Leerojanaprapa, Kanogkan
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Preferred name
Leerojanaprapa, Kanogkan
Alternative Name
Leerojanaprapa, K.
Main Affiliation
Email
kanogkan.le@kmitl.ac.th
15 results
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Item type:Publication, Analysis of two-missing-observation 4 × 4 latin squares using the exact approach(2018-01-01); This research deals with the analysis of incomplete Latin square designs using the exact approach. Specifically, the study investigated the 4 × 4 Latin square designs with two missing observations without replication. In the research, the general regression significance test (i.e. the exact approach) was used to derive the estimation formulae of fitted parameters for the full and reduced linear statistical models, thereby simplifying the calculation process. In addition, the proposed exact approach-based formulae facilitate the determination of the treatment sum of squares and the error sum of squares, both of which are subsequently employed in the analysis of variance (ANOVA). - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Industry cluster using cluster analysis to support industry cluster policy of Thailand(2019-06-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Applying Bayesian network for noncommunicable diseases risk analysis: Implementing national health examination survey in Thailand(2017-07-02); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Using DEMATEL to explore the relationship of factors affecting consumers' behaviors in buying green products(2018-01-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Comparison of Bayesian Networks for Diabetes Prediction(2019-01-01); A Bayesian network (BN) can be used to predict the prevalence of diabetes from the cause–effect relationship among risk factors. By applying a BN model, we can capture the interdependencies between direct and indirect risks hierarchically. In this study, we propose to investigate and compare the predictive performances of BN models with non-hierarchical (BNNH), and non-hierarchical and reduced variables (BNNHR) structures, hierarchical structure by expert judgment (BNHE), and hierarchical learning structure (BNHL) with type-2 diabetes. ROC curves, AUC, percentage error, and F1 score were applied to compare performances of those classification techniques. The results of the model comparison from both datasets (training and testing) obtained from the Thai National Health Examination Survey IV ensured that BNHE can predict the prevalence of diabetes most effectively with the highest AUC values of 0.7670 and 0.7760 from the training and the testing dataset, respectively. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Factors associated with Thai exporter's interest in using New Dawei deep seaport(2017-01-01); ; Bhundarak, KomnDawei 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Management practices of Thai silk product(2015-01-01); Atthirawong, WalailakThai silk is one of Thailand’s most well-known products, which is considered as the best silk in the world. It is famous for its bearing uniqueness features, beautiful designs, bright colors and elegant quality. Silk weaving is a long-established folk craft passed down from generation to generation that resembles the region’s cultural heritage. It pays an important role to the foundation of Thai local economy for centuries.Thai products produced by local communities using indigenous skills and craftsmanship combined with available natural resources and raw materials. However, it is widely accepted that there exists a managerial gap in many small or micro-sized enterprise, many activities may not be enough to make those products are sustainable. Understanding the key issues affected Thai silk entire chain is an important step to improve the competitiveness of those products. In this paper, case studies are therefore conducted, as part of a research project to examine the current stage. The SCOR model is employed to identify challenges and pinpoint weaknesses in management practices also in what processes have an effect on inefficiency within the supply chain before radical suggestions improvements. This paper intends to report two case studies of Thai silk products and concludes with a summary of key issues that are raised in the fieldwork. Finally, conclusions and recommendations for further development of the main study are also provided in the paper. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Relationships between Labour Demand and Resource Factors for Textile and Garment Industry in Thailand(2017-07-19); Bhundarak, KomnThis 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 yourconsent settings
Item type:Publication, Optimal production volume of rubber gloves mold for rubber gloves production planning(2016-01-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Inventory Management for Consumer Products: A Distribution Center in Thailand(2022-01-01);