Zhiyuan: Digital Transformation in Supply Chain Financing Service Custom Case Solution & Analysis

1. Evidence Brief: Case Extraction

Financial Metrics

• Loan Approval Rate: Traditional manual processing yields a 30 percent approval rate for SME applicants [Para 4].
• Transaction Volume: Zhiyuan processed over 15 billion RMB in financing during the 2022 fiscal year [Ex 1].
• Cost of Funds: Average borrowing cost from partner banks sits at 4.5 percent, while lending rates to SMEs average 8.2 percent [Ex 2].
• Non-Performing Loan (NPL) Ratio: Current traditional model maintains an NPL of 1.2 percent, significantly lower than the industry average of 2.5 percent for unsecured SME loans [Para 12].

Operational Facts

• Verification Process: Traditional model requires physical stamps and paper invoices from core enterprises to validate accounts receivable [Para 6].
• Onboarding Time: Average time to onboard a new SME supplier is 14 business days [Para 8].
• Technology Stack: Existing system is a centralized database with manual data entry points; the proposed digital transformation involves blockchain-enabled smart contracts [Para 15].
• Geographic Reach: Operations are concentrated in the Yangtze River Delta, serving 450 core enterprises and 3,200 SMEs [Ex 3].

Stakeholder Positions

• Wang Wei (CEO): Advocates for a platform-based model to decouple growth from headcount [Para 3].
• Li Jun (Head of Risk): Expresses concern that removing core enterprise guarantees will spike default rates [Para 19].
• Partner Banks: Require 100 percent transparency on underlying transactions before increasing credit lines to the platform [Para 22].
• SME Borrowers: Demand faster liquidity and lower collateral requirements to manage working capital cycles [Para 25].

Information Gaps

• The specific capital expenditure required for the full blockchain migration is not disclosed.
• Competitor pricing for digital-only supply chain finance platforms is absent.
• Retention rates for SMEs after the first loan cycle are not provided.

2. Strategic Analysis

Core Strategic Question

• How can Zhiyuan transition from a labor-intensive, guarantee-dependent financing model to a scalable digital platform without compromising its superior credit risk profile?

Structural Analysis

The supply chain finance industry is shifting from credit-based lending to data-based lending. Using the Value Chain lens, Zhiyuan's primary bottleneck is the Inbound Logistics of data. Manual verification creates a linear relationship between volume and cost. Porter's Five Forces reveal high threat from tech-giant entrants who possess superior data ecosystems, making Zhiyuan's current manual moat temporary.

Strategic Options

1. The Pure Platform Play: Pivot to a software-as-a-service model. Zhiyuan provides the blockchain infrastructure for banks and core enterprises but takes zero balance sheet risk.
   • Trade-off: Higher scalability but lower interest income; requires massive SME volume to offset margin compression.
   • Resources: Significant investment in API development and cybersecurity.
2. The Hybrid Risk-Sharing Model: Implement the digital platform while retaining a 10-20 percent first-loss position on loans. Use IoT and blockchain to track real-time inventory and logistics data.
   • Trade-off: Higher capital requirement than Option 1 but retains better bank partnerships by aligning incentives.
   • Resources: Integration with third-party logistics providers for data feeds.
3. The Core-Enterprise Deepening: Double down on the traditional model by embedding staff within the procurement offices of the top 50 core enterprises to capture more of their tier-2 and tier-3 suppliers.
   • Trade-off: Lowest execution risk but fails to address the fundamental scalability problem.
   • Resources: Increased headcount in account management.

Preliminary Recommendation

Zhiyuan must pursue the Hybrid Risk-Sharing Model. Relying solely on software fees is insufficient to sustain current growth targets, while the manual model is a dead end. By taking a small portion of the risk, Zhiyuan proves the efficacy of its digital credit scoring to banks, enabling them to lower the cost of capital for SMEs.

3. Implementation Roadmap

Critical Path

• Month 1-2: Establish API protocols with the top 10 core enterprises to automate accounts receivable data extraction.
• Month 3-4: Launch a pilot blockchain node with one primary funding bank to test smart contract execution for automated repayments.
• Month 5-6: Transition 20 percent of existing SME clients to the digital onboarding process, targeting a reduction in approval time from 14 days to 48 hours.

Key Constraints

• Data Silos: Core enterprises are often reluctant to share granular supplier data due to fears of price transparency or competitive poaching.
• Regulatory Compliance: Chinese data privacy laws require explicit SME consent for every data point used in credit scoring, complicating automated scraping.

Risk-Adjusted Implementation Strategy

To mitigate the risk of model failure, Zhiyuan will run the digital credit engine in parallel with the manual process for six months. No loan will be approved by the algorithm alone until the digital model achieves a 95 percent correlation with manual risk assessments over a full 90-day repayment cycle. Contingency funding of 50 million RMB should be earmarked to cover potential early-stage defaults during the calibration phase.

4. Executive Review and BLUF

BLUF

Zhiyuan must transition to a hybrid digital platform immediately. The current manual verification model is not scalable and leaves the firm vulnerable to fintech competitors with lower cost structures. By integrating blockchain and real-time logistics data, Zhiyuan can reduce onboarding time by 85 percent and lower the cost of funds through improved bank transparency. The shift from core-enterprise guarantees to data-driven risk assessment is the only path to maintaining market leadership. Failure to digitize within 18 months will result in terminal margin erosion as banks bypass Zhiyuan to work directly with tech-enabled platforms.

Dangerous Assumption

The analysis assumes that core enterprises will grant Zhiyuan access to their internal ERP systems. These firms often view supplier payment terms as proprietary strategic data and may resist integration, regardless of the technological benefits to the SMEs.

Unaddressed Risks

• Systemic Liquidity Risk: If partner banks face a credit crunch, Zhiyuan's platform volume will collapse regardless of its technology, as it lacks its own lending license.
• Cybersecurity Breach: Centralizing the financial data of 3,200 SMEs on a single blockchain creates a high-value target for state-sponsored or independent hackers.

Unconsidered Alternative

The team did not evaluate an acquisition strategy. Instead of building a blockchain platform internally, Zhiyuan could acquire a struggling fintech startup with the required architecture. This would accelerate the digital transition by 12 months and provide immediate access to technical talent that is currently difficult to recruit.

Verdict: APPROVED FOR LEADERSHIP REVIEW