Performance Appraisal and Determination of Supplier Selection Models Using the Analytical Hierarchy Process and Data Mining Techniques

Oct 13, 2025 by magicalceylontours.com 3 views

Performance Appraisal and Determination of Supplier Selection Models Using the Analytical Hierarchy Process and Data Mining Techniques

Introduction

In today's competitive business landscape, companies are constantly seeking ways to improve their supply chain management and optimize their supplier selection processes. One of the key challenges faced by companies is the effective appraisal of supplier performance and the selection of the right suppliers. This is particularly true for companies like PT. QRS, a Car Spare Parts Distributor Company, which faces obstacles in managing supplier performance, including delays in shipping and high levels of disability spare parts received.

The Problem Statement

PT. QRS requires a comprehensive supplier performance appraisal system and accurate supplier selection model to overcome the challenges it faces. The company needs a solution that can assess supplier performance based on relevant criteria and sub-criteria, classify suppliers into different categories, and select the right suppliers with a high level of accuracy.

The Analytical Hierarchy Process (AHP) Method

The Analytical Hierarchy Process (AHP) method is a decision-making tool that can be used to assess supplier performance based on a set of criteria and sub-criteria. In this study, AHP is used to assess supplier performance based on 5 criteria and 22 relevant sub-criteria. The assessment results are then classified based on the Traffic Light System approach, which divides suppliers into three categories: * Green light * (very good performance), * Yellow light * (good performance), and * Red Light * (Performance needs to be increased).

The Results of the AHP Analysis

The results of the AHP analysis show that there are 2 suppliers with *green light *, 4 suppliers with *yellow light *, and 1 supplier with *red light *. This classification provides a clear and easy-to-understand visualization of the level of supplier qualifications, allowing PT. QRS to identify the suppliers that need improvement.

Data Mining Techniques

In addition to AHP, this study also uses data mining techniques to form a supplier selection model. Historical data purchasing spare parts from the six suppliers is processed using two methods: *Decision Tree *and *Naïve Bayes *. This process is carried out with the help of rapidminer software. The * Decision Tree * method produces a decision tree model with an accuracy rate of 93.75%, while the * Naïve Bayes * method produces a probabilistic model with an accuracy rate of 81.25%.

Comparison of Data Mining Methods

Based on this comparison, the * Decision Tree * method is considered superior as a supplier selection model for PT. QRS. This is because the level of accuracy is higher, thus allowing companies to choose the right supplier with more precision.

Benefits and Advantages of AHP Integrated Solutions and Data Mining

The use of AHP integrated solutions and data mining has several benefits and advantages, including:

*** Comprehensive performance appraisal: ** AHP allows Supplier Performance Assessment in a structured and measurable manner based on relevant criteria and sub -criteria. *** CLEAR CLASSIFICATION SYSTEM: ** TRAFFIC LIGHT SYSTEM provides an easy -to -understand visualization regarding the level of supplier qualifications. *** Accurate supplier selection: ** Supplier selection model resulting from data mining (specifically the Decision Tree method) helps PT. QRS chooses the right supplier with a high level of accuracy. *** Increased efficiency and effectiveness: ** This integrated solution helps PT. QRS to increase efficiency and effectiveness in choosing suppliers, so as to minimize late delivery and spare part disability levels.

Conclusion

The use of AHP integrated solutions and data mining has proven effective in determining the level of performance qualifications and supplier selection models for PT. QRS. The * Decision Tree * method resulting from data mining techniques offers the right solution for the selection of suppliers with a high level of accuracy. The application of this solution has the potential to improve the performance of the supply chain of PT. QRS, minimize the obstacles faced, and achieve the company's business goals more effectively.

Recommendations

Based on the findings of this study, the following recommendations are made:

PT. QRS should continue to use the AHP integrated solution and data mining techniques to assess supplier performance and select the right suppliers.
The company should also consider using other data mining methods, such as clustering and association rule mining, to further improve the accuracy of the supplier selection model.
The company should also consider implementing a supplier development program to help improve the performance of suppliers that are classified as *red light *.

Limitations of the Study

This study has several limitations, including:

The study only used data from PT. QRS and may not be generalizable to other companies.
The study only used two data mining methods and may not have considered other methods that could have been used.
The study only considered supplier performance and may not have considered other factors that could have been used to select suppliers.

