Application of Fuzzy Time Series Lee to Predict Rice Demand

Oct 13, 2025 by orovajewels.com 90 views

Optimization of Product Inventory Purchases with Fuzzy Time Series Lee and the Fuzzy Sugeno Inference System

Introduction

Inventory management is a crucial aspect of every business, and it can be a significant challenge to manage inventory effectively, especially when facing demand fluctuations. Overstock and out of stock conditions can harm the business, resulting in financial losses and damage to the company's reputation. In this article, we will discuss the optimization of product inventory purchases using Fuzzy Time Series Lee and the Fuzzy Sugeno inference system.

The Importance of Inventory Management

Inventory is an important asset for every business, and managing it appropriately can be a challenge. Overstock and out of stock conditions can harm the business, resulting in financial losses and damage to the company's reputation. For example, CV. The Gema Permata Sejati, a company that deals with rice products, often experiences overstock due to forecasting requests and determining the number of purchases. This is caused by the uncertainty of recurring purchases from consumers.

The Challenge of Demand Fluctuations

Demand fluctuations can be caused by various factors, including changes in consumer behavior, economic conditions, and seasonal variations. These fluctuations can make it difficult to predict demand accurately, leading to overstock or out of stock conditions. In the case of CV. The Gema Permata Sejati, the company often experiences overstock due to forecasting requests and determining the number of purchases.

The Solution: Fuzzy Time Series Lee and the Fuzzy Sugeno Inference System

To overcome the challenge of demand fluctuations, this research uses a combination of two Fuzzy methods, namely the Fuzzy Time Series Lee and the Fuzzy Sugeno inference system. The aim is to determine the optimal purchase of 5 kg, 10 kg, and 30 kg packaged rice, thus minimizing overstock on CV. The Gema Permata Sejati.

Application of Fuzzy Time Series Lee to Predict Rice Demand

Fuzzy Time Series Lee is a Fuzzy-based forecasting method that utilizes historical data patterns to predict future values. In this study, this method was used to predict the demand for packaged rice 5 kg, 10 kg, and 30 kg.

The results show that forecasting using Fuzzy Time Series Lee is quite accurate. Forecasting of 5 kg packaged rice produces 2539 units with a mape of 7.87%, which is categorized as "very good". For 10 kg packaging, forecasting produces 2032 units with 13.30% mape, which is categorized as "good". Meanwhile, forecasting for 30 kg packaging produced 1949 units with a 6.48% mape, back in the "very good" category.

Optimizing the Number of Purchases with the Fuzzy Sugeno Inference System

After getting a request forecast through the Lee Fuzzy Time Series, the next step is to determine the number of optimal purchases. Here, the Fuzzy Sugeno inference system plays an important role. This method helps in decision making by combining expert knowledge and historical data.

The results of the analysis show that the Fuzzy Sugeno inference system can produce effective optimal purchases. For 5 kg packaged rice, the recommended number of optimal purchases is 2313 units with a 3.70% mape ("very good"). For 10 kg packaging, the optimal purchase number is 1742 units with 2.52% mape ("very good"). Finally, for 30 kg packaging, the optimal purchase number is 1086 units with 7.83% mape ("very good").

Benefits and Implications of Application of the Fuzzy Method

The application of the Fuzzy Time Series Lee and the Fuzzy Sugeno inference system provides several benefits for CV. True Permata Guna, namely:

*** Increasing Forecasting Accuracy: ** The use of the fuzzy method helps reduce uncertainty in demand forecasting, resulting in a more accurate estimate. ** Minimizing Overstock: ** By determining the optimal number of purchases, overstock risk can be minimized, thus minimizing losses due to unused stock. ** Operational Efficiency: ** Inventory management that is more optimal to increase operational efficiency by minimizing storage and waste costs.

Conclusion

This study shows that the fuzzy method can be an effective solution in managing inventory. The application of Fuzzy methods in various industries that have uncertainty characteristics can help improve business efficiency and profitability. By using Fuzzy Time Series Lee and the Fuzzy Sugeno inference system, businesses can optimize their inventory purchases, reduce overstock and out of stock conditions, and increase operational efficiency.

Recommendations

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

Businesses should consider using Fuzzy methods to manage their inventory, especially in industries with uncertainty characteristics.
The Fuzzy Time Series Lee and the Fuzzy Sugeno inference system can be used to predict demand and determine the optimal number of purchases.
Businesses should regularly review and update their inventory management systems to ensure that they are using the most effective methods.

Limitations of the Study

This study has several limitations, including:

The study only used historical data to predict demand and determine the optimal number of purchases.
The study did not consider other factors that may affect demand, such as changes in consumer behavior and economic conditions.
The study only used a small sample size, which may not be representative of the larger population.

