Zero Defects Project for Sonae Arauco


Zero Defects Project for Sonae Arauco

Built to Heal

The product was e-launched by Karnataka’s Health Minister along with other bureaucrats.

Data-driven Decision Support System that improves manufacturing decision making with predictive approach to ensure consistent level of product quality (towards zero defects), while generating higher production efficiency and cost-efficient product development.

From an IoT perspective the most critical points to be Managed are:

  • Connectivity between all the core machines involved in the process;
  • Control over the appropriate communications paths and scheduling;
  • Monitor Data Orchestration to improve recipes;
  • Operational KPIs to monitor the quality of the Product.


Sonae Arauco first implemented the connected factory project, being the foundation for the Zero Defects project, providing:

  • Automation & sensorization for collecting real-time data from shop floor processes;
  • Integration with legacy systems & data silos;
  • Increase of technology & equipment digital convergency;
  • Definition & roadmap for an Industrial Architecture, integrating IT & OT assets & capacities.


Ensure consistent high level of product quality is a challenging goal:

  • higher quality standards from clients;
    product combinations are multiplying, introducing additional complexity;
    higher propensity to defects that generate wastage of resources.

Can we use manufacturing data and artificial intelligence to anticipate defects, save resources and empower the shop-floor staff?

  • A ZDM challenge is automating the analysis of vast amounts of data which requires the adoption of decision-making systems (Wang 2013).



Manufacturing process and production flow analysis:

  • process key parameters impacting quality;
    defect categorization.

Connect & collect data, integrate different systems & data sources (IT & OT), adapt interoperability.

Data-driven Decision Support System to:

  • train predictive models to map production;
  • analyze production orders;
  • check raw materials conditions;
  • predict defect probability per production order
  • signal quality issues;
  • recommend concrete actions to the staff.


  • Reduce production costs through defect prevention and the optimal use of resources;
  • Improve product quality through the reduction of defects
  • Sustainable use of resources, such as, wood, chemicals, energy and time;
  • Help shop-floor staff through better decision making;
  • Efficient production through smart recommendations;


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