Predictive Analytics uses data to better predict future process performance.
Symbol helps organisations to analyse process data, develop predictive models, and translate insights into proactive management.
Predictive Analytics uses historical and current data to identify patterns and predict future process performance. Where traditional analyses primarily show what has happened, Predictive Analytics helps to see what is likely to happen sooner.
Using statistical models and machine learning, organisations can identify trends, deviations, and risks at an early stage. This makes it possible not only to improve retrospectively but also to proactively manage quality, performance, and reliability.
Within CIMM Level 4, Predictive Analytics supports organisations in further improving capable processes. Data is used to make better predictions, reduce risks, and strengthen decision-making.
The following four analysis techniques are widely applied within Predictive Analytics. Symbol uses Minitab, among others, for this purpose. By applying these techniques, complex patterns are discovered and future performance can be predicted.
These techniques are covered in the Six Sigma Black Belt training or more extensively in the Predictive Analytics masterclass.
CART uses a single decision tree to divide data into homogeneous groups step-by-step. The model predicts categories or values and provides understandable decision rules, making causes, risks, and segments visible.
Random Forest combines many randomly constructed decision trees into a single robust prediction. The model processes complex relationships, limits overfitting, and predicts accurately, but is less easily explainable to users than CART.
TreeNet builds sequential decision trees that each correct previous prediction errors. This boosting technique discovers subtle, non-linear patterns and delivers high accuracy, provided that settings, validation, and monitoring effectively manage overfitting during practical use.
MARS models continuous outcomes with linked line segments and automatically determined hinge points. The technique recognises non-linear relationships, threshold effects, and interactions, and generally remains more explainable than complex ensemble models for operational decision-making.
Predictive Analytics techniques such as CART, Random Forest, TreeNet, and MARS help organisations discover complex patterns, interactions, and non-linear relationships in process data. This allows them to predict future performance, failures, quality issues, and risks before they actually occur. The models support faster, better-informed decisions and enable targeted preventive measures.
A Symbol Master Black Belt or data analyst starts with an inventory in which the quality of the data is assessed. A pilot is then launched to demonstrate the added value of Predictive Analytics.
Within CIMM Level 4, these techniques are applied in data-driven breakthrough projects, where complex process problems are addressed in a structured manner and predictions demonstrably contribute to more stable, better, and more predictable process performance and sustainably secured operational decision-making within the organisation.
Symbol helps organisations to transform process data into predictive insights that contribute to better performance and proactive decision-making.