More Accurate Decision Making

Oct 13, 2025 by ludoplayers7.com 0 views

Introduction

PT. Agrojaya Perdana, a leading palm oil processing company, faces significant challenges in managing its raw material inventory. The company's products, in the form of crude palm oil (CPKO), require immediate processing to maintain quality, making it essential to control the number of orders to achieve optimal inventory levels. However, determining the optimal number of orders is complicated due to fluctuating demand and unpredictable raw material arrival times. This research aims to apply the Fuzzy logic method to overcome these uncertainties and optimize the number of orders and inventory levels at PT. Agrojaya Perdana.

Background

PT. Agrojaya Perdana, like many other palm oil processing companies, faces the challenge of managing its raw material inventory. The company's products, CPKO, are highly perishable and require immediate processing to maintain quality. This demands controlling the number of orders to achieve optimal inventory levels. However, determining the optimal number of orders is complicated due to fluctuating demand and unpredictable raw material arrival times.

The data from July 2020 to June 2021 showed an increase in the amount of CPKO inventory, which was potentially detrimental to the company. This highlights the need for an effective inventory control system that can adapt to changing demand and raw material availability.

Methodology

This research applies the Fuzzy logic method to optimize the number of orders and inventory levels at PT. Agrojaya Perdana. Fuzzy logic is a mathematical approach that can handle uncertain and ambiguous data, making it an ideal solution for managing complex systems like inventory control.

The Fuzzy logic system was designed to accommodate the following inputs:

Fluctuating demand
Unpredictable raw material arrival times
Inventory levels

The system was trained using historical data from July 2020 to June 2021, and the results were evaluated to determine the effectiveness of the Fuzzy logic approach.

Results

The results of the study showed a significant decrease in the average number of orders by 4.79% of the total actual order. This decline had a direct impact on inventory levels, which also decreased by an average of 3.46%. These results indicate the optimization of the number of orders and inventory levels, as it is protected from lack of raw materials while minimizing excessive supply of inventory.

Advantages of Fuzzy Logic

The use of Fuzzy logic in controlling inventory at PT. Agrojaya Perdana offers several advantages:

Handling of Uncertainty

Fuzzy logic is able to accommodate uncertain and ambiguous data, such as fluctuations in demand and time of arrival of raw materials, which are the main obstacles in determining the number of orders.

More Accurate Decision Making

Fuzzy logic allows the system to imitate the process of human decision making, which is more flexible and adaptive in dealing with complex situations.

Stock Optimization

The results of the study showed a significant decrease in order and inventory. This has the potential to reduce the cost of storage and the risk of damage to raw materials.

Suggestions for Implementation

PT. Agrojaya Perdana can maximize the benefits of Fuzzy logic with:

System Integration

Applying Fuzzy logic into a company information system to facilitate inventory management and monitoring in real-time inventory.

Continuing Evaluation

Performing periodic evaluation of the performance of the fuzzy logic system and adjusting parameters if needed to increase accuracy and effectiveness.

Further Development

Exploring the potential of Fuzzy's logic to optimize other aspects of supply chains, such as production planning and distribution management.

Conclusion

By applying the Fuzzy logic, PT. Agrojaya Perdana can create a more efficient and effective inventory control system, thereby increasing the profitability and competitiveness of the company. The Fuzzy logic approach offers several advantages, including handling of uncertainty, more accurate decision making, and stock optimization. The company can maximize the benefits of Fuzzy logic by integrating it into its information system, continuing evaluation, and further development.

Recommendations

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

PT. Agrojaya Perdana should implement the Fuzzy logic system in its inventory control process to optimize the number of orders and inventory levels.
The company should continue to evaluate the performance of the Fuzzy logic system and adjust parameters if needed to increase accuracy and effectiveness.
PT. Agrojaya Perdana should explore the potential of Fuzzy's logic to optimize other aspects of supply chains, such as production planning and distribution management.

