Formation of basic rules of fuzzy

Oct 13, 2025 by leolavanderia.com.br 25 views

Application of the Fuzzy Sugeno Method in Determining the 2016 Cigarette Paper Inventory (Case Study: PT. Pusaka Prima Mandiri (PPM))

Introduction

In today's fast-paced business environment, companies face numerous challenges in making informed decisions, particularly in inventory management. One such challenge is determining the optimal amount of paper inventory, which is crucial for the production of cigarettes. PT. Pusaka Prima Mandiri, a leading cigarette manufacturer, has been grappling with this issue. The uncertainty in demand and production is one of the factors that complicate the decision-making process. To overcome this problem, the Fuzzy logic method, specifically the Fuzzy Sugeno approach, is used as a solution that can handle uncertainty in the inventory system.

The Importance of Inventory Management in Cigarette Production

Inventory management is a critical aspect of cigarette production, as it directly affects the company's operational efficiency and overall performance. The optimal amount of paper inventory is essential to ensure that production can meet demand while minimizing the risk of stockouts or excess stock. However, determining this optimal amount is a complex task, as it involves considering various factors such as demand, production, and market uncertainty.

The Fuzzy Sugeno Method: A Solution to Inventory Management Challenges

The Fuzzy Sugeno method is a type of fuzzy logic approach that is widely used in decision-making applications, including inventory management. This method is particularly useful in handling uncertainty in data, which is a common challenge in inventory management. The Fuzzy Sugeno approach involves several key stages:

Fuzzification: This process converts numerical input into fuzzy values, allowing for the understanding of uncertainty in data.
Formation of basic rules of fuzzy: Rules are made based on experience and knowledge regarding the relationship between demand, production, and inventory.
Affirmation (Defuzzification): This last stage aims to convert fuzzy output into a crisp value that can be understood and applied directly in operational decisions.

Case Study: PT. Pusaka Prima Mandiri

In this study, the data analyzed included production data, demand, and paper stock from January to December 2016. The Fuzzy Sugeno approach was designed through the key stages mentioned above. The results of the analysis showed that the recommended amount of paper inventory in January was 275 tons. However, the actual data from PT. Pusaka Prima Mandiri showed that the inventory of paper in the same month reached 395 tons.

Analysis Results

The comparison between the results obtained from the Fuzzy Sugeno method and the actual data from PT. Pusaka Prima Mandiri shows a significant difference. However, the results obtained from the Fuzzy Sugeno method are more optimal because they are able to handle the variables of uncertainty that may not be taken into account in a conventional approach. This approach allows companies to make better decisions in dealing with market uncertainty, especially in the aspect of inventory management.

Conclusion

The use of the Fuzzy Sugeno method in determining paper inventory at PT. Pusaka Prima Mandiri is proven to provide more appropriate results despite differences with realization data. This approach allows companies to make better decisions in dealing with market uncertainty, especially in the aspect of inventory management. Through a deeper analysis using Fuzzy logic, companies can minimize the risk of shortcomings or excess stock, thereby increasing operational efficiency and overall performance.

Recommendations

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

Implement the Fuzzy Sugeno method: Companies can implement the Fuzzy Sugeno method to determine the optimal amount of paper inventory.
Use fuzzy logic in decision-making: Companies can use fuzzy logic in decision-making to handle uncertainty in data.
Monitor and adjust inventory levels: Companies can monitor and adjust inventory levels regularly to ensure that production can meet demand while minimizing the risk of stockouts or excess stock.

Limitations of the Study

This study has several limitations, including:

Limited data: The study only analyzed data from January to December 2016.
Simplification of the Fuzzy Sugeno method: The study simplified the Fuzzy Sugeno method to make it easier to understand and apply.
Limited scope: The study only focused on the application of the Fuzzy Sugeno method in determining paper inventory.

