Due to COVID-19, not only the global aerospace industry is experiencing demand stagnation, but supply chains around the world have collapsed. The war in Ukraine not only undermined free trade, but also made it difficult to supply natural resources such as gas. Because tensions are rising between wor...
Due to COVID-19, not only the global aerospace industry is experiencing demand stagnation, but supply chains around the world have collapsed. The war in Ukraine not only undermined free trade, but also made it difficult to supply natural resources such as gas. Because tensions are rising between world powers. With the era of the 4th Industrial Revolution, global companies are accelerating digital transformation to improve productivity. Competition between countries in the aerospace industry has intensified. This is because low-wage countries such as China and India are entering the aerospace market. Many companies in Korea are also promoting digital transformation, but it is difficult for the aerospace manufacturing industry to change rapidly due to high wages and labor-intensive environments. And cost reduction efforts have reached their limits.
K-Company, which leads the aerospace industry in Korea, also has the following problems in the manufacturing field. First, it is necessary to develop an export market by strengthening cost competitiveness, but it is difficult to overcome because it has reached its limit. Second, it is necessary to improve process operations and build a supply chain, but production innovation is difficult due to complex processes and fear of culture change. Third, inventory costs increase, but production material shortages continue to occur, making it difficult to respond to changes in production scheduling. Fourth, activities based on personal judgment affect production scheduling. These activities lead to human error, which is caused by distrust of production system information. Fifth, production scheduling is unstable due to poor production systems of subcontractors, making it difficult to collect and analyze production historical information.
The purpose of this study is to eliminate waste factors such as overproduction and underproduction and to develop an autonomous manufacturing process based on an efficient production system and robust production historical data. For a case study, we investigated the problems of K-company production system. This paper aims to develop a rule-based production scheduling system through a case study of K-Company.
This study was conducted in the following way for the development of an intelligent production scheduling system. 1) In this paper, an MTA (Make To Availability) based intelligent production scheduling system architecture was designed. 2) We collected a total of 33 million transaction data through K-company's ERP system from April 2018 to February 2022. The collected data was extracted from the ERP, APS, DBR, MES, and KAPS systems operated by K company. This information includes standard information such as material master, production order historical data, process work historical data, purchase order, material stock information, material type, and lead time created over the past 47 months. 3) We not only identified key variables in production schedules, but also developed variable optimization rules based on cumulative production historical data. 4) The normality of the data was verified using the Shapiro-Wilks Test, a traditional statistical technique. Data preprocessing used quartile and z-score methods to remove outliers. 5) The initial value of the analysis was set using the Magic Seven method and the Min-Max centroid method of the fuzzy technique, as well as the anchoring and adjustment heuristic method and the exponential smoothing method. Finally, we developed a simulation system to verify the improvement of material shortage reduction rate and production schedule compliance rate, which are key production indicators.
As a result of this study, after applying the variable optimization rule, in the case of K-company, the production lead time, a major variable, was found to be reduced by 5.4%. It was confirmed that the production lead time of subcontractors was also reduced by 2.4%. A production expert diagnosed it as a very dramatic improvement. Compared to the result of reducing 2.7 Mday when promoting 217 tasks targeting only specific processes in the past K-Company production innovation, the 1.5 Mday reduction was evaluated as a dramatic result achieved in a short period of time for all manufacturing products in 2022. It was verified that the MTA-based production scheduling technique improved at least 10% over the production scheduling compliance rate of the existing ERP system.
The theoretical implications can be meaningful in that the MTA (Make To Availability) technique of the existing literature study was extended to propose an advanced production priority concept based on appropriate inventory. Another theoretical implication is that the periodic Dynamic Rolling-up Optimization Method proposed a methodology that can be used when establishing production scheduling by automatically updating data collected from the production site.
The practical implication is that Dynamic Rolling-up Optimization Rules provide guidelines for companies using ERP systems to leverage production historical information to control production scheduling. The study results of the optimization rules we developed contributed to laying the groundwork for scientific and objective research on production scheduling. We emphasize that not only planning is important, but also execution to achieve goals. Based on appropriate inventory, the MTA production priority application methodology will be very attractive not only to the person in charge of production scheduling, but also to the person in charge of business management and Information System management.
However, in order for this methodology to be effective, it is recommended to carry out the following. First, measure a financial metric like inventory cost tied to revenue. Manage these financial metrics to drive productivity improvements. Second, use historical data from production to continuously improve Dynamic Rolling-up Optimization Rules. Cultivate rule creators who can analyze and measure data. Third, seek ways to reduce set-up time for each process as well as discovering improvement requirements for process stabilization continuously. Fourth, it is necessary to establish a system that can manage the entire supply chain process. Finally, develop and upgrade intelligent automation process technology and upgrade analysis algorithms.
