Pathum Thani, Thailand

Valaya Alongkorn Rajabhat University

www.vru.ac.th
Pathum Thani, Thailand
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Kerdpitak C.,Valaya Alongkorn Rajabhat University
Journal of Applied Business Research | Year: 2017

The research on Factors Leading to the Success of a Tourism Business aims to 1) study the current condition of the tourism business 2) study the guideline for tourism business development 3) study marketing innovation and tour program arrangement and the competition strategies of the tourism business 4) study the business success of the tourism business management 5) study the influencing factors to business success of tourism business. Quantitative and qualitative researches have been applied as research methodology. The population includes tourism companies in Bangkok. The unit of analysis is a tourism organization. The simple random sampling is used to select the sample for the quantitative research. The sample used for data collection includes 339 tourism business operators and managers, and the instrument used is a questionnaire. The purposive sampling method selection is used to collect the sample for the qualitative research. The data have been collected from the tourism business operators or managers by means of in-depth interview. The data have been analyzed by using the descriptive statistics, and the path analysis has been applied for equation analysis. According to the study result, the factors of the marketing innovation, the tour program arrangement and the competition strategies influence the business success of the tourism business management by having the influential level of 42.6 percent. There are 45 main factors that can be used for the success of the business. The current condition of the tourism business is highly competitive. Marketing innovations are always applied for the competition. In addition, the tour program arrangement is significant, and it must be adjusted for the high season. The guideline used for the tourism business development includes the differentiation of the tour program arrangement, the price and the service quality, such as accommodations, vehicles, food, safety and clients’ needs. © by author(s).


Kandananond K.,Valaya Alongkorn Rajabhat University
International Journal of GEOMATE | Year: 2017

Carbon emission from the manufacturing sector is a critical issue which is concerned by the environmental authorities since the violation of the carbon emission cap might lead to the sanction by one of Thailand's largest trade partner, European Union (EU). As a result, it is important for the manufacturers to be able to assess their own products' carbon footprint. In this study, the selected case study is a ceramic factory which manufactures non-glazed floor files. The scope of evaluation covers Business-to-Customer (B2C) transaction while the life cycle of a product includes four stages, i.e., resource extraction, manufacturing, distribution, use and waste disposal. The study results indicate that the highest contribution to the carbon emission is from the extraction of ceramic clay while the manufacturing stage has the second highest effect on the emission. The distribution of products, use and disposal are the life cycle stages which have small effects on the emission. Another objective of this research is to conduct an empirical study which leads to the capability to quantify the effect of different factors on the manufacturing of floor tiles. According to the experimental study, three factors, i.e., chalk clay, ball clay and feldspar, are considered as the process inputs while the response variables are percent absorption and hardness. Elaborately, 23 full factorial design was deployed to study the find the relationship between inputs and outputs. The results has two folds. The first fold is useful for the manufacturers who would like to understand how much their product has emitted the greenhouse gas to the atmosphere and it might lead to the minimization of their emission. Moreover, the relation between the tile characteristic and factors affecting the manufacturing is known so the manufacturer is able to efficiently optimize the manufacturing process in order to achieve the highest quality products. © Int. J. of GEOMATE.


Kandananond K.,Valaya Alongkorn Rajabhat University
ACM International Conference Proceeding Series | Year: 2017

This study focuses on the utilization of non-parametric to assess the distribution of repair times of a machine part as well as the prediction of the future values. There are two folds of objectives, namely, the distribution assessment of the repair times. The diagnostic graph, i.e., histogram and normal probability plot, as well as a non-parametric test, Kolmogorov-Smirnov (KS) method, is utilized to assess the distribution of data. According to the KS test, it can be used effectively to test the distribution of the repair times of a machine which are selected as a case study. Another objective is the prediction of the future repair time required to fix a designated part. The time series analysis based on autoregressive integrated moving average (ARIMA) model is deployed in order to forecast the repair times. It turned out that one of the simplest models of ARIMA, ARIMA (0, 1, 0) or random walk, is the most appropriate model for the prediction and this indicates that the pattern of repair times is non-stationary. © 2017 Association for Computing Machinery.