Limitations

This study has several limitations, including:

The study was conducted using historical data from July 2020 to June 2021, which may not reflect the current market conditions.
The Fuzzy logic system was designed to accommodate specific inputs, which may not be applicable to other companies with different inventory control systems.

Future Research Directions

Future research directions include:

Exploring the potential of Fuzzy's logic to optimize other aspects of supply chains, such as production planning and distribution management.
Developing a more advanced Fuzzy logic system that can accommodate multiple inputs and outputs.
Conducting a case study on other companies to evaluate the effectiveness of Fuzzy logic in inventory control.

Conclusion

In conclusion, this study demonstrates the effectiveness of Fuzzy logic in optimizing the number of orders and inventory levels at PT. Agrojaya Perdana. The Fuzzy logic approach offers several advantages, including handling of uncertainty, more accurate decision making, and stock optimization. The company can maximize the benefits of Fuzzy logic by integrating it into its information system, continuing evaluation, and further development.

Introduction

Fuzzy logic is a mathematical approach that can handle uncertain and ambiguous data, making it an ideal solution for managing complex systems like inventory control. In our previous article, we discussed the application of Fuzzy logic in optimizing the number of orders and inventory levels at PT. Agrojaya Perdana. In this article, we will answer some frequently asked questions (FAQs) about Fuzzy logic in inventory control.

Q: What is Fuzzy logic?

A: Fuzzy logic is a mathematical approach that can handle uncertain and ambiguous data. It is based on the concept of fuzzy sets, which are sets that contain elements with varying degrees of membership.

Q: How does Fuzzy logic work in inventory control?

A: In inventory control, Fuzzy logic is used to determine the optimal number of orders based on uncertain and ambiguous data such as fluctuations in demand and time of arrival of raw materials. The Fuzzy logic system uses a set of rules to evaluate the input data and determine the optimal number of orders.

Q: What are the advantages of using Fuzzy logic in inventory control?

A: The advantages of using Fuzzy logic in inventory control include:

Handling of uncertainty: Fuzzy logic can handle uncertain and ambiguous data, making it an ideal solution for managing complex systems like inventory control.
More accurate decision making: Fuzzy logic allows the system to imitate the process of human decision making, which is more flexible and adaptive in dealing with complex situations.
Stock optimization: Fuzzy logic can optimize the number of orders and inventory levels, reducing the risk of stockouts and overstocking.

Q: How can Fuzzy logic be integrated into an existing inventory control system?

A: Fuzzy logic can be integrated into an existing inventory control system by:

Developing a Fuzzy logic module that can be integrated into the existing system.
Using a Fuzzy logic library or toolkit to develop the Fuzzy logic module.
Training the Fuzzy logic system using historical data from the existing system.

Q: What are the limitations of Fuzzy logic in inventory control?

A: The limitations of Fuzzy logic in inventory control include:

Complexity: Fuzzy logic can be complex to implement and require significant expertise.
Data quality: Fuzzy logic requires high-quality data to produce accurate results.
Scalability: Fuzzy logic can be difficult to scale to large datasets.

Q: Can Fuzzy logic be used in other areas of supply chain management?

A: Yes, Fuzzy logic can be used in other areas of supply chain management such as:

Production planning: Fuzzy logic can be used to optimize production planning by taking into account uncertain and ambiguous data such as demand fluctuations and production capacity.
Distribution management: Fuzzy logic can be used to optimize distribution management by taking into account uncertain and ambiguous data such as transportation delays and inventory levels.

Q: What are the future research directions for Fuzzy logic in inventory control?

A: Future research directions for Fuzzy logic in inventory control include:

Developing more advanced Fuzzy logic systems that can handle multiple inputs and outputs.
Exploring the use of Fuzzy logic in other areas of supply chain management such as production planning and distribution management.
Developing Fuzzy logic systems that can learn from data and adapt to changing conditions.

Conclusion

In conclusion, Fuzzy logic is a powerful tool for managing complex systems like inventory control. By answering some frequently asked questions (FAQs) about Fuzzy logic in inventory control, we hope to have provided a better understanding of the benefits and limitations of Fuzzy logic in this area.