Future Research Directions

Future research should focus on:

Developing more accurate Fuzzy methods for predicting demand and determining the optimal number of purchases.
Considering other factors that may affect demand, such as changes in consumer behavior and economic conditions.
Using larger sample sizes to ensure that the results are representative of the larger population.

References

Lee, Y. H., & Lee, J. H. (2013). Fuzzy time series forecasting using a novel approach. Journal of Intelligent Information Systems, 40(2), 147-164.
Sugeno, M. (1985). An introductory survey of fuzzy control. Information Sciences, 36(1-2), 59-83.
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353.
Frequently Asked Questions (FAQs) about Optimization of Product Inventory Purchases with Fuzzy Time Series Lee and the Fuzzy Sugeno Inference System

Q: What is Fuzzy Time Series Lee and how does it work?

A: Fuzzy Time Series Lee is a Fuzzy-based forecasting method that utilizes historical data patterns to predict future values. It works by analyzing the relationships between different variables and identifying patterns in the data.

Q: What is the Fuzzy Sugeno Inference System and how does it work?

A: The Fuzzy Sugeno Inference System is a decision-making method that combines expert knowledge and historical data to determine the optimal number of purchases. It works by analyzing the relationships between different variables and identifying patterns in the data.

Q: How does the Fuzzy Time Series Lee and the Fuzzy Sugeno Inference System help in optimizing product inventory purchases?

A: The Fuzzy Time Series Lee and the Fuzzy Sugeno Inference System help in optimizing product inventory purchases by predicting demand and determining the optimal number of purchases. This reduces the risk of overstock and out of stock conditions, and increases operational efficiency.

Q: What are the benefits of using the Fuzzy Time Series Lee and the Fuzzy Sugeno Inference System?

A: The benefits of using the Fuzzy Time Series Lee and the Fuzzy Sugeno Inference System include:

Increasing forecasting accuracy
Minimizing overstock and out of stock conditions
Increasing operational efficiency
Reducing storage and waste costs

Q: What are the limitations of the Fuzzy Time Series Lee and the Fuzzy Sugeno Inference System?

A: The limitations of the Fuzzy Time Series Lee and the Fuzzy Sugeno Inference System include:

The study only used historical data to predict demand and determine the optimal number of purchases
The study did not consider other factors that may affect demand, such as changes in consumer behavior and economic conditions
The study only used a small sample size, which may not be representative of the larger population

Q: How can businesses implement the Fuzzy Time Series Lee and the Fuzzy Sugeno Inference System in their inventory management systems?

A: Businesses can implement the Fuzzy Time Series Lee and the Fuzzy Sugeno Inference System in their inventory management systems by:

Collecting historical data on demand and sales
Analyzing the data using the Fuzzy Time Series Lee and the Fuzzy Sugeno Inference System
Using the results to determine the optimal number of purchases
Regularly reviewing and updating the inventory management system to ensure that it is using the most effective methods

Q: What are the future research directions for the Fuzzy Time Series Lee and the Fuzzy Sugeno Inference System?

A: The future research directions for the Fuzzy Time Series Lee and the Fuzzy Sugeno Inference System include:

Developing more accurate Fuzzy methods for predicting demand and determining the optimal number of purchases
Considering other factors that may affect demand, such as changes in consumer behavior and economic conditions
Using larger sample sizes to ensure that the results are representative of the larger population

Q: What are the implications of the Fuzzy Time Series Lee and the Fuzzy Sugeno Inference System for businesses?

A: The implications of the Fuzzy Time Series Lee and the Fuzzy Sugeno Inference System for businesses include:

Improved forecasting accuracy
Reduced risk of overstock and out of stock conditions
Increased operational efficiency
Reduced storage and waste costs

Q: How can businesses measure the effectiveness of the Fuzzy Time Series Lee and the Fuzzy Sugeno Inference System?

A: Businesses can measure the effectiveness of the Fuzzy Time Series Lee and the Fuzzy Sugeno Inference System by:

Tracking the accuracy of demand forecasts
Monitoring the number of overstock and out of stock conditions
Measuring the reduction in storage and waste costs
Evaluating the impact on operational efficiency

Q: What are the potential applications of the Fuzzy Time Series Lee and the Fuzzy Sugeno Inference System in other industries?

A: The potential applications of the Fuzzy Time Series Lee and the Fuzzy Sugeno Inference System in other industries include:

Supply chain management
Production planning
Quality control
Financial forecasting

Q: What are the potential limitations of the Fuzzy Time Series Lee and the Fuzzy Sugeno Inference System in other industries?

A: The potential limitations of the Fuzzy Time Series Lee and the Fuzzy Sugeno Inference System in other industries include:

The need for large amounts of historical data
The complexity of the Fuzzy methods
The potential for overfitting
The need for regular updates and maintenance of the inventory management system.