Production Optimization and Quality Management With AI and SAP

LeverX used AI and SAP BTP to solve critical manufacturing challenges. The implementation resulted in a 90% improvement in defect detection accuracy, and reduced operational costs by up to 15-20%.

Client and Challenge

Our client is a large manufacturing company with a distributed structure, including a head office in Europe, 5 production sites in Asia and North America, and 200 distribution centers worldwide. The company faced significant challenges in managing its logistics network due to:

• Fragmented Logistics Data: Data was scattered across numerous disparate sources, including Excel spreadsheets, decentralized fleet management systems, and third-party services. This made it difficult to obtain a complete and reliable picture.
• Slow Planning and Reporting: Route planning, demand forecasting, and logistics reporting were slow. Preparing consolidated reports could take days or even weeks, rendering data outdated upon receipt.
• Manual and Error-Prone Processes: A significant portion of the planning and analysis work was performed manually, which increased the risk of errors and led to inaccuracies in route planning and resource allocation.
• Inaccurate Demand Forecasting: Slow processes prevented quick reactions to market changes, such as traffic jams or shifts in demand.
• Lack of a Single Source of Truth: The absence of a single data source led to discrepancies and misunderstandings among logistics, sales, and production departments.

Methodology and Approach

Our methodology for this project was centered on a phased approach to reduce implementation risks and avoid a "big-bang" modernization. We began with a thorough audit of the client’s existing systems and a market solution assessment. This analysis confirmed that.

SAP BTP was the ideal platform due to its ability to seamlessly integrate with the client's existing SAP ecosystem, including SAP S/4HANA.

The implementation was structured to directly address the key pain points:

• Data Integration and Unification: Using SAP BTP and SAP Integration Suite, we established a single, unified logistics data source. This consolidated data from disparate systems like SAP S/4HANA Cloud (for inventory and orders), GPS/telemetry data, and third-party APIs into a single source of truth.
• AI-Powered Forecasting and Optimization: We have integrated advanced AI models to drive operational efficiency and enhance our decision-making. Specifically, we leverage predictive AI to analyze historical delivery data, a process that allows us to optimize logistics and determine the most efficient routing. In addition to this, we use generative models to understand and forecast complex consumer demand patterns, thereby enabling more accurate and proactive inventory and resource management.
• Advanced Analytics: We leveraged SAP Analytics Cloud as the core component for business intelligence, planning, and predictive analytics. This provided real-time insights and significantly accelerated analysis and planning processes.
• Scalability and Flexibility: The platform's flexibility and scalability allowed for rapid application development and adaptation as data volumes and business needs grew.

This structured approach allowed the company to transition from reactive to proactive, data-driven logistics management.

Solution

We implemented a comprehensive solution based on AI and SAP BTP to transform their production processes. The core of the solution was to automate workflows, optimize production with advanced analytics, and drastically improve quality control using artificial intelligence.

• Workflow Automation: We used SAP Build Process Automation to streamline and automate key workflows.
• Analytics and Optimization: The solution leverages SAP Analytics Cloud for advanced analytics, helping to optimize production volume.
• AI-Powered Quality Control: The integration of AI with SAP BTP AI Core significantly improved product quality. The system now detects defects with 90% more accuracy than human inspectors.
• Real-Time Data Management: By utilizing SAP Datasphere, we enabled the real-time collection and analysis of large volumes of production data, providing a unified view for quick decision-making.

Technology Stack

The solution is built on a scalable and integrated SAP technology stack:

• Core Platform: SAP BTP.
• Analytics and Planning: SAP Analytics Cloud.
• Database: SAP HANA Cloud.
• Integration: SAP BTP Integration Suite.
• Data Unification: SAP Datasphere.
• Generative AI Models: Predictive and generative AI models integrated via SAP AI Core. 

Results

The implementation delivered significant, measurable business benefits for the client:

• Reduced Operational Costs: Optimization of inefficient routes and improved vehicle utilization led to savings of up to 15-20% on fuel and operational expenses. Empty mileage decreased by 5%.
• Increased Delivery Punctuality: Improved forecasting and planning reduced delivery delays by 10-15%.
• Improved Quality: We achieved a 90% improvement in defect detection accuracy.
• Team Unification: 200 profit/distribution centers with 320 employees were united under corporate plans, working with a single source of truth.