KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Search Results

Now showing 1 - 6 of 6

Some of the metrics are blocked by your
consent settings
Modeling Equivalent Circulating Density During Drilling Operations in the Gulf of Thailand
(2026-03-31)
Leerojanaprapa, Kanogkan
;
Suttaloon, Sudarat
;
Bhundarak, Komn
;
Sirikasemsuk, Kittiwat
Equivalent Circulating Density (ECD) represents the total hydrostatic pressure generated by drilling fluid while in motion. This prevents the internal pressure within the well from exceeding the fracture resistance of the rock, which could lead to lost circulation and an inability to effectively control the wellbore pressure. This research aims to predict ECD in 6.125-inch production section in Gulf of Thailand field by using five machine learning algorithms were utilized, namely Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Networks (ANN), Gradient Boosting (GB), and Extreme Gradient Boosting (XGBoost). Various sensors from the Measure While Drilling (MWD), Logging While Drilling (LWD), and Pressure While Drilling (PWD) tools were used to collect raw data, totaling 38,863 records and 24 variables to predict the ECD value. The dataset was randomly split into 80% for training and validation and 20% for testing. The results indicate that the RF technique outperformed the other models in predicting ECD values, producing the lowest RMSE of 0.031. Therefore, the RF model is most suitable for further development and real-time application in predicting ECD values.
Some of the metrics are blocked by your
consent settings
Measuring the Efficiency of Thai Commercial Banks during the COVID-19 Pandemic by Data Envelopment Analysis
(2022-08-26)
Leerojanaprapa, Kanogkan
;
Bhundarak, Komn
;
Atthirawong, Walailak
;
Sirikasemsuk, Kittiwat
This study intends to measure the comparative efficiency of Thai Commercial Banks. The performance data has been collected over the past 2 years, between 2020 and 2021 during the COVID-19 pandemic and then compared to performance during the normal period of 2017-2018. Data Envelopment Analysis (DEA) using Variable Return to Scale (VRS) is applied to measure banking performance. Through Intermediation Approach, specific input variables are considered: employee expenses, directors' remuneration expenses, premises and equipment expenses and deposits. The output variables, including loans to customers and investment, are also analyzed.The same time, through the Production Approach, these specific input variables are analyzed: employee expenses, directors' remuneration expenses, premises and equipment expenses, fees and service expenses, taxes and duties and interest expenses. The output variables, including interest income, fee and service income, deposit, and loans to customers, are used to analyze the efficiency. The results of analysis with the Intermediation Approach indicate 5 of 13 Thai Commercial Banks were considered relatively inefficient including BAY, BBL, KBANK, SCB and SCBT. In addition, CIMB is 1 of 13 Thai Commercial Banks is considered as relatively inefficient under the Production Approach.
Some of the metrics are blocked by your
consent settings
Inventory Management for Consumer Products: A Distribution Center in Thailand
(2022-01-01)
Leerojanaprapa, Kanogkan
;
Bhundarak, Komn
;
Atthirawong, Walailak
;
Sirikasemsuk, Kittiwat
The objective of the study is to formulate an appropriate order quantity policy. A best-selling product, a cleaner fresher mouth, was selected to be a representative of this study. Monthly Sales data of the cleaner fresher product was collected from January 2013 to December 2021, for 9 years, to forecast the sales of each product. Two forecasting techniques i.e. Exponential Smoothing and Box-Jenkins methods were compared to select the appropriate forecasting method by using MSE criterion. It was found that the Triple Exponential Method was the suitable forecasting model to forecast demand for the cleaner fresher products. These forecast values were then applied in inventory planning to determine the order policy by using Economic Order Quantity (EOQ) method of inventory management. The results found that EOQ technique resulted in a reduction in total cost rather than the company's current technique by 27.80%•
Some of the metrics are blocked by your
consent settings
Industry cluster using cluster analysis to support industry cluster policy of Thailand
(2019-06-01)
Leerojanaprapa, Kanogkan
;
Bhundarak, Komn
;
Sirikasemsuk, Kittiwat
This research implies clustering into three clusters for five industries by means of k-Means clustering method. Agro-processing, textiles and clothing, petrochemicals and chemicals, electronics and telecommunications equipment and automotive and parts are selected for this study as they are main target promoting industries for Thailand. There were 23,628 firms in this study. From this study, we can identify different patterns of demanded resources in three different groups. One cluster required low level of resourced demands for all variables while the other two clusters required capital and manpower interchangeable between high and medium level. Only the electronics and telecommunications equipment sector showed high to medium demand for all variables. After all firms were divided into three clusters, we were able to define cluster regions by provinces, for particular types of clusters in order to evaluate the potential of each region and also define supporting policy for those firms to meet their demands following the regional economic development strategy.
Some of the metrics are blocked by your
consent settings
Relationships between Labour Demand and Resource Factors for Textile and Garment Industry in Thailand
(2017-07-19)
Leerojanaprapa, Kanogkan
;
Bhundarak, Komn
This article aims to forecast and compare labor demand in textile industry represented by fiber manufacturing companies (ISIC CODE 13111) and in garment industry represented by the manufacturer of work wear (ISIC CODE 14111) in Thailand. Secondary data were acquired from the Department of Industrial Works where registered companies submitted their essential data and obtained the approval permit in doing business during 2015. Complete data from 494 textile companies and 656 garment companies are implemented in Multiple Regression Analysis. This research defined eight independent variables: Land capital (X1), Buildings capital (X2), Machinery capital (X3), Working capital (X4), Factory area (X5), Building area (X6), Horsepower (X7), and Type of manufacturer (X8). The results from the study revealed that 63.09% of labor demand variation for textile manufacturers can be explained by Y = 28.6246 + 0.0180X7 0.0004 X5 + 0.0081X6 + 0.0004X4 0.0002X3 while 51.50% of the labor demand variation for garment manufacturers can be explained by Y = 116.86 + 0.006X2+ 0.004X5 109.748X8 + 0.008X6 + 0.040X7 + 0.001X4.
Some of the metrics are blocked by your
consent settings
Factors associated with Thai exporter's interest in using New Dawei deep seaport
(2017-01-01)
Leerojanaprapa, Kanogkan
;
Sirikasemsuk, Kittiwat
;
Bhundarak, Komn
Dawei deep seaport in a part of the Dawei Special Economic Zone (Dawei SEZ) in Myanmar aims to support the new economics along the GMS Southern Corridor. The Dawei seaport can serve the potential new industries along the new industry zones. The new port will be the alternative route for Thai exporters in the future as it is under construction. The exploratory study by employing survey was selected and analyzed to identify the significant influencing factors. The results of hypothesis testing by Pearson Chi-Square test confirm the relation between the interest of using new Dawei deep seaport and the location of manufacturer (p-value = 0.027). In addition, the results of t-test confirm the significant six decision variables of Time for transportation (v7), Reliability of service (v10), Port size and capability (v15), Facility (v25), Professionals and skilled labors in port operation (v30), and Port accessibility (v31) are more important for exporters who are interested in Dawei seaport than the exporters who may not be intend to use the new seaport, p-value (1-tailed) < 0.05.

KMITL

Browse

Filters

Settings

Sort By

Results per page

Search Results