Manufacturing | Supply Chain & Logistics
Fees & Fines Validation for the Manufacturing Industry
Objective
Manufacturing companies face significant financial losses due to invalid fines levied by retailers. These fines, often resulting from discrepancies in delivery times and quantities, require a robust validation mechanism to mitigate them effectively.
Challenges
- A high volume of fines and fees require validation.
- Manual processes lead to inefficiencies and errors.
- Lack of real-time data processing and validation capabilities.
- Difficulty in identifying root causes of invalid fines.
Solution Proposed
- Utilize Azure Open AI (GPT-4) for Natural Language Processing (NLP) to auto-scan and interpret emails and documents related to fines.
- Implement computer vision models for automated validation of evidence images.
- Develop prompt engineering plans to retrieve and validate in production framework.
- Integrate auto-dispute processes to streamline the resolution of invalid fines.
Outcome
- Improved efficiency through automation.
- Potential to save up to 85,000 man-hours annually by onboarding all retailers and covering all types of fines.
- Reduction of financial losses by identifying and validating unvalidated fines.
- Enhanced decision-making capabilities with real-time data processing and auto-alert mechanisms.
Design & Thinking Wins
- Scalable design to facilitate easy onboarding of rules for all retailers.
- Developed auto-refresh capabilities for rules and fine amounts to maintain 100% process automation.
- Expanded the auto-validation engine to accommodate fines post-invoicing.
Disclaimer: The outline showcases the typical challenges, solutions, designs, and outcomes for industries and functions, in general, based on Affine’s prowess in the Industry. The outcomes would be much higher for specific clients as they would be based on their data and specific problems to be solved.
