Check the final outcome here: https://corporategovernance-chen.streamlit.app/

1. Scenario & Conflict Definition
Vertical Scenario: Supplier Qualification Review in Supply Chain Procurement
Typical Conflict Scenarios:

1. Efficiency vs. Compliance Conflict:

   • Procurement Department: Uses AI to automatically approve low-risk suppliers with complete qualifications to shorten procurement cycles.
     • Tech Stack: Machine learning (historical collaboration data + business registration information scraping) + RPA for automated form filling.
   • Legal Department: Legal AI detects litigation records involving the supplier’s affiliated companies, requiring process suspension.
     • Tech Stack: NLP (real-time monitoring of China Judgments Online) + Knowledge Graph (affiliated company penetration analysis).
   • Finance Department: Identifies that supplier quotes exceed historical average prices in AI models, triggering renegotiation.
     • Tech Stack: Time series forecasting (3-year price fluctuation analysis) + dynamic cost modeling.

2. Data Interpretation Conflict:

   • Procurement: Recommends partnerships based on supplier delivery capability scores.
   • Legal: AI scans contracts and flags ambiguous clauses, demanding manual revision.
   • Finance: Detects conflicts between payment terms and cash flow forecasts, suggesting adjustments to payment timelines.

Core Pain Points:

• Goal Conflicts: Procurement prioritizes efficiency, Legal emphasizes compliance, Finance focuses on cost control.
• Decision Silos: Departmental AI systems operate independently without holistic oversight.
• Coordination Challenges: Manual coordination is time-consuming, with blurred accountability between model decisions and human responsibilities, leading to supplier attrition.

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2. Specific Conflict Scenario (Simplified)
Time: March 2024
Trigger Event: New supplier “Ruifeng Precision” applies for battery tray supply qualification.

1. Procurement Specialist Wang Yue (Efficiency-Oriented):
   • System confirms supplier qualifications (Tax Class A, certifications complete), recommends direct contract signing.
   • Submits e-contract to Legal/Finance.
2. Legal Manager Zhang Tao (Risk-Averse):
   • NLP system identifies unresolved transportation contract disputes involving another company owned by the supplier’s actual controller.
   • Email alert to Procurement: “Recommend delaying signing; supplementary risk disclosure required.”
3. Finance Director Chen Min (Cost-Focused):
   • Time series model shows current quote is 12% above 3-year average, exceeding acceptable fluctuation (±5%).
   • Internal system flag: “Recommend renegotiation to avoid missing quarterly cost-saving targets.”

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3. Solution Design
”ChainAI Judge” Cross-Departmental AI Arbitration Workflow
A three-phase governance method transforms AI conflicts into traceable organizational decisions, managing technical complexity through predefined rules.

Phase 1: Conflict Identification & Classification (Automated)

• System: Supply Chain Governance AI Tagging System
• Inputs:
  • Daily: Summaries of departmental AI standards and tool documentation.
  • Emergency: Legal AI risk ratings + Finance AI cost deviation reports + Procurement AI supplier metrics.
• Workflow:
  1. Activate Pre-Processing Matrix:

     Conflict Type	Resolution Channel	Time Limit
     Single metric outrate	Auto-compensation negotiation	2h
     Dual-goal conflict	Departmental pre-review	8h
     Triple conflict + Confidence difference	Escalate to AI Arbitration	24h

  2. Generate Conflict Summary Report highlighting departmental outputs.
  3. Prepare for Phase 2: Decision Arbitration.

Phase 2: Decision Arbitration (Human-AI Collaboration)

• Executor: Cross-Departmental AI Arbitration Committee
• Participants:
  • Chair: Supply Chain Director
  • Members: Deputy heads of Procurement, Legal, Finance + Internal Audit.
• Process:
  1. Preparation (Automated):
     • Extract historical case resolutions tagged with governance responsibility types.
     • Generate impact prediction dashboard (supplier attrition probability/compliance risk score/cost fluctuation range).
  2. Output:
     • Conditional execution instructions (e.g., “Approve but increase performance bond”).
     • Update training data labels for all three AIs.
       Phase 3: Execution & Feedback (Closed-Loop Control)
• Responsibility Matrix:

