5 AI capabilities reshaping the logistics industry

AI-driven transformation in supply chains: Generative AI is poised to revolutionize supply chain operations, with nearly three-quarters of executives planning to deploy the technology, despite current implementation challenges.

• EY research indicates that 73% of supply chain and operations executives intend to implement generative AI, but only 7% have successfully done so to date.
• The transition from proof-of-concept to large-scale GenAI deployment remains difficult due to issues with data quality, organizational readiness, and both internal and external volatility.
• Early AI adopters in supply chain and inventory management have reported meaningful revenue increases, according to a 2023 McKinsey survey.

Enhancing planning accuracy: AI-powered solutions are improving supply chain decision-making by leveraging historical data and predictive analytics to optimize lead times and inventory management.

• Data integrity is crucial for enabling seamless end-to-end processes across the entire supply chain, forming the foundation for autonomous operations.
• Only 50% of organizations using GenAI in their supply chain have achieved end-to-end visibility, highlighting the need for further integration and digitization.
• AI models trained on verified and consolidated data can accurately predict future lead times and track shipment status in real-time, leading to improved customer satisfaction.

AI-integrated ERP systems: The integration of AI solutions with Enterprise Resource Planning (ERP) systems is supercharging business decisions and moving supply chains towards autonomy.

• SAP’s AI copilot, Joule, works in conjunction with the company’s ERP solution, SAP S/4HANA, to provide planners with better insights into inventory constraints and proactive recommendations.
• This integration helps supply chain professionals make more informed decisions and accurately predict lead times, reducing the need for human intervention.

Streamlining manufacturing processes: AI is transforming manufacturing by minimizing disruptions, improving error resolution, and enhancing product design and compliance.

• AI can accelerate error resolution faster than human labor, preventing production standstills and catching issues before they occur.
• The technology is being used to eliminate redundant tasks, such as tagging data on product visualizations, and to develop more efficient product recipes while supporting compliance and sustainability.

Predictive maintenance optimization: AI-powered predictive maintenance is minimizing unplanned outages, reducing supply chain disruptions, and optimizing asset performance.

• Through camera footage and visual inspections, AI models can detect errors, faults, or defects in equipment before they happen, allowing for proactive maintenance scheduling.
• Swiss Federal Railways (SBB) is piloting AI-enabled inspections to assess critical components like pantographs, improving safety and punctuality in their operations.
• SAP’s Asset Performance Management solution compiles condition data through Joule and connects it to S/4HANA, providing contextual information and AI-powered recommendations for next steps.

Challenges and future outlook: While AI shows promise in supply chain management, widespread integration remains a challenge for many organizations.

• Companies are implementing generative AI tools for small use cases to demonstrate the technology’s potential before scaling across their organizations.
• As digitalization efforts continue, finding applicable use-cases for AI will be crucial for accelerating progress and realizing real-world benefits in supply chain operations.

The path to autonomous supply chains: The innovations discussed represent significant steps towards achieving truly autonomous supply chains, with AI playing a central role in this transformation.

• First-movers who have integrated AI into their processes have already seen positive results, paving the way for broader adoption across industries.
• As companies continue their digital transformation journeys, the focus will be on finding and implementing AI use-cases that deliver tangible benefits and drive progress towards supply chain autonomy.