Kandananond K.,Valaya Alongkorn Rajabhat University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2016

The capability to optimize the surface roughness is critical to the surface quality of manufactured work pieces. If the performance of the available CNC machine is correctly characterized or the relationship between inputs and output is clearly identified, the operators on the shop floor will be able to operate their machine at the highest efficiency. In order to achieve the desired objective, this research is based on the empirical study which is conducted in such a way that the optimization method is utilized to analyze the empirical data. The focused process in this study is the lathing process with three input factors, spindle speed, feed rate and depth of cut while the corresponding output is surface roughness. Two methods, namely artificial neural network (ANN) and 2k factorial design, are used to construct mathematical models exploring the relationship between inputs and output. The performance of each method is compared by considering the forecasting errors after fitting the model to the empirical data. The results according to this study signify that there is no significant difference between the performance of these two optimization methods. © Springer International Publishing Switzerland 2016.


Kandananond K.,Valaya Alongkorn Rajabhat University
Energies | Year: 2011

Demand planning for electricity consumption is a key success factor for the development of any countries. However, this can only be achieved if the demand is forecasted accurately. In this research, different forecasting methods-autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and multiple linear regression (MLR)-were utilized to formulate prediction models of the electricity demand in Thailand. The objective was to compare the performance of these three approaches and the empirical data used in this study was the historical data regarding the electricity demand (population, gross domestic product: GDP, stock index, revenue from exporting industrial products and electricity consumption) in Thailand from 1986 to 2010. The results showed that the ANN model reduced the mean absolute percentage error (MAPE) to 0.996%, while those of ARIMA and MLR were 2.80981 and 3.2604527%, respectively. Based on these error measures, the results indicated that the ANN approach outperformed the ARIMA and MLR methods in this scenario. However, the paired test indicated that there was no significant difference among these methods at α = 0.05. According to the principle of parsimony, the ARIMA and MLR models might be preferable to the ANN one because of their simple structure and competitive performance. © 2011 by the authors.


Kandananond K.,Valaya Alongkorn Rajabhat University
International Journal of Quality, Statistics, and Reliability | Year: 2010

The objective of this paper is to quantify the effect of autocorrelation coefficients, shift magnitude, types of control charts, types of controllers, and types of monitored signals on a control system. Statistical process control (SPC) and automatic process control (APC) were studied under non-stationary stochastic disturbances characterized by the integrated moving average model, ARIMA (0, 1, 1). A process model was simulated to achieve two responses, mean squared error (MSE) and average run length (ARL). A factorial design experiment was conducted to analyze the simulated results. The results revealed that not only shift magnitude and the level of autocorrelation coefficients, but also the interaction between these two factors, affected the integrated system performance. It was also found that the most appropriate combination of SPC and APC is the utilization of the minimum mean squared error (MMSE) controller with the Shewhart moving range (MR) chart, while monitoring the control signal (X) from the controller. Therefore, integrating SPC and APC can improve process manufacturing, but the performance of the integrated system is significantly affected by process autocorrelation. Therefore, if the performance of the integrated system under non-stationary disturbances is correctly characterized, practitioners will have guidelines for achieving the highest possible performance potential when integrating SPC and APC. © 2010 Karin Kandananond.


Kandananond K.,Valaya Alongkorn Rajabhat University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

The dynamic characteristic of a drum boiler is complex and this complication leads to the difficulty in controlling the output of the system, i.e., steam pressure. Therefore, this study attempts to investigate the application of two model predictive methods, artificial neural network (ANN) and system identification, in order to assess the performance of each method. According to the system, the inputs are feed water flow rate and applied heat while the output is the steam pressure. The ANN method used is based on a training algorithm, Levenberg-Marquardt back propagation. On the other hand, the optimal model of system identification method is the output error (OE). The performance measurement is compared by considering the mean squared error (MSE) after fitting the simulated prediction from each model to the observation. The results show that ANN slightly outperforms the system identification technique. Moreover, another finding is that ANN method is capable of identifying the outlier among the observations so it is robust to the disturbances. © Springer International Publishing Switzerland 2015.


