Achieving lean production through im-proved RFID technology and the use of GM11 predictive models
Qian, Drew Zhicheng Jr (2024)
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-202404308472
https://urn.fi/URN:NBN:fi:amk-202404308472
Tiivistelmä
At present, China's freight volume in some goods is large, which is a challenge to the logistics industry practi-tioners. Some companies are starting to use RFID and electronic tags in their packaging departments. These technologies can greatly speed up the efficiency of the logistics industry. How to use RFID tags, RFID readers, and achieve lean production process is very critical. Through this series of measures, we can reduce the reten-tion of goods in the packaging workshop, reduce the cost of warehouse rental, shorten the packaging time, and let the goods reach the hands of customers faster. To improve the implementation of lean production.
The GM11 forecasting model is a model widely used in production forecasting in China. Production for the coming year can be predicted using data from previous years. This kind of prediction can be done in a com-puter software, which is a very fast and efficient prediction model.
The procedure for my thesis is this. First, the distance and conveyor speed between several RFID technologies and the goods are selected through the system literature. Test at speeds and distances that the test factory can adjust to understand current best practices and results obtained in similar scenarios. Then the experi-mental design began. According to the results of literature review, the experiment was designed to adjust the distance between RFID and goods and the speed of conveyor belt. This phase will be carried out in selected company packaging workshops through field tests to determine maximum logistics efficiency. Data collection is also very important, on the basis of the experimental design, the collection of cargo flow, time and other key operational data. This data will be used for subsequent model building and analysis. The next step is to make predictions using the GM11 model
Using the collected data, the GM11 model is applied to predict the flow of goods in the next year. This step uses the GM11 prediction model in the grey system theory to forecast the freight volume of the next year based on historical data. After the prediction is completed, the efficiency improvement analysis is started. Using the predicted amount of cargo combined with the adjusted conveyor speed, calculate the time required to process the same amount of cargo. This time is then compared to the time required to use the original conveyor speed to assess the time saved by adjusting the conveyor speed.
The GM11 forecasting model is a model widely used in production forecasting in China. Production for the coming year can be predicted using data from previous years. This kind of prediction can be done in a com-puter software, which is a very fast and efficient prediction model.
The procedure for my thesis is this. First, the distance and conveyor speed between several RFID technologies and the goods are selected through the system literature. Test at speeds and distances that the test factory can adjust to understand current best practices and results obtained in similar scenarios. Then the experi-mental design began. According to the results of literature review, the experiment was designed to adjust the distance between RFID and goods and the speed of conveyor belt. This phase will be carried out in selected company packaging workshops through field tests to determine maximum logistics efficiency. Data collection is also very important, on the basis of the experimental design, the collection of cargo flow, time and other key operational data. This data will be used for subsequent model building and analysis. The next step is to make predictions using the GM11 model
Using the collected data, the GM11 model is applied to predict the flow of goods in the next year. This step uses the GM11 prediction model in the grey system theory to forecast the freight volume of the next year based on historical data. After the prediction is completed, the efficiency improvement analysis is started. Using the predicted amount of cargo combined with the adjusted conveyor speed, calculate the time required to process the same amount of cargo. This time is then compared to the time required to use the original conveyor speed to assess the time saved by adjusting the conveyor speed.