Make data-driven decisions applying successful strategies and methodologies for control and continuous improvement of processes

Síagro includes the most advanced analytical models for quality improvement

control de calidad

Statistical Process Control, SPC

Making changes? Want to evidence their impact and measure improvement?

Variation itself is neither good nor bad. It is something natural of life. The aim of the Statistical Process Control is to keep the process under control, understand its performance and make decisions. 

Control charts


Principal Component Analysis, PCA

Statistical Process Control, SPC

Making changes? Want to evidence their impact and measure improvement?

Variation itself is neither good nor bad. It is something natural of life. The aim of the Statistical Process Control is to keep the process under control, understand its performance and make decisions. 

Exploratory Data Analysis, EDA

El primer paso para reducir la variabilidad es entender cómo son los datos.

The first step to reduce variability is to understand how the data is. This is where Exploratory Analysis comes in, providing simplicity with the use of simple graphics that include relevant information about the data and its characteristics, helping us to make decisions.

Skim


Balance


Pairs


Histograms


Boxplots


Sequential charts


Normality test


Exploratory Data Analysis, EDA

El primer paso para reducir la variabilidad es entender cómo son los datos.

The first step to reduce variability is to understand how the data is. This is where Exploratory Analysis comes in, providing simplicity with the use of simple graphics that include relevant information about the data and its characteristics, helping us to make decisions.

Pattern Discovery

Let us forget for a moment technical concepts and the most common definitions of control, monitorization and improvement, and ask ourselves… What is the basis of quality management? Isn’t it detecting problems early and fixing them before they start to have a direct impact on productivity?

This type of analysis focuses on managing the processes to detect recurring and relevant events, the known “patterns”, before they become major problems.

More proactivity and less reactivity.

Principal Component Analysis, PCA


Clustering techniques:

∴ Hierarchical

∴ K-means

Pattern Discovery

Let us forget for a moment technical concepts and the most common definitions of control, monitorization and improvement, and ask ourselves… What is the basis of quality management? Isn’t it detecting problems early and fixing them before they start to have a direct impact on productivity?

This type of analysis focuses on managing the processes to detect recurring and relevant events, the known “patterns”, before they become major problems.

More proactivity and less reactivity.

Prediction Models

Among the many ways in which statistics help us to improve, one of the most demanded by companies is to predict what will happen in the future to reduce costs, increase profits, and detect market trends.

Predictive models help to infer the probability that certain situations will occur before they happen and to deduce future results. These data analysis methods make it possible to have a better understanding of the information we collect at our facilities, predict performance and failures, and respond in time.

Lineal


Logistic


Poisson


ANOVA

Prediction Models

Among the many ways in which statistics help us to improve, one of the most demanded by companies is to predict what will happen in the future to reduce costs, increase profits, and detect market trends.

Predictive models help to infer the probability that certain situations will occur before they happen and to deduce future results. These data analysis methods make it possible to have a better understanding of the information we collect at our facilities, predict performance and failures, and respond in time.

Thanks to its modular architecture, you can expand and customize the functionalities according to your needs, incorporating new components and maintaining the simplicity that characterizes Síagro

control de calidad

Thanks to its modular architecture, you can expand and customize the functionalities according to your needs, incorporating new components and maintaining the simplicity that characterizes Síagro.

Measure what can be measured, and make measurable what cannot be measured

Galileo Galilei

Start managing your data and making decisions based on evidence

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