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Reducing the Cost of Poor Quality (CoPQ) in Manufacturing with SAP BTP and AI

Business Challenge: Overcoming Quality and Cost Inefficiencies

Manufacturing organizations struggle to manage the high costs associated with quality failures. Common challenges include:
  • Difficulty identifying quality issues in real time due to siloed data across multiple systems.
  • Lack of predictive insights to preemptively address potential quality issues.
  • Increased operational costs and reduced profitability stemming from inefficient quality control processes.
Limited access to actionable, consolidated insights hindering data-driven decision-making.

The Vision: A Seamless, Intelligent Quality Control Process

SAP Business Technology Platform (BTP), integrated with advanced AI capabilities, provides a transformative approach to reducing CoPQ. By creating a boundaryless and automated quality assurance process, SAP BTP enables manufacturing organizations to achieve:
  • Real-time insights into production and quality metrics.
  • Predictive analytics to forecast potential failures and optimize corrective actions.
Streamlined data integration across MES, IoT/PLC systems, ERP, and quality management systems.

Solution Overview

Leveraging SAP BTP, the solution seamlessly connects applications and systems, ensuring efficient data flow and actionable insights. The key components include:
  • SAP Analytics Cloud (SAC):Provides real-time dashboards and analytics for quality insights.
  • SAP Work Zone:Offers an intuitive, unified interface for quality managers and operational staff.
  • Generative AI Capabilities:Supports natural language interaction, predictive quality analytics, and actionable recommendations.
Integration Suite: Ensures secure and bi-directional communication across systems like IoT devices, QMS, ERP, and LIMS.

Key Steps in the Solution Framework

  1. Data Ingestion & Processing:Collect and preprocess production, quality, and maintenance data from diverse systems.
  2. AI-Driven Insights:Utilize predictive models to identify potential defects, failure likelihoods, and improvement opportunities.
  3. Interactive Dashboard:Provide role-specific views with recommendations and alerts.
  4. Implementation of Actions: Enable immediate corrective and preventive actions (CAPA) based on AI-driven insights.

Impact Highlights

  • Reduction in Quality Costs:Lower scrap, rework, and warranty claims by identifying issues early.
  • Increased Operational Efficiency:Real-time access to insights accelerates decision-making.
  • Enhanced Productivity:Predictive analytics reduces downtime and optimizes maintenance schedules.
  • Improved Profit Margins:Reduced CoPQ leads to increased profitability and customer satisfaction.

Future Outlook: Continuous Learning and Improvement

The solution evolves with generative AI’s natural language capabilities, learning from historical data to refine recommendations and improve operational performance