Logistics Optimization with SAP BTP and Generative AI

LeverX significantly optimized logistics processes for a large manufacturing company by implementing a solution based on SAP BTP and Generative AI. Our work generated measurable results, including a 15–20% cut in operational costs and improved delivery punctuality.

Client and Challenge

Our client is a large manufacturing company with a distributed structure, including a head office in Europe, five production sites in Asia and North America, and 200 distribution centers worldwide. The company operates with high volumes of data and faced significant challenges in managing its logistics network.

The client's main pain points included:

• Fragmented logistics data: Data was scattered across numerous disparate sources, including Excel spreadsheets, decentralized fleet management systems, and third-party services, making it difficult to obtain a complete and reliable picture.
• Slow planning and reporting processes: Route planning, demand forecasting, and logistics reporting processes were slow. Preparing consolidated reports could take days or weeks, rendering data outdated upon receipt.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: The company struggled with obtaining timely and up-to-date information, and slow processes prevented quick reactions to market changes (e.g., traffic jams, changes in demand), leading to missed opportunities.
• Lack of a single source of truth: The absence of a central data hub caused discrepancies in expectations and misunderstandings among logistics, sales, and production departments. Management expressed dissatisfaction with these inconsistencies, as they reduced overall efficiency and hindered strategic alignment.

Solution

LeverX built an SAP BTP-powered extension for supply chain processes, ensuring seamless logistics data integration and faster planning and analysis. This fully complements SAP’s digital supply chain portfolio.

The choice of SAP BTP resulted from a thorough audit of the client's existing systems and a market solution assessment. The platform was chosen because its key capabilities perfectly matched the company's needs.

Key aspects of the solution:

• Comprehensive integration: SAP BTP and SAP Integration Suite provide powerful tools for seamless data integration from various systems (SAP S/4HANA, GPS trackers, third-party traffic and weather API data), which eliminates the fragmentation of logistics data.
• Single source of truth: Thanks to SAP BTP, including SAP Analytics Cloud and SAP Datasphere, a centralized data repository is created, ensuring consistency and reliability of logistics information across the entire organization.
• Flexibility and scalability: The platform allows for rapid application development and adaptation, as well as scaling them as data volumes and business needs grow.
• SAP ecosystem: The platform ensures deep integration with key components of the SAP ecosystem, including SAP S/4HANA, which guarantees the seamless and accurate operation of critical business processes.
• Forecasting and optimization: The built-in business intelligence and predictive planning tools within SAP Analytics Cloud accelerate current processes and enable demand forecasting and route optimization. This is achieved using Transformer models to analyze delivery data and predict routes, and Variational Autoencoders (VAEs) to analyze consumer demand patterns.

Key components of the solution included:

• Unified logistics data source: Leveraging SAP BTP and SAP Analytics Cloud, data from various sources, including SAP S/4HANA Cloud (for inventory and orders) and GPS/telemetry data, is integrated into a single, reliable source of truth. This eliminates data fragmentation and ensures consistency across all 200 distribution centers.
• Accelerated analysis and planning: The solution provides on-demand data access, significantly speeding up the analysis of fleet utilization, route efficiency, and planning processes.
• Predictive demand and route planning: Predictive demand forecasting and route optimization, considering real-time factors (traffic, weather), are implemented.
• Business intelligence, planning, and predictive analytics: SAP Analytics Cloud is a core component for business intelligence, planning, and predictive analytics, including demand forecasting and route optimization.
• Data integration: SAP Integration Suite ensures seamless data integration from SAP S/4HANA and other logistics systems (warehouse management systems, tracking), guaranteeing a continuous and accurate flow of information.
• Data unification: SAP Datasphere creates a unified logistics data source by consolidating data from various disparate sources.

Through this phased integration project, the company gains real-time insights and aligned expectations across teams, significantly improving overall logistics management efficiency.

Technology Stack

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

• Core platform: SAP BTP provides the foundational cloud environment and services for developing and running applications.
• Analytics and planning: SAP Analytics Cloud is the key component for business intelligence, planning, and predictive analytics.
• Database: SAP HANA Cloud (core of SAP Datasphere) is utilized for high-performance in-memory data storage and processing.
• Integration: SAP Integration Suite ensures seamless data integration.
• Data unification: SAP Datasphere creates a unified analytical base from heterogeneous sources.
• Generative AI models: Transformer-based models analyze delivery data and predict optimal routes, while Variational Autoencoders (VAEs) model and forecast consumer demand patterns.
• AI management: SAP AI Core, SAP AI Launchpad, and SAP Generative AI Hub are used to manage the integration lifecycle of AI models.
• Pre-configured tools: Intelligent applications and data products provided a solid foundation and reduced implementation time.

Results

The implementation of the solution resulted in significant, measurable business benefits for the client:

• Reduced operational costs: Optimizing inefficient routes and improving vehicle utilization saved up to 15-20% on fuel and operational expenses, resulting in a 5% decrease in empty mileage.
• Increased delivery punctuality: Improved forecasting and planning reduce delivery delays by 10-15%.
• Team unification: 200 distribution centers with 320 employees have been united under corporate plans, working with a unified information base.
• Phased approach: Implementing the solution in stages minimized risks and avoided a disruptive one-time ‘big-bang’ modernization.

Ultimately, the realization of this project allows the client to transition from reactive to proactive, data-driven logistics management. The company not only significantly reduces operational costs and improves service quality but also strengthens its competitive position in the market by implementing intelligent, adaptive processes capable of quickly responding to changing market conditions.