Kandananond K.,Valaya Alongkorn Rajabhat University
Procedia Computer Science | Year: 2013

The performance of artificial neural network (ANN) and support vector machine (SVM) method for forecasting time series data is still an open issue for discussions among many authors in the literature. Hence, the purpose of this study is to characterize the capability of these two methods under the autocorrelation structure of time series and the most appropriate model is chosen. In this research, the performance of ANN and SVM is compared with respect to the autoregressive integrated moving average (ARIMA) structure. Two classes of ARIMA models, ARMA (1, 1) and IMA (1, 1), are utilized to represent stationary and non-stationary processes while the performance index of each learning method is the forecasting errors computed after each learning cycle. In order to deliver the right conclusions, the statistical analysis is conducted and the conclusions are drawn by utilizing the factorial design of experiment. The results indicate that these two machine learning methods have a different performance under the specific scenario of autocorrelation. When processes are stationary, the ANN might be a better choice than the SVM method. However, it turns out to be that the SVM has obviously outperformed the ANN for non-stationary cases. © 2013 The Authors.


Kandananond K.,Valaya Alongkorn Rajabhat University
Procedia Engineering | Year: 2014

The implementation of statistical control charts under autocorrelated situations is a critical issue since it has a significant impact on the monitoring capability of manufacturing processes. The objective of this study is to assess the performance of control charts under different scenarios and to optimize the design of control charts to best deal with autocorrelated processes. To achieve the proposed objective, two autoregressive integrated moving average models, ARIMA (1, 0, 1) and ARIMA (0, 1, 1), are utilized to characterize stationary and non-stationary processes. These process models were simulated to achieve the response, average run length (ARL), which is the performance measure of this study. The factorial design of experiment was conducted to quantify the effect of critical factors, i.e., ARIMA coefficients, types of charts (exponentially weighted moving average: EWMA and moving range: MR) and shift sizes on the ARL. The experimental results show that EWMA chart is the most appropriate control chart to monitor autocorrelated observations. Additionally, both AR and MA parameters along with shift sizes have a significant effect on the performance of control charts. Therefore, this study has pointed out a suitable tool for use under the different scenarios of autocorrelation. The validation of the above experimental results was conducted on another ARIMA model, ARIMA (1, 0, 0). If the performance of control charts under autocorrelated disturbances is correctly characterized, practitioners will have guidelines for achieving the highest possible performance potential when deploying SPC. © 2014 The Authors. Published by Elsevier Ltd.


Kandananond K.,Valaya Alongkorn Rajabhat University
Procedia Engineering | Year: 2014

Green supply chain is a supply chain system focusing on environmental impacts and the efficiency of energy used. A green supply chain will be achieved if a system is able to track down all information regarding the environmental influence. However, a green supply chain will not be possible without enterprise resource planning (ERP) implementation in organizations. ERP is the integrated information system overlooking manufacturing processes from raw materials to finished products. However, the successful implementation of ERP depends on four critical factors: defining business cases, prepare system and users, stabilizing to obtain normal operations and maintaining and upgrading. Moreover, learning organization (learning from own experience and learning from others) is another key ingredient for the successful implementation. Last but not least, process mapping from "As- Is" to "To-Be" models is also a powerful technique which facilitates the implementation by identifying the process models of current and future ones. Moreover, the featured functions of ERP for a green supply chain should include the capability to keep and track the environmental data of raw materials from suppliers, to prepare an environmental report for each product from raw materials to finished products, to keep the environmental data regarding logistics and transportation and to comply with the ERP software used by third-party manufacturers. © 2014 The Authors. Published by Elsevier Ltd.

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