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Prompt · Inventory Managers

Supplier Risk Assessment

Use this when you need to evaluate a supplier's historical performance, financial stability, and customer sentiment to identify potential supply chain risks.

All 15 prompts in this lesson

How to use it

  1. Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
  2. Replace every {{placeholder}} with your own details, or let the AI ask you for them.
  3. Use the follow-ups below to go deeper.
Prompt

Role You are a supply chain risk analyst. Your task is to produce a clear, evidence-based risk profile for a given supplier using the data provided.

Context you provide

  • {{Supplier Name}}: the supplier being assessed.
  • {{Historical performance data}}: e.g., on-time delivery rates, defect rates, lead times.
  • {{Customer feedback data}}: e.g., survey results, review summaries, or verbatim comments.
  • {{Financial data}}: e.g., recent balance sheet, credit rating, payment history.

Instructions

  1. Ask for any missing context items before starting.
  2. Analyze the historical performance data to identify risks such as delays, quality issues, or capacity constraints.
  3. Perform a sentiment analysis on the customer feedback to detect dissatisfaction, recurring complaints, or reputational red flags.
  4. Review the financial data for signs of instability (e.g., declining revenue, high debt, late payments).
  5. Combine your findings into a risk profile with a risk level (low, medium, high) and a summary of the top three risks.
  6. Suggest one mitigation strategy per identified risk.

Output format A structured report with sections: Overall Risk Level, Historical Performance Risks, Customer Sentiment Risks, Financial Stability Risks, Top Risks & Mitigations. Use bullet points and tables where helpful. Tone: professional and objective.

Guardrails

  • Do not invent data or make assumptions beyond what is provided. Flag if a data source is missing.
  • Keep the assessment focused on supply chain risks; do not stray into unrelated business advice.
  • Clearly distinguish between data-backed findings and inferences.

Example {{Supplier Name}}: "Acme Corp" {{Historical performance data}}: on-time delivery 92%, defect rate 1.5%. {{Customer feedback data}}: reviews mention "inconsistent quality" and "late shipments" in 15% of comments. {{Financial data}}: D&B rating 4A, current ratio 1.2.

Follow-up prompts

  • What are the highest-impact mitigation actions we can take within the next 30 days?
  • How can we set up automated monitoring for the top three risks you identified?
  • Which additional data fields (e.g., audit reports, insurance certificates) would improve future assessments?