齐心集团：将AI大模型应用至40+业务场景｜2024千峰系列访谈

Qixin Group leverages AI large language models to address pain points in digital procurement, achieving efficiency gains and cost reduction.

1. The system covers over 40 business scenarios, including ordering, product settlement, reconciliation services, after-sales, and marketing across the entire procurement process.

2. Specific outcomes include a 20% inventory reduction, over 15% improvement in fulfillment efficiency, and optimized customer service experience.

Practical case studies demonstrate deep technology implementation.

1. An intelligent product recommendation model automatically matches suppliers and fulfillment methods based on client needs, enhancing decision-making efficiency.

2. Intelligent settlement and digital human applications streamline reconciliation processes and reduce human errors.

3. Case example: In master data governance and intelligent inquiry/quotation, AI assists in ensuring data security and improves end-to-end automation levels.

The digital procurement trend is driving strategic adjustments for brands.

1. Consumption trends are shifting towards digitalization and centralization, with government policies supporting e-commerce and digital procurement, creating growth opportunities for enterprises.

2. User behavior observations show corporate procurement preferences for efficient, intelligent services, influencing brands' channel development and product R&D directions.

AI technology provides new insights for product development.

1. Large model applications, such as intelligent product analysis combined with big data for quality scoring, inspire brands to optimize quality control and design.

2. Proprietary large model libraries for products support personalized recommendations, helping brands capture demand shifts and enhance price competitiveness and marketing effectiveness.

3. Case example: When serving government and enterprise clients, Qixin's AI in product standard libraries reduces inventory pressure and drives brand product innovation.

Policy analysis reveals potential in growing markets.

1. Government policies supporting digital procurement provide security for the collective procurement industry, bringing positive opportunities like market expansion and risk mitigation.

2. Changing consumption demands show corporate procurement shifting towards automation; sellers can learn from AI applications to improve service responsiveness.

Event response measures and collaboration methods inspire new business models.

1. Qixin's practice of AI in intelligent order allocation and logistics scheduling improves efficiency by over 15%, offering sellers strategies to handle supply chain disruptions.

2. Collaboration opportunities: Utilize Qixin's one-stop platform for multi-mode integration (e.g., digital procurement, cloud procurement) and access support systems like supplier evaluation.

3. Opportunity note: AI reduces errors in bidding processes; sellers can explore intelligent decision support to mitigate compliance risks.

AI enables precise management for optimizing production and design needs.

1. AI provides product procurement suggestions and planning guidance, improving production plan accuracy and real-time responsiveness, thereby reducing inventory backlog.

2. Data sharing helps factories optimize inventory management, achieving 20% cost reduction and efficiency gains, highlighting the value of digital production processes.

Business opportunities emerge within the digital procurement ecosystem.

1. Opportunity: Supply products like MRO industrial goods through Qixin's platform, participating in a system with over 2 million SKUs to expand sales channels.

2. E-commerce advancement: Leveraging AI technologies like intelligent warehousing can enhance supply chain coordination and lower operational costs for factories.

3. Case example: Suppliers using Qixin's data sharing achieve efficient alignment with government/enterprise client needs, promoting industrial upgrading.

Industry trends point towards comprehensive digital transformation.

1. Pain points like data security and inconsistent coding are prominent; AI large models offer solutions such as master data governance and intelligent auditing.

2. New technology applications: Large models automate over 40 scenarios, including intelligent customer service and settlement, boosting service efficiency.

Client pain points and solutions drive innovation.

1. Pain points: Data silos and quality issues; Qixin addresses these through data cleansing and integration to build private enterprise knowledge bases.

2. Solutions: MOE architecture and cloud computing services tackle technical limitations, such as constrained model inference capabilities.

3. Future direction: Supply chain digitalization is deepening ecosystem-wide; service providers can explore green supply chains and industry-specific large model development.

Business demands on platforms focus on automated, efficient collaboration.

1. Needs like automated procurement processes are met by Qixin's platform through automated order splitting, fulfillment, and settlement, enhancing collective procurement service efficiency.

2. Challenges like data management issues are addressed via AI technologies that ensure security and robustness, mitigating risks.

Latest platform practices and operational management strengthen competitive advantage.

1. Building a one-stop enterprise service platform covering 50+ categories and 2+ million SKUs, supporting multi-mode integration (e.g., digital procurement, cloud procurement).

2. Platform recruitment: Attracting partners through supplier system evaluations and automated processes optimizes operational management.

3. Risk mitigation: AI in intelligent demand forecasting reduces errors; platforms can promote intelligent decision support to address compliance issues.

Industry trends indicate AI-driven supply chain transformation.

1. New developments: Large model applications are expanding from single links to full-chain digital transformation, such as intelligent collaboration and green supply chain development.

2. Emerging issues: Data privacy/security challenges and AI cognition gaps are prominent, requiring engineering approaches to address shortcomings.

Policy recommendations and business models inform industrial upgrading.

1. Policy suggestions: Government support for digital procurement policies provides safeguards, highlighting the need for strengthened data privacy regulations.

2. Business models: Platform-enabled empowerment, like Qixin's industry-specific large model exploration, fosters digital industrial cluster formation and value synergy.

3. Future direction: Industrial chain integration and sustainable development; researchers can analyze how intelligent technologies enhance industrial efficiency.