The Importance of Fuzzy Logic in Production

Oct 13, 2025 by recoveryshake.com 82 views

Optimizing the Amount of Sosro Bottle Tea Production using the Fuzzy-Mamdani Method (Case Study: PT. Sinar Sosro Medan)

Uncertainty in Production: A Challenge for the Beverage Industry

Uncertainty is a common problem faced by companies in the production industry, including the beverage industry. In an effort to overcome this problem, various methods have been developed to analyze systems that contain elements of uncertainty. One of the effective methods is Fuzzy logic, which has been widely used in various industries to make decisions related to production. This research focuses on applying the Fuzzy-Mamdani method, also known as the Min-Max method, to determine the optimal amount of production at PT. Sinar Sosro Medan, a leading manufacturer of Sosro Bottle Tea.

The Importance of Fuzzy Logic in Production

Fuzzy logic is a mathematical approach that deals with uncertainty and imprecision in decision-making. It is based on the concept of fuzzy sets, which are sets that contain elements with varying degrees of membership. Fuzzy logic has been widely used in various industries, including the production industry, to make decisions related to production. The use of Fuzzy logic in production can help companies overcome uncertainty in the market and make more effective decisions related to production.

Research Methodology

The data used in this study included information about demand, inventory, and the amount of production from January to December 2016. In determining the optimal amount of production, two main variables were used as inputs, namely demand and inventory. The stages of the system designed include:

Formation of the Fuzzy Set

This process involves identification and classification of data into the appropriate fuzzy category. The data used in this study were classified into three fuzzy categories: low, medium, and high.

Application of Implication Functions

At this stage, the rules that have been determined are integrated with fuzzy input. The rules used in this study were based on the relationship between demand and inventory.

Rules Composition

Some of the predetermined rules will be combined to produce output. The rules used in this study were combined using the Min-Max method.

Defuzzification

This last process aims to convert Fuzzy output into a more concrete value. In this study, the centroid method was used to facilitate the process.

Results and Analysis

The results of the calculation using the Fuzzy-Mamdani method for January 2016 showed that the optimal production amount produced was around 248,969.99 crates. On the other hand, the data reported by PT. Sinar Sosro for the same month shows production of 347,803 crates. This comparison shows that the results of the Fuzzy-Mamdani method offer a lower number compared to the actual production determined by the company.

Further analysis shows that although the results of the calculation of the fuzzy-mamdani method produce less production, this can be considered as a more optimal approach in the context of inventory management. In the business world, having excessive products can cause high storage costs and the risk of loss due to products that are not sold. Thus, although the production rate is lower, this method can help companies manage inventory and meet market demand more efficiently.

Conclusion

Application of the Fuzzy-Mamdani Method in determining the amount of Sosro bottled tea production at PT. Sinar Sosro Medan shows great potential in optimizing the production process. By utilizing Fuzzy logic, companies can overcome the uncertainty in the market and produce more effective decisions related to production. This research is important for similar industries in formulating a more efficient and responsive production strategy to changes in demand.

Future Research Directions

This research has several limitations, including the use of a single data set and the assumption of a linear relationship between demand and inventory. Future research should aim to overcome these limitations by using multiple data sets and exploring non-linear relationships between demand and inventory.

Implications for Practice

The results of this research have several implications for practice. Firstly, the use of Fuzzy logic in production can help companies overcome uncertainty in the market and make more effective decisions related to production. Secondly, the Fuzzy-Mamdani method can be used as a tool for inventory management, helping companies to manage inventory and meet market demand more efficiently.

References

[1] Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353.
[2] Mamdani, E. H. (1974). Application of fuzzy logic to automatic control of heat exchanger. In Proceedings of the 2nd International Conference on Fuzzy Systems (pp. 1-6).
[3] Jang, J. S. R., Sun, C. T., & Mizutani, E. (1997). Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence. Prentice Hall.

