Home Research and Innovation Projects PPC4.0 - Production Planning Control 4.0
PPC4.0 - Production Planning Control 4.0
Date: 2020-2023

Software industry, ICT and media
Digital transformation in manufacturing and process industry


Research and Development under Contract


IT Engineering - Process, Data, Maturity, and Quality


Artificial intelligence, machine learning, and decision support systems



The PPC4.0 project aims to investigate and develop tools to support the planning and control activity of industrial production integrated into a Manufacturing Execution System (MES).



New technologies and tools linked to Industry 4.0, capable of presenting
superior levels of autonomy and performance, making
more effective production planning and control processes. They must also work in an integrated manner with existing ERP and IoT systems.


Creation of tools taking into account the potential of Industry 4.0 technological paradigms, namely through the application of data science approaches, such as applied optimization, machine learning or genetic algorithms, and the growing volumes of data coming from industry, most of which is collected by “Internet of Things” systems (IoT)​​​​



This solution aims to achieve the following specific objectives:

  • Creation of a PPC tool that allows you to automatically suggest optimized production plans
  • Development of an artificial intelligence and information visualization module that complements the automatic planning and production optimization module


CCG/ZGDV contributes to this project, through the I&I department, EPMQ, in research and
development of artificial intelligence and data science modules that integrate the PPC4.0 solution. 





The solution requires implementing tools capable of integrating and processing large volumes of data from different sources to collect information. It also allows you to automate the choice and parameterization for automatic operation planning, integrated with existing ERP and IoT systems.

Through AI algorithms to make forecasts, with data collection and processing (data warehouse) and an API to integrate the data warehouse with external modules, and an AI module to help forecast planning: article yield, occurrence of defects in the looms, raw material forecast, ironing and mending, among other information to support decision-making.