This result shows that the decision support analysis model is effective in optimizing the control of imported raw material inventory

Oct 13, 2025 by thesweetdesigns.com 26 views

Optimizing Control of Imported Raw Material Inventory: Solution for PT. Mabar Feed Indonesia

The smooth running of the production process is the main key in achieving profits for each company. This is closely related to optimizing the inventory of raw materials. Excessive supply of raw materials lead to high storage costs, while lack of raw materials can cause disruption of production and loss of opportunities to meet consumer demand. Effective inventory management is crucial for companies like PT. Mabar Feed Indonesia, which operates in the chicken feed industry. The company faces challenges in providing adequate raw materials, and its inventory control systems often rely on past experiences without considering demand fluctuations.

PT. Mabar Feed Indonesia's inventory control systems are often inefficient, resulting in a lack of imported raw materials. This shows the inefficiency of decision making based on inaccurate data, thus impacting the inability to meet consumer demand. The company's inability to meet consumer demand can lead to a loss of revenue and damage to its reputation. To overcome this problem, PT. Mabar Feed Indonesia requires a more comprehensive analysis system. Through the application of the decision support analysis model, the company can optimize the control of imported raw material inventory.

Decision Support Analysis Model: A Solution for PT. Mabar Feed Indonesia

The decision support analysis model applied in this study combines historical data, estimated demand, and various factors related to inventory costs, such as storage costs, ordering costs, and inventory lack costs. This model is then implemented through a decision support system that is integrated in the form of a dashboard. The dashboard is designed to display visualization of data that is easily understood, thus facilitating management in making decisions. The model helps determine the frequency and amount of ordering raw materials by considering the total inventory costs per year and the remaining stock after ordering.

Implementation Results: A Case Study of PT. Mabar Feed Indonesia

Based on the model applied, PT. Mabar Feed Indonesia can order once a month with 2,920,000 kg. The total inventory costs per year are estimated to reach Rp 23,730,791,997.46, with an average of 2,682,273 kg remaining stock. The total inventory costs produced are lower than other alternatives, while the remaining stock is above the reorder point and safety stock. This result shows that the decision support analysis model is effective in optimizing the control of imported raw material inventory. PT. Mabar Feed Indonesia can minimize the risk of shortage of stock and avoid waste of storage costs.

Benefits of Decision Support Analysis Model: A Case Study of PT. Mabar Feed Indonesia

This analysis model provides a number of benefits for PT. Mabar Feed Indonesia, including:

*** More accurate and structured decision making. ** This model helps the company in determining optimal raw material procurement strategies, based on comprehensive data and considerations**. ** Efficiency of inventory costs. ** This model helps minimize storage costs, booking costs, and cost of stock shortages, thereby increasing the company's operational efficiency**. *** Increasing customer satisfaction. ** By minimizing the risk of stock shortages, PT. Mabar Feed Indonesia can ensure the smooth production process and meet customer demand in a timely manner**.

Conclusion: The Effectiveness of Decision Support Analysis Model in PT. Mabar Feed Indonesia

Application of Decision Support Analysis Models in Control of Imported Raw Material Inventory Proven effective in increasing the efficiency and operational effectiveness of PT. Mabar Feed Indonesia. This model provides a strong foundation for companies to make the right decisions, so as to create a smooth production process and avoid the risk of shortage of stock. The decision support analysis model is a solution for companies like PT. Mabar Feed Indonesia, which operates in the chicken feed industry, to optimize the control of imported raw material inventory.

Recommendations for Future Research

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

Further research is needed to explore the application of decision support analysis models in other industries.
The model can be modified to include other factors, such as supplier performance and transportation costs.
The study can be replicated in other companies to validate the results and provide more generalizable findings.

Limitations of the Study

This study has several limitations, including:

The study is based on a single case study, which may not be representative of other companies.
The model is based on historical data, which may not reflect future demand and supply conditions.
The study does not consider other factors, such as supplier performance and transportation costs.

Future Directions

The decision support analysis model has the potential to be applied in other industries, such as manufacturing and retail. The model can be modified to include other factors, such as supplier performance and transportation costs. The study can be replicated in other companies to validate the results and provide more generalizable findings. The model can also be used to optimize other business processes, such as supply chain management and logistics.

Conclusion

In conclusion, the decision support analysis model is a solution for companies like PT. Mabar Feed Indonesia, which operates in the chicken feed industry, to optimize the control of imported raw material inventory. The model provides a strong foundation for companies to make the right decisions, so as to create a smooth production process and avoid the risk of shortage of stock. The study provides a number of benefits, including more accurate and structured decision making, efficiency of inventory costs, and increasing customer satisfaction. The model can be modified to include other factors, such as supplier performance and transportation costs. The study can be replicated in other companies to validate the results and provide more generalizable findings.
Frequently Asked Questions (FAQs) about Decision Support Analysis Model for PT. Mabar Feed Indonesia

In this article, we will answer some of the frequently asked questions about the decision support analysis model for PT. Mabar Feed Indonesia.

Q: What is the decision support analysis model?

A: The decision support analysis model is a mathematical model that combines historical data, estimated demand, and various factors related to inventory costs, such as storage costs, ordering costs, and inventory lack costs. This model is then implemented through a decision support system that is integrated in the form of a dashboard.

Q: What are the benefits of using the decision support analysis model?

A: The decision support analysis model provides a number of benefits, including more accurate and structured decision making, efficiency of inventory costs, and increasing customer satisfaction. By minimizing the risk of stock shortages, PT. Mabar Feed Indonesia can ensure the smooth production process and meet customer demand in a timely manner.

Q: How does the decision support analysis model work?

A: The decision support analysis model works by combining historical data, estimated demand, and various factors related to inventory costs. This model is then implemented through a decision support system that is integrated in the form of a dashboard. The dashboard is designed to display visualization of data that is easily understood, thus facilitating management in making decisions.

Q: What are the limitations of the decision support analysis model?

A: The decision support analysis model has several limitations, including the study is based on a single case study, which may not be representative of other companies. The model is based on historical data, which may not reflect future demand and supply conditions. The study does not consider other factors, such as supplier performance and transportation costs.

Q: Can the decision support analysis model be applied in other industries?

A: Yes, the decision support analysis model can be applied in other industries, such as manufacturing and retail. The model can be modified to include other factors, such as supplier performance and transportation costs.

Q: What are the future directions of the decision support analysis model?

A: The decision support analysis model has the potential to be applied in other industries, such as manufacturing and retail. The model can be modified to include other factors, such as supplier performance and transportation costs. The study can be replicated in other companies to validate the results and provide more generalizable findings.

Q: How can the decision support analysis model be implemented in PT. Mabar Feed Indonesia?

A: The decision support analysis model can be implemented in PT. Mabar Feed Indonesia by following these steps:

Collect historical data: Collect historical data on inventory levels, demand, and costs.
Estimate demand: Estimate future demand based on historical data and other factors.
Implement the model: Implement the decision support analysis model through a decision support system that is integrated in the form of a dashboard.
Monitor and adjust: Monitor the performance of the model and adjust it as needed to ensure that it continues to provide accurate and useful insights.

Q: What are the costs associated with implementing the decision support analysis model?

A: The costs associated with implementing the decision support analysis model will depend on the specific requirements of PT. Mabar Feed Indonesia. However, the costs are likely to include:

Data collection: The cost of collecting historical data and estimating future demand.
Model development: The cost of developing and implementing the decision support analysis model.
Training: The cost of training personnel to use the decision support analysis model.
Maintenance: The cost of maintaining and updating the decision support analysis model.

Conclusion