Predictive Manufacturing

Intelligent systems that are able to predict results: the new frontier of industry 4.0

Take proactive measures today, anticipate and avoid mistakes tomorrow.

Predictive Manufacturing

Predictive Quality Analysis

In industrial processes, which are becoming increasingly complex, the timeliness of intervention has become one of the critical success factors. But when it comes to quality, timeliness is often not enough to avoid losses. What if it is possible to predict the quality of a product before it is produced?

Predict, prevent, fix and improve quality: Divisible combines your experience in production and quality management with advanced predictive analysis and the latest machine learning technologies.

The predictive quality analysis proposed by Divisible is an accurate process of extracting and processing available information from a business process aimed at predicting results, errors and productivity indexes, projecting data and making proactive decisions.

Through the use of machine learning and Artificial Intelligence technologies, and thanks to the huge amount of data available, Divisible provides fast and effective predictive and prescriptive analysis and a continuous flow of new knowledge and insights.

Predictive Manufacturing

Advantages for Companies

Increased product quality

Optimization of quality through machine learning that ensures automated, continuous and real-time analysis, reliable simulations and expected quality alerts.

Reduction of operating and production costs

Quick detection of potential errors and minimisation of scrap, rework and warranty claims by predicting and preventing quality problems.

Resource Optimization

Reduced downtime related to quality, by predicting problems and errors, and identifying their causes.

Our approach

First step

Correlation of pre-existing data sources: company know-how, customer feedback, technical data of repair orders and complaints, reseller communications, indirect information on similar products.

Second step

Advanced analysis of historical and current data

Third step

Development of interactive, intuitive and customizable dashboards for a quick understanding

Fourth step

Training and transfer of necessary skills

Predictive Manufacturing

Predictive Maintenance

Downtime is one of the main inefficiencies in manufacturing, particularly when it occurs due to sudden breakdowns. On the otherhand, over-maintenance is likely to drive up business costs. What ifyou could predict the breaking point of each component to replace it a moment earlier?

Predictive maintenance represents one of the many opportunities of Artificial Intelligence. Through the use of data and advanced analytical methodologies, Divisible offers predictive maintenance tools that can impact your business by reducing downtime and maintenance costs.

Divisible develops and introduces customizable predictive maintenance solutions in business processes, predicting failures and errors before they happen.

Why Predictive Maintenance

Maximisation of plant autonomy and uptime, optimisation of production efficiency, reduction in the number of breakdowns, reduction in overall maintenance costs, reduction in maintenance planning time, optimisation of spare parts purchases and warehouse management, increase in guaranteed safety: these are some of the main benefits of using predictive maintenance tools.

The higher the cost of the expected error, the greater the benefit obtained.

Necessary requirements:

• Detailed list of errors and process anomalies: date, time, type, cause (if known), consequence.

• Detection and collection of data andprocess/plant variables: physical measurements such as temperature, pressure, rotation speed, voltage and other (in case of lack of the necessary information Divisible identifies sensors to be integratedand connected).

• Collection of data and indirect process parameters such as raw materials, personnel, suppliers.

Predictive Manufacturing

Implementation of Predictive Maintenance of Divisible

Step 1

Analysis and evaluation of the available process data and the sensors used.

Step 2

Identification and knowledge of parameters that represent possible errors or faults.

Step 3

Detection, collection and processing of anomalies.

Through the advanced analysis of Divisible, the data collected allow to define precise rules for each process, plant or activity ensuring the immediate and reliable identification of even minimal variations.

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