  Task	Owner	Supervisor	Success Criteria
  Contract revision	Legal	Internal Audit	Risk score < threshold
  Payment adjustment	Finance	Arbitration AI	Cash flow deviation <5%
  Supplier relationship	Procurement	Customer Success + AI Committee	Satisfaction score maintained

• Control Points:
  • Traceability: Unique event IDs linked to supplier profiles.
  • Quarterly Review: Analyze AI Misjudgment patterns to optimize models.
  • Cost Quantification: Convert hidden compliance costs (e.g., legal due diligence hours) into decision parameters to prevent KPI-driven cost shifting.

4. Why It Solves the Problem?

1. Goal Conflicts → Arbitrable Issues:
   • Conflict Classification Matrix converts abstract conflicts into actionable types (e.g., “efficiency vs. compliance” triggers dual-goal resolution).
2. Decision Silos → Unified Baseline:
   • Arbitration mandates transparent AI decision logic (e.g., Legal AI must cite specific laws).
   • Shared impact prediction models eliminate departmental data biases.
3. Coordination Inefficiency → Standardized Response:
   • Reduces resolution time from 2-3 days (manual) to ≤24h (automated).
   • Builds a Conflict Resolution Knowledge Base to accelerate future decisions.

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5. Implementation Case: “Ruifeng Precision” Resolution & Outcome
Phase 1: Conflict Identification (Initiated within 24h)

• Trigger: Triple conflict (Procurement AI approval + Legal AI freeze + Finance AI renegotiation) activates 24h arbitration.

Phase 2: Arbitration (Human-AI Collaboration)

• Committee Actions:
  • Reviewed historical cases (similar litigation + premium suppliers).
  • System predictions: 68% attrition probability vs. 0.62 compliance risk score.
• Resolution:
  • Contract: Added “affiliated litigation disclosure” clause.
  • Payment: Reduced upfront payment from 50% to 40%, final payment tied to litigation outcome.
  • AI Training: Legal AI no longer auto-freezes transportation disputes but triggers payment reviews.

Phase 3: Execution & Feedback (30-Day Closure)

• Results:

  Task	Outcome
  Contract revision	Risk score reduced to 0.58 (reach the standard)
  Payment adjustment	Cost deviation at 4.9% (<5%)
  Supplier relationship	Satisfaction score maintained at 82

Phase 4: AI Governance Upgrade

• Legal AI: Reduced risk weighting for affiliates with <20% ownership.
• Finance AI: Added “emergency procurement premium” exemption.
• Added 1 case to the Cross-Departmental Collaboration Library.

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6. Core Advantages
Structural Advantages:

1. Conflict Resolution Efficiency:
   • Reduces average resolution time from 3.5 days (manual) to 24h.
   • Decision Accountability Encoding cuts cross-department communication costs by 50%.
2. Accountability Clarity:
   • Event ID Tracing resolves 94% of duty boundary disputes.
   • Arbitration outcomes link to department OKRs, preventing KPI evasion.
3. Hidden Cost Visibility:
   • Quantifies compliance costs (e.g., $1,280/hr audit fees vs. supplier loss).
   • Balances risk-cost-efficiency via data-driven KPIs.
4. Knowledge Retention:
   • Quarterly AI Misjudgment White Papers improve training accuracy by 19%.
   • 127 reusable decision templates in the Risk Resolution Library.

Organizational Adaptability:

1. Incremental Implementation:
   • Compatible with companies of varying sizes/digital maturity via modular routing.
   • Low-code tools (e.g., Finance’s threshold adjustment plugin) ease transitions.
2. Supply Chain Empowerment:
   • Shares risk assessment models with suppliers for collaboration efficiency.
   • Monitors supplier AI health to preempt risks.

Check the final outcome: https://corporategovernance-chen.streamlit.app/

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