Future Research Directions

Future research can build on the findings of this study by:

Extending the scope: Extending the scope of the study to include other aspects of inventory management.
Using more complex fuzzy logic approaches: Using more complex fuzzy logic approaches, such as the Fuzzy Mamdani method.
Analyzing data from multiple sources: Analyzing data from multiple sources to improve the accuracy of the results.

Conclusion

In conclusion, the application of the Fuzzy Sugeno method in determining the 2016 cigarette paper inventory at PT. Pusaka Prima Mandiri is a valuable contribution to the field of inventory management. This approach allows companies to make better decisions in dealing with market uncertainty, especially in the aspect of inventory management. Through a deeper analysis using Fuzzy logic, companies can minimize the risk of shortcomings or excess stock, thereby increasing operational efficiency and overall performance.
Frequently Asked Questions (FAQs) about the Application of the Fuzzy Sugeno Method in Determining the 2016 Cigarette Paper Inventory

Q: What is the Fuzzy Sugeno method?

A: The Fuzzy Sugeno method is a type of fuzzy logic approach that is widely used in decision-making applications, including inventory management. It involves several key stages, including fuzzification, formation of basic rules of fuzzy, and affirmation (defuzzification).

Q: What is the main advantage of using the Fuzzy Sugeno method in inventory management?

A: The main advantage of using the Fuzzy Sugeno method in inventory management is its ability to handle uncertainty in data. This is particularly useful in inventory management, where uncertainty in demand and production is common.

Q: How does the Fuzzy Sugeno method determine the optimal amount of paper inventory?

A: The Fuzzy Sugeno method determines the optimal amount of paper inventory by analyzing production data, demand, and paper stock from January to December 2016. It uses fuzzy logic to handle uncertainty in data and provides a recommended amount of paper inventory.

Q: What are the limitations of the Fuzzy Sugeno method?

A: The Fuzzy Sugeno method has several limitations, including limited data, simplification of the method, and limited scope. Future research can build on the findings of this study by extending the scope, using more complex fuzzy logic approaches, and analyzing data from multiple sources.

Q: Can the Fuzzy Sugeno method be used in other industries besides cigarette production?

A: Yes, the Fuzzy Sugeno method can be used in other industries besides cigarette production. It can be applied in any industry where inventory management is critical and uncertainty in data is common.

Q: How can companies implement the Fuzzy Sugeno method in their inventory management systems?

A: Companies can implement the Fuzzy Sugeno method in their inventory management systems by using fuzzy logic software or by hiring experts in fuzzy logic. They can also use online resources and tutorials to learn more about the method.

Q: What are the benefits of using the Fuzzy Sugeno method in inventory management?

A: The benefits of using the Fuzzy Sugeno method in inventory management include:

Improved decision-making
Reduced risk of stockouts or excess stock
Increased operational efficiency
Improved overall performance

Q: Can the Fuzzy Sugeno method be used in conjunction with other inventory management methods?

A: Yes, the Fuzzy Sugeno method can be used in conjunction with other inventory management methods, such as the Economic Order Quantity (EOQ) method. This can provide a more comprehensive approach to inventory management.

Q: How can companies evaluate the effectiveness of the Fuzzy Sugeno method in their inventory management systems?

A: Companies can evaluate the effectiveness of the Fuzzy Sugeno method in their inventory management systems by tracking key performance indicators (KPIs) such as inventory levels, stockouts, and excess stock. They can also conduct regular audits and reviews to ensure that the method is working effectively.

Q: What are the future research directions for the Fuzzy Sugeno method in inventory management?

A: Future research directions for the Fuzzy Sugeno method in inventory management include:

Extending the scope of the method to include other aspects of inventory management
Using more complex fuzzy logic approaches
Analyzing data from multiple sources
Developing new fuzzy logic algorithms for inventory management

Conclusion

In conclusion, the Fuzzy Sugeno method is a valuable tool for inventory management that can help companies make better decisions in dealing with market uncertainty. By understanding the FAQs about the method, companies can implement it in their inventory management systems and improve their operational efficiency and overall performance.