Manufacturing | Supply Chain & Logistics
Supplier Risk Assessment Framework in the Manufacturing Industry
Objective
Manufacturing clients need to evaluate suppliers and measure their performance to assign them risk and criticality scores. The objective is to reduce process disruptions due to supplier non-adherence and ensure accurate supplier contracts based on risk and criticality.
Challenges
- Identifying key drivers of supplier risk from diverse data sources.
- Generating accurate and actionable risk and criticality scores.
- Integrating the assessment tool into the existing client system.
Solution Proposed
- Data Collection: Aggregated data from supplier financials, contract details, firmographics, and performance metrics.
- Utilized machine learning algorithms such as Random Forest for classification and Gradient Boosting for risk prediction due to their robustness and accuracy in handling diverse data sets.
- Implemented Tableau for visualizing supplier performance.
- Engineering Modules: Developed a scalable tool using Python and Microsoft SQL Server for data processing and storage, ensuring seamless integration with the client’s systems.
Outcome
- Reduced process disruptions due to supplier non-adherence.
- Accurate supplier contracts based on risk and criticality.
- Tool integration in client system to monitor supplier performance and assign risk scores.
Design & Thinking Wins
- Evaluated supplier performance to identify the key drivers of risk.
- Generated scorecards of all the suppliers based on their performance.
- Optimized suppliers based on predicted risk and criticality.
- Recommended various strategies to mitigate supplier risk.
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.
