Use Case | Application of the Generative Model for Supplier Proposal Evaluation

Use Cases: Application of the Generative Model for Supplier Proposal Evaluation

Overview:

In the supplier selection process, it is crucial to efficiently assess the proposals submitted in response to a Request for Proposal (RFP). This model allows for the automatic analysis of proposals submitted by suppliers, identifying the one most aligned with the company’s objectives and highlighting the less favorable ones, facilitating data-driven, objective decision-making.

How It Works:

1. Uploading Proposals:
   • Users upload supplier proposals in PDF format to the system.
2. Automatic Model Analysis:
   • The model analyzes the proposals based on key criteria such as:
     • Compliance with technical requirements: Does the proposal meet the required specifications?
     • Cost: Is the price competitive and transparent?
     • Timeline: Does the supplier offer viable delivery times aligned with needs?
     • Quality: Are quality standards or certifications included?
     • Added value: Does the proposal include additional services or favorable terms?
3. Evaluation and Comparison:
   • The system classifies the proposals based on their alignment with the defined criteria, highlighting the best options and identifying the less competitive ones.
4. Report Generation:
   • The model generates a clear report that includes:
     • The best proposal: The one that meets most or all of the criteria.
     • The worst proposal: The one with the most omissions or disadvantages.
     • Recommendations: Suggested adjustments or negotiation points to improve the less favorable proposals.

Example of Use:

Scenario: A company receives five proposals from suppliers in response to a request for raw materials.

Simplified Process:

1. Upload:
   • The manager uploads the five proposals to the system.
2. Automatic Evaluation:
   • The model reviews the proposals and answers questions such as:
     • Does it meet the required technical specifications?
     • Is the price competitive and within budget?
     • Are the delivery times reasonable?
     • Does it include guarantees or additional conditions, such as volume discounts?
3. Results:
   • Proposal A: High quality, competitive price, optimal delivery times.
   • Proposal B: Meets technical requirements, but the price is high.
   • Proposal C: Attractive price, but lacks quality certifications.
   • Proposal D: Unrealistic timeline, high operational risk.
   • Proposal E: Lacks key details about deadlines and quality standards.
4. Generated Report:
   • Best Proposal: Proposal A – Ideal due to its balance of quality, price, and delivery times.
   • Worst Proposal: Proposal E – Insufficient key information and high uncertainty.
   • Recommendation: Negotiate with Supplier B to adjust the price or explore additional alternatives.

Benefits for End Users and Managers:

1. Time Savings:
   • Automatically evaluates all proposals, eliminating the need for extensive manual analysis.
2. Data-Driven Decisions:
   • The objective evaluation of each proposal ensures that the best decision is clear and well-founded.
3. Risk Identification:
   • Detects potential issues in weak proposals, such as hidden prices, unrealistic timelines, or insufficient quality standards.
4. Informed Decision-Making:
   • Provides a detailed report that facilitates proposal comparison and supports negotiation.
5. Ease of Use:
   • Results are presented in clear, straightforward language, requiring no technical expertise.

Summary of Generated Report:

• Proposals Analyzed: 5
• Best Proposal: Proposal A
  • High quality, competitive price, aligned timelines with needs.
  • Recommendation: Proceed with this supplier.
• Worst Proposal: Proposal E
  • Lack of key information on deadlines and quality.
  • Recommendation: Request clarifications or discard this proposal.

General Recommendations:

• Negotiate price adjustments for Proposal B to make it more competitive.
• Require quality certifications for Proposal C if moving forward.

Conclusion: The generative model for Supplier Proposal Evaluation transforms a complex, manual process into a streamlined, automated one. It provides managers and end users with a clear, data-driven analysis, identifying the best option and highlighting areas for improvement in less favorable proposals. This enables the company to make quick, effective decisions, optimizing costs, timelines, and quality in its procurement process.