As an expert in industrial quality, Ellistat is enhancing its DATA Analysis module with new artificial intelligence capabilities. In addition to machine learning and classification, the integration of neural networks makes it possible to model more complex industrial behaviors. With this software update, Ellistat is opting for an applied, gradual, and controlled approach to AI.
\nUnderstanding the causes of discrepancies and taking action on the right metrics is no longer just an intention\u2014it is now a concrete approach.<\/p>\n
For Ellistat, artificial intelligence is only valuable if it actually helps manufacturers
\nto produce better results. And to do that, it must first be accessible to everyone. Quality depends on
\nbased on a simple principle: mastering one's processes, that is, understanding the parameters
\nfactors that affect production\u2014temperature, tool, material, machine, operator,
\nsupplier, configuration, environment. But identifying these factors usually requires
\nstatistical skills that not everyone has, and that few workshops
\nhave time to mobilize.<\/p>\n
When a deviation, discrepancy, or variation occurs, identifying the cause often involves
\nabout the investigation: we proceed based on intuition, testing one parameter after another, and time
\nTime spent searching results in waste, delays, and adjustments that are more like
\nexperience rather than evidence.<\/p>\n
That's where DATA Analysis comes in: the software handles the complex part\u2014the
\ncalculations, models, and statistical methods\u2014and presents it through an interface
\nSimple. You don't need to be a data science expert to analyze your data and identify the
\ninfluential variables, understanding the causes of variations, and building models
\nthat can be used in the field. This gives everyone access to analytical capabilities that were previously
\nintended for specialists only.<\/p>\n
This accessibility also defines the software's target audience. DATA Analysis is not limited to
\nexperts in statistics or artificial intelligence: it is primarily intended for those who
\nare familiar with the process\u2014process engineers, production engineers, and quality engineers. They are the ones who
\nhave the on-the-ground knowledge, and it is for them that the tool must provide the analysis
\naccessible.<\/p>\n
The new AI features follow this logic exactly: they add
\npower without sacrificing ease of use. For a non-expert user,
\nThe approach remains guided, step by step. For an expert profile, the software offers more options
\nin-depth, with advanced settings for model structure and functions
\nactivation: the user can configure a neural network, choose a training mode,
\nadjust how the model learns from the data or add neurons
\ndepending on the desired level of analysis. The same interface thus adapts to the level of
\neach one, from rapid diagnosis to detailed modeling.<\/p>\n
\u00abIndustrial AI shouldn't be just another black box on the factory floor. It should help the
\nteams to understand what's really happening in their processes. With DATA
\nAnalysis: We want to provide manufacturers with more powerful models, but
\nuseful to those who know the field. That's how AI becomes useful:
\n\u00bbwhen it informs production decisions,\" explains Davy Pillet, CEO of Ellistat.<\/p>\n
The strength of DATA Analysis lies in the wide range of methods it brings together in a single tool. At the
\nGenerally speaking, traditional statistics answer the most common questions in the workshop:<\/p>\n
compare two variables, test whether a change has a real effect, measure a correlation,
\nbuild a regression model or a design of experiments. Easy to implement, these
\nThese methods remain essential and form the foundation of a quality approach
\nrigorous.<\/p>\n
The new version significantly expands this range. DATA Analysis includes
\nnow includes machine learning models, neural networks, and
\nunsupervised classification. Whereas classical statistics describe simple relationships
\nUsing a few variables, these models address phenomena in which many
\nThe parameters interact: they determine the function that links the production conditions to the
\nthe results obtained, and reveal patterns or behaviors that the eye
\nHumans don't distinguish between them.<\/p>\n
This gives users a comprehensive range of options: they can choose the level of analysis that best suits
\nhis question\u2014from the two-variable test to the predictive model\u2014without ever straying from the same
\nenvironment. The goal remains the same at every level: to reduce analysis time,
\nimprove the reliability of diagnoses and focus efforts on the right levers.<\/p>\n
DATA Analysis is part of the Ellistat Quality Suite, a fully web-based software suite dedicated to
\nindustrial-grade quality. The module truly shines when it leverages data from
\nother software components: using SPC to track the statistical trends of their
\nprocesses and monitor production in real time and perform incoming quality control (IQC) to compare suppliers,
\nanalyze the quality of incoming shipments or track changes in supplier performance
\nback then.<\/p>\n
This complementarity ensures consistency across the entire Quality Suite: collecting
\ncollect data, monitor it, analyze it, and then make decisions based on facts.<\/p>\n
With this evolution of DATA Analysis, Ellistat advocates a pragmatic approach to
\nartificial intelligence. No promises of autonomous factories. No out-of-touch rhetoric
\nproduction realities. AI must be based on reliable data and processes
\nclear and measurable objectives.<\/p>\n
Ellistat has chosen an AI solution that helps manufacturers better analyze, better understand, and
\nbetter manage their processes.<\/p>","protected":false},"excerpt":{"rendered":"
Ellistat fait \u00e9voluer DATA Analysis avec l’IA pour mieux ma\u00eetriser les proc\u00e9d\u00e9s industriels Expert de la qualit\u00e9 industrielle, Ellistat enrichit son module DATA Analysis de nouvelles capacit\u00e9s d’intelligence artificielle. \u00c0 c\u00f4t\u00e9 du machinelearning et de la classification, l’int\u00e9gration de r\u00e9seaux de neurones permet demod\u00e9liser des comportements industriels plus complexes. Avec cette \u00e9volution du logiciel, Ellistat […]<\/p>\n","protected":false},"author":4,"featured_media":4421,"comment_status":"","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[7],"tags":[],"class_list":["post-4422","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-analysis"],"acf":[],"yoast_head":"\n