Must be data-driven.

Optimize resources and increase your competitiveness

Facing the intrinsic variability of the agri-food sector and its processes is a challenge for quality improvement, and that is why Síagro was developed.

With data, we can control processes and make decisions to optimize them. And thus, boost productivity and profitability.

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Statistical program with a user-friendly interface to analyze data instantly

Identify deviations in time and prioritize actions.
Detect your strengths and weaknesses.
Reduce waste and losses.
Monitor and standardize processes.
Apply continuous improvement and motivate the team, it is the basis of any company’s success.
Use evidence as a sales pitch.
Make data-driven decisions.

Statistics had never been this easy

One dashboard

Basic and advanced analytic models

Reports in real-time

Statistical Process Control

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.

Detect special causes of variation.
Control and specification limits.
Continuous hypothesis testing to check if the process is under control.
Know the cost of each error to act first where the greater losses are being generated.
Determine the qualitative and quantitative quality characteristics.
Control of proportions, populations, events within the same process, …
Monitoring and standarization.

Exploratory Data Analysis

The first step to reducing 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, helping us in the following analysis and to have the first conclusions.

Explore the data distribution.
Identify outliers, discontinuities, concentrations of variables, relations between variables, …
Population studies, randomization, …
Detect relations among different variables.

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?

To improve quality, it is critical to detect problems early and solve them before they have a direct impact on productivity.

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

Data mining techniques.
Reduction of dimensionality.
Predicts performance of either individuals or groups.
Probabilities of occurrence.
Classify the information considering the essential.
Clústering methods.

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.

Determine the factors that have the greatest influence on your results.
Establish dependency relations between variables.
Find out if something you want to occur or not may happen.
The probability that several events will occur in a fixed interval of time and/or space if these events occur each a constant time and independently of the time elapsed since the last event.
Analysis of variance (a measure that represents the variability) and its evaluation, for example, the effect of a treatment on the variability of the response variable.