ML & Predictive Analytics for Supplier Selection
Read how we helped our client halve the time spent on research and supplier selection. We had leveraged a hybrid approach to reduce risk - the most complicated cases were automatically flagged for manual verification.
Background
Free Up Your Time
With both a great number of suppliers and a high number of variables, it can be quite overwhelming for a human to make such an analysis and decision, and often some vital information might be missed. Selecting the supplier and maintaining a proper relationship can be difficult.
We leveraged machine learning to reduce the time spent on research and risk assessment from 5 hours to 3 hours a day on average.
Value Delivered
From 5 hours to 3 hours
On average, for an individual, the time required for research and risk assessment was cut down from 5 hours to 3 hours a day.
Greater precision
A much higher number of suppliers is automatically compared. As a result, helpful, non-obvious patterns can be found.
Informed Assessment
The solution allows our client to predict outcomes based on historical data, enabling him to make better-informed decision.
Business Problem
Our client wanted to create a platform that would allow him to compare and choose suppliers, maintain them, then continuously assess and manage risk in a more informed manner. The main business goal here was to allocate less time for research and prevent potential financial losses that could be caused by inadequate supplier selection.
A crucial part of purchasing management and enhancing supply chain sustainability is supplier selection. This can be a complicated process with any number of variables to consider. Also, it can be increasingly difficult with the growing number of available suppliers.
With both a great number of suppliers and a high number of variables, it can be quite overwhelming for a human to make such an analysis and decision, and often some vital information might be missed. Selecting the supplier and maintaining a proper relationship can be difficult.
Analysis
Risks To Consider
There is a significant amount of supplier-related risks that have to be taken into consideration. These risks types include:
- Financial: Risk involved with using a third party supplier. There can always be a problem of tax changes, bankruptcy, or price change. If the supplier does not meet expectations, you face financial losses. A supplier overseas means fluctuating foreign exchange rates.
- Delivery: include government or economic issues, natural disasters, etc. Such risk cannot be predicted and avoided, and they can prevent a supplier from delivering goods.
- Compliance: it is essential to ensure that a company you choose is compliant with standards and regulations. Otherwise, you can face fines.
- Image: it is also vital to ensure that the company that you choose is ethical and professional.
- Quality: quality and performance supplier risk may involve over-promising and under-delivering, violating contracts.
Implementation
Choosing Variables
- product price – most basic variable, which everyone takes into account
- tariff and taxes – if the supplier is from another country it is essential to know government regulations
- product quality
- percentage of faulty items
- delivery performance
- stable delivery
- lead time
- flexibility
- responsiveness and customer response
- commitment to quality
- The financial position of the company,
- Market reputation
- Geographic location
- The political stability of the country
- Exchange rates
- Environmental factors
“With both a great number of suppliers and a high number of variables, it can be quite overwhelming for a human to make such an analysis and decision, and often some vital information might be missed.”
Solution
The big picture here was to leverage machine learning to make the supplier selection process more predictable and intelligible. We created a platform providing the best scenarios for supplier selection and risk management, based on parameters that our client found the most important, such as product price or lead time. The solution serves as an augmentation of the whole process with a final decision made by a human.
The system has two main components: the prediction engine and the recommendation engine.
Data sets generated from SRM actions, such as supplier assessments, audits, and credit scoring, provide an important basis for further decisions regarding a supplier.
Conclusion
Our AI solution made the process of supplier selection much easier. On average, for an individual, the time required for research and risk assessment was cut down from 5 hours to 3 hours a day. A much higher number of suppliers is automatically compared. As a result, helpful, non-obvious patterns can be found.
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