This paper will be used as a reference guide for companies and practitioners who continuously utilize robust data in establishing production scheduling through the key driver variable optimization rule method. In addition, if the production priority-based MTA technique, which is the result of this study, is well applied to each company's environment, productivity will be greatly improved.
keywords : Production Scheduling, Make To Availability, Manufacturing Priority, Production Key Variables, Dynamic Rolling-up Optimization
Due to COVID-19, not only the global aerospace industry is experiencing demand stagnation, but supply chains around the world have collapsed. The war in Ukraine not only undermined free trade, but also made it difficult to supply natural resources such as gas. Because tensions are rising between world powers. With the era of the 4th Industrial Revolution, global companies are accelerating digital transformation to improve productivity. Competition between countries in the aerospace industry has intensified. This is because low-wage countries such as China and India are entering the aerospace market. Many companies in Korea are also promoting digital transformation, but it is difficult for the aerospace manufacturing industry to change rapidly due to high wages and labor-intensive environments. And cost reduction efforts have reached their limits.
K-Company, which leads the aerospace industry in Korea, also has the following problems in the manufacturing field. First, it is necessary to develop an export market by strengthening cost competitiveness, but it is difficult to overcome because it has reached its limit. Second, it is necessary to improve process operations and build a supply chain, but production innovation is difficult due to complex processes and fear of culture change. Third, inventory costs increase, but production material shortages continue to occur, making it difficult to respond to changes in production scheduling. Fourth, activities based on personal judgment affect production scheduling. These activities lead to human error, which is caused by distrust of production system information. Fifth, production scheduling is unstable due to poor production systems of subcontractors, making it difficult to collect and analyze production historical information.
The purpose of this study is to eliminate waste factors such as overproduction and underproduction and to develop an autonomous manufacturing process based on an efficient production system and robust production historical data. For a case study, we investigated the problems of K-company production system. This paper aims to develop a rule-based production scheduling system through a case study of K-Company.
This study was conducted in the following way for the development of an intelligent production scheduling system. 1) In this paper, an MTA (Make To Availability) based intelligent production scheduling system architecture was designed. 2) We collected a total of 33 million transaction data through K-company's ERP system from April 2018 to February 2022. The collected data was extracted from the ERP, APS, DBR, MES, and KAPS systems operated by K company. This information includes standard information such as material master, production order historical data, process work historical data, purchase order, material stock information, material type, and lead time created over the past 47 months. 3) We not only identified key variables in production schedules, but also developed variable optimization rules based on cumulative production historical data. 4) The normality of the data was verified using the Shapiro-Wilks Test, a traditional statistical technique. Data preprocessing used quartile and z-score methods to remove outliers. 5) The initial value of the analysis was set using the Magic Seven method and the Min-Max centroid method of the fuzzy technique, as well as the anchoring and adjustment heuristic method and the exponential smoothing method. Finally, we developed a simulation system to verify the improvement of material shortage reduction rate and production schedule compliance rate, which are key production indicators.
As a result of this study, after applying the variable optimization rule, in the case of K-company, the production lead time, a major variable, was found to be reduced by 5.4%. It was confirmed that the production lead time of subcontractors was also reduced by 2.4%. A production expert diagnosed it as a very dramatic improvement. Compared to the result of reducing 2.7 Mday when promoting 217 tasks targeting only specific processes in the past K-Company production innovation, the 1.5 Mday reduction was evaluated as a dramatic result achieved in a short period of time for all manufacturing products in 2022. It was verified that the MTA-based production scheduling technique improved at least 10% over the production scheduling compliance rate of the existing ERP system.
The theoretical implications can be meaningful in that the MTA (Make To Availability) technique of the existing literature study was extended to propose an advanced production priority concept based on appropriate inventory. Another theoretical implication is that the periodic Dynamic Rolling-up Optimization Method proposed a methodology that can be used when establishing production scheduling by automatically updating data collected from the production site.
The practical implication is that Dynamic Rolling-up Optimization Rules provide guidelines for companies using ERP systems to leverage production historical information to control production scheduling. The study results of the optimization rules we developed contributed to laying the groundwork for scientific and objective research on production scheduling. We emphasize that not only planning is important, but also execution to achieve goals. Based on appropriate inventory, the MTA production priority application methodology will be very attractive not only to the person in charge of production scheduling, but also to the person in charge of business management and Information System management.
However, in order for this methodology to be effective, it is recommended to carry out the following. First, measure a financial metric like inventory cost tied to revenue. Manage these financial metrics to drive productivity improvements. Second, use historical data from production to continuously improve Dynamic Rolling-up Optimization Rules. Cultivate rule creators who can analyze and measure data. Third, seek ways to reduce set-up time for each process as well as discovering improvement requirements for process stabilization continuously. Fourth, it is necessary to establish a system that can manage the entire supply chain process. Finally, develop and upgrade intelligent automation process technology and upgrade analysis algorithms.
This paper will be used as a reference guide for companies and practitioners who continuously utilize robust data in establishing production scheduling through the key driver variable optimization rule method. In addition, if the production priority-based MTA technique, which is the result of this study, is well applied to each company's environment, productivity will be greatly improved.
keywords : Production Scheduling, Make To Availability, Manufacturing Priority, Production Key Variables, Dynamic Rolling-up Optimization
주제어
#Production Scheduling Make To Availability Manufacturing Priority Production Key Variables Dynamic Rolling-up Optimization
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