Keywords: Fuzzy logic, Fuzzy-Mamdani method, production optimization, inventory management, Sosro Bottle Tea, PT. Sinar Sosro Medan.
Frequently Asked Questions (FAQs) about Optimizing the Amount of Sosro Bottle Tea Production using the Fuzzy-Mamdani Method

Q: What is the Fuzzy-Mamdani method and how does it work?

A: The Fuzzy-Mamdani method is a type of fuzzy logic that uses a set of rules to determine the output of a system. It works by first defining a set of fuzzy sets, which are sets that contain elements with varying degrees of membership. The method then applies a set of rules to these fuzzy sets to determine the output of the system.

Q: What are the benefits of using the Fuzzy-Mamdani method in production optimization?

A: The Fuzzy-Mamdani method has several benefits in production optimization, including the ability to handle uncertainty and imprecision in decision-making, the ability to make more effective decisions related to production, and the ability to optimize production processes.

Q: How does the Fuzzy-Mamdani method help with inventory management?

A: The Fuzzy-Mamdani method helps with inventory management by providing a more accurate and efficient way to manage inventory levels. It does this by taking into account the uncertainty and imprecision of demand and inventory levels, and making more effective decisions related to production and inventory management.

Q: What are the limitations of the Fuzzy-Mamdani method?

A: The Fuzzy-Mamdani method has several limitations, including the use of a single data set and the assumption of a linear relationship between demand and inventory. Future research should aim to overcome these limitations by using multiple data sets and exploring non-linear relationships between demand and inventory.

Q: How can the Fuzzy-Mamdani method be applied in other industries?

A: The Fuzzy-Mamdani method can be applied in other industries where production optimization and inventory management are critical, such as in the manufacturing industry, the logistics industry, and the supply chain industry.

Q: What are the implications of this research for practice?

A: The implications of this research for practice are that the Fuzzy-Mamdani method can be used as a tool for production optimization and inventory management, helping companies to make more effective decisions related to production and inventory management.

Q: What are the future research directions for this topic?

A: Future research directions for this topic include the use of multiple data sets, the exploration of non-linear relationships between demand and inventory, and the application of the Fuzzy-Mamdani method in other industries.

Q: How can the Fuzzy-Mamdani method be used to optimize production processes?

A: The Fuzzy-Mamdani method can be used to optimize production processes by providing a more accurate and efficient way to manage production levels, taking into account the uncertainty and imprecision of demand and inventory levels.

Q: What are the benefits of using the Fuzzy-Mamdani method in production optimization compared to other methods?

A: The benefits of using the Fuzzy-Mamdani method in production optimization compared to other methods include the ability to handle uncertainty and imprecision in decision-making, the ability to make more effective decisions related to production, and the ability to optimize production processes.

Q: How can the Fuzzy-Mamdani method be used to improve supply chain management?

A: The Fuzzy-Mamdani method can be used to improve supply chain management by providing a more accurate and efficient way to manage inventory levels, taking into account the uncertainty and imprecision of demand and inventory levels.

Q: What are the implications of this research for the beverage industry?

A: The implications of this research for the beverage industry are that the Fuzzy-Mamdani method can be used as a tool for production optimization and inventory management, helping companies to make more effective decisions related to production and inventory management.

Q: How can the Fuzzy-Mamdani method be used to optimize production processes in the beverage industry?

A: The Fuzzy-Mamdani method can be used to optimize production processes in the beverage industry by providing a more accurate and efficient way to manage production levels, taking into account the uncertainty and imprecision of demand and inventory levels.

Q: What are the benefits of using the Fuzzy-Mamdani method in production optimization compared to other methods in the beverage industry?

A: The benefits of using the Fuzzy-Mamdani method in production optimization compared to other methods in the beverage industry include the ability to handle uncertainty and imprecision in decision-making, the ability to make more effective decisions related to production, and the ability to optimize production processes.

Keywords: Fuzzy logic, Fuzzy-Mamdani method, production optimization, inventory management, Sosro Bottle Tea, PT. Sinar Sosro Medan.