亿邦智库：产业互联网如何实现数据价值共创？融合利用关键在哪？

The article examines the value co-creation mechanisms and implementation pathways in industrial internet data, emphasizing multi-party collaboration to break down data silos and generate new value.

1. Value Co-creation Mechanism: Platform enterprises, upstream/downstream firms, and technology providers form a sustainable data circulation ecosystem through resource sharing and collaborative innovation, enhancing overall efficiency.

2. Five Implementation Paths: These include supply chain collaboration (e.g., auto parts platforms boosting inventory turnover by 25%), industrial chain finance innovation (e.g., commodity platforms reducing financing costs by 2-3 percentage points), product/service智能化 upgrades (e.g., construction machinery platforms cutting failure rates by 30%), industrial knowledge co-creation (e.g., chemical platforms lowering energy consumption by 8%), and market/resource optimization (e.g., textile platforms increasing capacity utilization by 12%), each supported by case studies and data.

3. Technology Enablers: Privacy-preserving computing (e.g., federated learning) and blockchain ensure secure data usability, addressing sharing concerns.

4. Challenges & Trends: Current hurdles include technical performance bottlenecks, trust-building difficulties, uneven data quality, and talent shortages; future trends highlight shifts from point solutions to ecosystem synergy and from technology-driven to dual technology-institution drivers.

The article reveals how data drives product R&D and consumer trend insights for brands, enhancing competitive advantage.

1. Product R&D: Integrating operational and user data enables intelligent upgrades, e.g., construction machinery platforms improving designs to reduce failure rates by 30%, shifting from product sales to "product + service" models.

2. Consumer Trends: Market data optimization provides insights, e.g., textile platforms consolidating order and trend data to create industry monitoring indices, aiding demand forecasting and investment decisions.

3. User Behavior: After-sales data fuels service innovation, boosting customer experience and economic returns, such as lessors increasing rental income by 20% through maximized equipment utilization.

4. Case Example: Auto parts platforms demonstrate how multi-party collaboration stabilizes supply chains, ensuring OEM security and supplier efficiency, reflecting brand channel development and user value extension.

The article offers policy context, market opportunities, and learnable business models to help sellers adapt to changes.

1. Policy Insight: China’s domestic demand expansion strategy promotes data element circulation, spurring new demand and high-quality supply, with national data bureaus backing industrial internet platforms.

2. Growth Opportunities: Shifting consumer demands drive efficiency gains, e.g., supply chain collaboration raising on-time delivery by 18%, while financial innovations address SME financing gaps, with credit lines exceeding ¥5 billion.

3. Risks & Opportunities: Benefits include order growth and cost reduction; challenges involve high technical barriers. Opportunities lie in platform participation, e.g., "efficiency-sharing" models.

4. Business Models: Learnable approaches include "data-enhanced credit" in industrial finance, product subscription services, and partnerships with OEMs, suppliers, or financial institutions.

5. Risk Mitigation: Leveraging trusted technologies (e.g., blockchain) avoids data risks, and revenue-sharing mechanisms address trust issues.

The article focuses on how data optimizes factory production and design, uncovering digital business opportunities.

1. Production Needs: Supply chain collaboration integrates order, inventory, and production data for accurate forecasting and smart scheduling, e.g., auto parts cases increasing inventory turnover by 25% and reducing disruption losses by 40%.

2. Design Needs: Operational data drives intelligent upgrades, e.g., construction machinery platforms using device parameters to cut failure rates by 30%, enabling "manufacturing + service" value extension.

3. Business Opportunities: Participating in industrial platforms yields stable orders, knowledge sharing (e.g., chemical platform process optimization), or financing ease, as seen in unsecured credit reducing costs for commodities.

4. Digitalization Insights: Building full-lifecycle data networks, e.g., maximizing equipment utilization to raise rental income by 20%; paths include adopting federated learning, emphasizing ecosystem integration.

The article outlines industry trends, new technologies, and solutions for service providers to gain insights.

1. Industry Trends: Data integration shifts from point solutions to ecosystem synergy, with future changes like dual technology-institution drivers and knowledge co-creation, positioning industrial internet as a value hub.

2. New Technologies: Privacy-preserving computing (e.g., federated learning, secure multi-party computation) and blockchain mature, enabling "usable but invisible data" to address client security concerns.

3. Client Pain Points & Solutions: Issues include data silos, SME financing hurdles, and knowledge barriers; solutions cover five paths, e.g., supply chain collaboration reducing disruptions, finance innovation via "trusted trade data chains" for dynamic credit, and knowledge co-creation optimizing processes.

4. Case Examples: Commodity platforms partner with banks to solve financing, while chemical platforms use knowledge graphs to serve SMEs, demonstrating subscription-based profitability.

The article details platform strategies, operations, and business needs for marketplace sellers.

1. Platform Practices: As connectors, platforms build data collaboration spaces, e.g., auto parts platforms using federated learning for supply chain optimization, or commodity platforms leveraging blockchain for "trusted trade data chains."

2. Business Needs & Issues: Addressing pain points like financing and efficiency; needs include multi-party cooperation (OEMs, suppliers, financial institutions), while challenges involve trust-building and lack of technical standards.

3. Operations Management: Revenue models like "efficiency-sharing" or service fees ensure sustainability; merchant recruitment involves integrating industry players, e.g., textile platforms aggregating data from 2,000+ firms.

4. Risk Avoidance: Privacy computing and blockchain secure data flow, mitigating tampering risks; institutional designs like "three-right separation" clarify responsibilities, reducing operational uncertainty.

The article explores industry dynamics, challenges, and policy implications for researchers.

1. Industry Trends: Four key shifts include moving from point applications to ecosystem synergy, data linkage to knowledge co-creation, and cost centers to value centers, reflecting dual technology-institution drivers.

2. New Problems: Challenges encompass slow trust accumulation, inconsistent data quality (e.g., tacit knowledge hurdles), talent shortages, and technical limits like privacy computing performance.

3. Policy Implications: Institutional innovation requires incentive-compatible mechanisms, e.g., "three-right separation" for data ownership, usage, and operation rights, with layered revenue distribution; governments should craft precise industrial policies, while firms engage in compliance.

4. Business Models: Value co-creation transforms production relations through multi-party collaboration (platforms, firms, financial institutions), assetizing data, with cases showing value multiplication via supply chain and finance innovations.