation to support root cause analysis and faster troubleshooting.
Which factors affect process performance or product quality?
Explore relationships between process parameters, material attributes, and quality outcomes to understand which factors and interactions have the greatest influence.
How is the process likely to perform?
Build interpretable models to predict quality, yield, concentration, process performance, or other outcomes, with results that scientists can explain and investigate.
SIMCA® software at a glance
Applications across the biopharma lifecycle
Explore how SIMCA® software can be applied across process development and commercial manufacturing to understand process behavior, optimize performance, support scale-up, and investigate manufacturing issues.
Multivariate data analysis capabilities
Interactive visualization and multivariate analysis make it easier to explore high-dimensional datasets and gain deeper insight. Review and plot data to identify important correlations and influential variables, and examine individual data points or clusters to understand what contributes to similarities and differences.
Build multivariate models from historical process data to characterize normal behavior, support golden batch modeling, and compare performance across runs. These models can also be used to predict final outcomes, including titer, yield, and quality attributes.
Use multivariate calibration to connect spectral data with reference measurements and build predictive models for analytes and critical quality attributes. These models can then be applied to new spectral data, helping teams estimate key outputs without relying only on routine offline analysis.
Use soft sensor models to predict process outputs that are slow, difficult, or expensive to measure directly. By estimating critical quality attributes or other key results earlier, soft sensors can help teams reduce waiting time, support faster decisions, and improve process monitoring.
Use Python integration to extend data preprocessing and analysis workflows and automate repetitive tasks. This provides additional flexibility for preparing complex datasets and integrating custom analytical steps into SIMCA® software workflows.
SIMCA® software in practice
Build scalable Raman spectroscopy models
Spectroscopy techniques are fast, non-invasive, and suitable for continuous monitoring, but the large volume of data generated cannot be aligned and interpreted effectively using univariate approaches alone. SIMCA® software can be used to correlate Raman spectra with offline measurements and build predictive models for various analytes, improving process control across scales.
Understand raw material variation
Variability in raw materials used in cell culture media and feeds can contribute to variation in product quality, including changes in aggregation, amino acid substitutions, truncation, and glycosylation. Score plots in SIMCA® software can reveal shifts in batch performance associated with differences in raw material properties, helping teams understand and manage the impact of raw material variability.
Troubleshoot tech transfer issues
Maintaining consistency when transferring a process between sites can be challenging. Score contribution plots in SIMCA® software can quickly highlight the parameters driving observed variation, accelerating tech transfer and minimizing downtime.
Evaluate performance of continuous chromatography
Traditional chromatography process monitoring is time-consuming, error-prone, and not well suited to continuous bioprocessing. SIMCA® software can be used to detect small deviations in peak shapes before they are detected by traditional methods, improving consistency between cycles and columns and identifying deviations before they become problematic.
See how SIMCA® software fits into the Umetrics® Data Analytics Ecosystem
SIMCA® software is part of the Build area of the Umetrics® Data Analytics Ecosystem, where teams develop process understanding and create trusted models. Explore how Build connects with Innovate, Deploy, and Elevate to enable better-informed decisions across development and manufacturing.
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Frequently asked questions
SIMCA® software is used for multivariate data analysis — finding patterns, relationships, and models in complex, high-dimensional data. In bioprocessing it turns large process datasets into actionable process understanding.
SIMCA® software supports principal component analysis (PCA), partial least squares (PLS), orthogonal PLS (OPLS), batch modeling, and classification methods, among others.
SIMCA® software is used by process scientists, engineers, and data analysts across biopharma, food, chemical, and academic settings — anyone working to understand complex multivariate data.
SIMCA® software is used to explore data and build models offline. SIMCA®-online software deploys those models for real-time monitoring of live processes, applying the understanding developed in SIMCA® software to manufacturing.
