AI-Powered Production Optimization with SAP BTP

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 global manufacturing company with a distributed operating model: a head office in Europe, five production sites across Asia and North America, and 200 distribution centers worldwide.

The scale and complexity of the organization created several challenges across production, logistics, and planning:

• Fragmented logistics and production data. Critical information was distributed across Excel spreadsheets, local fleet management systems, SAP solutions, and third-party services. As a result, teams lacked a unified and reliable view of operations.
• Slow planning and reporting. Route planning, demand forecasting, and consolidated reporting relied heavily on manual processes and could take days or even weeks. By the time reports were available, some of the data was already outdated.
• Manual and error-prone processes. Planning, analysis, and resource allocation required significant manual effort, increasing the risk of errors and inconsistencies.
• Limited forecasting accuracy. Existing processes made it difficult to respond quickly to changes in customer demand, traffic conditions, and other operational factors.
• No single source of truth. Different departments worked with different datasets and versions of information, leading to discrepancies between logistics, sales, and production teams.

The client needed a scalable digital foundation that could unify operational data, automate routine processes, and enable faster, data-driven decision-making.

Methodology and Approach

To minimize implementation risks and avoid a "big-bang" transformation, LeverX followed a phased approach.

We began with a comprehensive assessment of the client's existing IT landscape, business processes, data sources, and available market solutions. The assessment confirmed that SAP BTP was a strong fit for the transformation, providing the integration, data management, analytics, and AI capabilities required while extending the client's existing SAP ecosystem, including SAP S/4HANA.

The implementation focused on four key areas:

Data Integration and Unification

Using SAP BTP and SAP Integration Suite, we consolidated data from multiple sources into a unified data environment. This included SAP S/4HANA Cloud data related to inventory and orders, GPS and telemetry data, and information from third-party APIs.

This approach established a single source of truth and gave business teams consistent, up-to-date information for operational planning and decision-making.

AI-Powered Forecasting and Optimization

We integrated AI models to improve forecasting and operational efficiency.

Predictive AI analyzes historical delivery and operational data to identify patterns and optimize logistics processes, including route planning and resource allocation. Generative AI capabilities complement predictive models by helping analyze complex demand patterns and supporting more proactive inventory and resource planning.

Together, these capabilities enabled the company to move from reactive decision-making toward more predictive and proactive operations.

Advanced Analytics

SAP Analytics Cloud was used as a core platform for business intelligence, planning, and predictive analytics. It provided business users with near real-time visibility into key operational metrics and accelerated planning and analysis.

Scalability and Flexibility

The SAP BTP architecture provided the flexibility to develop and adapt applications as the company's data volumes, operational requirements, and business processes evolved.

This phased and integrated approach helped the client establish a foundation for continuous optimization rather than a one-time transformation.

Solution

LeverX implemented a comprehensive solution combining SAP BTP, AI, data management, automation, and advanced analytics to optimize production and logistics operations.

Workflow Automation

SAP Build Process Automation was used to streamline repetitive processes and automate key operational workflows. This reduced manual effort, improved process consistency, and allowed employees to focus on higher-value activities.

Analytics and Production Optimization

SAP Analytics Cloud provided advanced analytics and planning capabilities, helping the company better understand production performance, optimize production volumes, and support faster operational decisions.

AI-Powered Quality Control

AI capabilities integrated through SAP AI Core enhanced automated quality inspection. The solution analyzes production data and identifies product defects with 90% higher detection accuracy than human inspection alone, helping improve quality while reducing the dependency on manual inspection.

Real-Time Data Management

SAP Datasphere enabled the integration and analysis of large volumes of production and operational data. By bringing information together in a unified data environment, the solution provided teams with a consistent view of operations and enabled faster, data-driven 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 measurable improvements across logistics, production, and quality management:

• 15–20% reduction in fuel and operational costs. Route optimization and improved vehicle utilization reduced unnecessary mileage and operating expenses.
• 5% reduction in empty mileage. More efficient planning helped decrease the distance traveled without cargo.
• 10–15% fewer delivery delays. Improved forecasting and planning increased delivery predictability and reduced disruptions.
• 90% improvement in defect detection accuracy. AI-powered quality control significantly enhanced the identification of production defects compared with manual inspection.
• Unified operations across 200 distribution centers. More than 320 employees across the distribution network were aligned around standardized corporate plans and a single source of truth.

By combining SAP BTP with AI, automation, and advanced analytics, the company established a unified digital foundation for production and logistics management. The solution not only addressed immediate operational challenges but also created a scalable platform for continuous optimization and data-driven growth.