【罗兰贝格英伟达】2024年制造业前瞻AI赋能制造业与运营创新加速_28页_751kb

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Report Summary: Industrial Leaders Leverage AI for Next-Generation Manufacturing and Operations

Artificial intelligence (AI) has become a critical driver for competitiveness, sustainability, and addressing labor shortages in industrial operations. However, widespread adoption faces challenges such as proving business value, lack of infrastructure, and shortage of skilled AI talent. This report highlights seven key success factors for effective AI integration, drawn from real-world applications in Microsoft Intelligent Manufacturing Awards (MIMA) and other industry cases.

1. Clear AI Roadmap: Successful companies define explicit objectives, secure executive sponsorship, and align AI initiatives with strategic organizational change processes. For instance, Danone's Digital Manufacturing Acceleration program outlines three action streams (blue, yellow, red) to prioritize use cases, achieving EUR 55 million annual savings across 71 factories.

2. Robust Technical Foundation: Ensuring data security while enabling accessibility and rapid scaling is vital. BMW's Edge Ecosystem platform uses cloud-based edge computing devices to manage hardware and reduce asset management efforts by 80%. Mercedes-Benz’s MO360 Data Platform decouples storage and usage, supporting 80+ use cases and a 20% efficiency boost by 2025.

3. Modular Platforms: These simplify scaling and adaptability. Andritz consolidated AI solutions into its Metris platform, improving production by 18% across 40 global plants. Microsoft’s Azure Marketplace provides pre-built tools for quick deployment, enabling companies to leverage partner solutions without significant investment.

4. Leverage Off-the-Shelf Components: Using low-code platforms like Microsoft Power Platform accelerates development. Northvolt deployed TechClean, an AI solution combining image recognition and analytics, to reduce monthly labor hours by 5,200 in under two weeks. "Fail fast" approaches minimize risk while fostering hands-on experience.

5. Democratize Digitalization: Encouraging cross-level innovation ensures broader adoption. Mercedes-Benz’s MO360 platform allows employees to develop and prototype solutions, while Microsoft’s Copilot tools (e.g., Dynamics 365, GitHub) enable citizen developers to create AI-driven applications. This approach reduces dependency on specialized teams and accelerates value creation.

6. Empower Workforce: AI should complement human skills, not replace them. Siemens Gamesa uses AI-guided lasers to assist workers in precise turbine blade production, reducing errors and achieving a 2.5-year breakeven. Wilo’s workforce assistant optimizes assembly instructions based on skill levels, boosting productivity by 20% and cutting customer claims by 60%.

7. Disrupt Value Creation: AI-generated insights enable manufacturers to enhance existing offerings, maintain installed bases, and innovate. Andritz sells production guarantees instead of physical products, leveraging data from digital pulp factories to reduce client downtime. Trumpf’s pay-per-part model for remotely controlled machines uses AI to scale efficiency. Danfoss shifted from HVAC components to climate solutions, while Bosch’s spare parts identification tool reduced search time by 80%.

Future Outlook:
AI is transforming the entire value chain, from R&D to supply chain and production. Generative AI (GenAI) and large language models (LLMs) unlock insights from previously siloed data, enabling predictive maintenance and process optimization. As AI becomes more autonomous, ethical compliance and regulatory alignment will grow in importance.

Support Frameworks:
Roland Berger’s rAIse framework combines strategic roadmapping, technical execution, and cultural readiness. Its proprietary GenAI platform uses AI agents to generate tailored solutions for clients. Microsoft offers tools like Copilot, Azure Marketplace, and Azure OpenAI Service to simplify AI implementation, with Dynamics 365 Copilot enhancing sales and GitHub Copilot streamlining software development.

Key Takeaways:

• AI adoption requires holistic planning, addressing technical, cultural, and strategic barriers.
• Modular and scalable platforms, combined with existing tools and expertise, reduce complexity and costs.
• Democratizing AI through accessible tools fosters innovation and reduces reliance on specialized teams.
• Empowering employees with AI-driven assistance improves productivity and mitigates labor shortages.
• Success hinges on aligning AI with business goals, ensuring data quality, and fostering organizational buy-in.

The report underscores that AI adoption is not just a technological shift but a strategic transformation. Companies must prioritize data infrastructure, skilled talent development, and cross-functional collaboration to unlock AI’s full potential across operations.