Future Research Directions

Future research should consider the following directions:

Developing a more comprehensive supplier performance appraisal system that considers other factors, such as supplier risk and supplier sustainability.
Using other data mining methods, such as clustering and association rule mining, to further improve the accuracy of the supplier selection model.
Implementing a supplier development program to help improve the performance of suppliers that are classified as *red light *.

References

[List of references cited in the study]

Appendix

[Appendix containing additional information, such as the AHP hierarchy and the data mining models used in the study]
Performance Appraisal and Determination of Supplier Selection Models Using the Analytical Hierarchy Process and Data Mining Techniques: Q&A

Introduction

In our previous article, we discussed the use of the Analytical Hierarchy Process (AHP) and data mining techniques to determine supplier performance and select the right suppliers for PT. QRS, a Car Spare Parts Distributor Company. In this article, we will answer some of the frequently asked questions (FAQs) related to this topic.

Q: What is the Analytical Hierarchy Process (AHP)?

A: The Analytical Hierarchy Process (AHP) is a decision-making tool that can be used to assess supplier performance based on a set of criteria and sub-criteria. It is a structured approach to decision-making that involves breaking down complex problems into smaller, more manageable parts.

Q: How does AHP work?

A: AHP works by creating a hierarchy of criteria and sub-criteria that are used to evaluate supplier performance. The hierarchy is typically structured as follows:

Goal (e.g. selecting the right supplier)
Criteria (e.g. price, quality, delivery time)
Sub-criteria (e.g. price per unit, quality rating, delivery time)

Q: What are the benefits of using AHP?

A: The benefits of using AHP include:

Improved decision-making: AHP provides a structured approach to decision-making that helps to ensure that all relevant criteria are considered.
Increased accuracy: AHP helps to reduce the risk of errors and biases in decision-making.
Improved communication: AHP provides a clear and transparent way of communicating the decision-making process to stakeholders.

Q: What is data mining?

A: Data mining is the process of discovering patterns and relationships in large datasets. It involves using statistical and mathematical techniques to analyze data and identify trends and correlations.

Q: How does data mining relate to supplier selection?

A: Data mining can be used to analyze historical data on supplier performance and identify patterns and relationships that can be used to inform supplier selection decisions.

Q: What are some common data mining techniques used in supplier selection?

A: Some common data mining techniques used in supplier selection include:

Decision trees: These are tree-like models that are used to classify data into different categories.
Naïve Bayes: These are probabilistic models that are used to classify data into different categories.
Clustering: This is a technique that is used to group similar data points together.

Q: What are the benefits of using data mining in supplier selection?

A: The benefits of using data mining in supplier selection include:

Improved accuracy: Data mining can help to identify patterns and relationships in data that may not be apparent through other means.
Increased efficiency: Data mining can help to automate the supplier selection process and reduce the time and effort required to make decisions.
Improved decision-making: Data mining can provide insights and recommendations that can inform supplier selection decisions.

Q: How can AHP and data mining be used together in supplier selection?

A: AHP and data mining can be used together in supplier selection by using AHP to identify the most important criteria and sub-criteria for supplier selection, and then using data mining to analyze historical data on supplier performance and identify patterns and relationships that can be used to inform supplier selection decisions.

Q: What are some common challenges associated with using AHP and data mining in supplier selection?

A: Some common challenges associated with using AHP and data mining in supplier selection include:

Data quality: Poor data quality can make it difficult to analyze data and identify patterns and relationships.
Complexity: AHP and data mining can be complex and require specialized expertise to implement.
Cost: AHP and data mining can be expensive to implement and maintain.

Q: How can these challenges be overcome?

A: These challenges can be overcome by:

Ensuring that data is accurate and complete.
Using simple and intuitive AHP and data mining models.
Investing in training and development to build expertise in AHP and data mining.
Using cloud-based solutions to reduce costs and improve scalability.

Conclusion

In conclusion, AHP and data mining can be powerful tools for supplier selection. By using AHP to identify the most important criteria and sub-criteria for supplier selection, and then using data mining to analyze historical data on supplier performance, companies can make more informed and accurate supplier selection decisions. However, there are also challenges associated with using AHP and data mining, and these must be carefully considered and addressed in order to achieve success.