The problem of this research is automotive part companies do not have order because of the world economic crisis and effect to automotive industry. Automotive and automotive parts demand in the world decrease. Automotive Industry of Thailand received affect. Automotive part companies cannot export to foreign markets. The companies in Thailand need the forecasting of automotive part quantity demanded by foreign markets by selecting the most accurate model. This paper presents the modeling for forecasting demand for automotive part of foreign markets to Thailand with time series forecasting techniques. In this study the automotive part are wheels and including parts and accessories because Thailand exported the most in group of automotive parts. These five countries have consistently demanded the largest number of wheels and including parts and accessories are Japan China South Korea Germany and Indonesia. The research involves historical collecting data for the period of 1997 to 2008. The evaluation function of forecast error is used with MAPE. The results reveal that each country suits for each model in term of the chosen dimension. The government can use the forecasting demand for effective production planning by informing manufactures.

This research aimed to design and construct a forecasting support system on demand of fresh durian of Thailand by applying 2 mathematical models which were Time Series model and Artificial Neural Network model in order to find an accurate model of forecasting support system on demand of durian yield to forecast durian yield quantity of Thailand in advance and use the forecast results to plan and determine quantity of fresh durian and durian product conforming with domestic consumption and export in order not to be over demand and cheap durian sold price. The forecast models were applied with Time Series model by using Moving Average Weighted Moving Average Single Exponential Smoothing and Holt’s Linear Exponential Smoothing techniques and Back Propagation of Artificial Neural Network model which used 4 input variables relating to durian yield. All of the forecast models were used to forecast total durian yield quantity in 26 provinces of durian cultivated area all around Thailand. Durian yield quantity data used was from 1996 to 2008. After that the forecast results were used to find the errors of MAE MSE RMSE and MAPE. Then the errors of each forecast model were compared. The forecast model which had the least error was the most accurate forecast model. The research result is following. Back Propagation with 4-8-1 structure of Artificial Neural Network model had the least error so that it was the most accurate forecast model to forecast fresh durian yield quantity in advance.

The objective of this research is to design forecasting support models on demand of durian for domestic markets and export markets for durian gardeners durian entrepreneurs of domestic markets and export markets and the Office of Agricultural Economics to plan durian demand conforming with domestic markets and export markets because of Production and marketing main problems (The Agricultural Information Center The office of Agricultural Economics: 2008) such as over - much durian production during production capital increased farmers durian sold price tended to be lower and lower inefficient domestic market management and more export but cheaper price therefore after that to design forecasting for the purpose in order that durian would not be over demand which is containing factors of the demand of durian fresh durian frozen durian paste and durian chips for domestic markets and export markets exported to Asia America Australia and Africa. The research involves historical collection data in the period of 2002 to 2008. The basic of forecast models are the designed and improved models using an intelligent knowledge-based approach beginning Moving average Deseasonalized Exponential smoothing Double exponential smoothing and Artificial neural network (ANNs) program within the model of new value Creation and comparative accuracy models. The evaluation result of forecasting durian demand showed that lowest MAPE of Deseasonlized models at 3 month was durian fresh durian frozen and durian paste but durian chips showed that lowest MAPE of ANNs at 4 input 10 hidden layers and 1 output was high accurate forecasting and optimal models. The analysis recommendations and error forecast of durian demand are also presented.

Although the self-checkout counter is being offered to clients in the hypermarket the frontline service staff are still required to assist the customers during the initial stages of the introduction process. Other clients are still in need of support with the technologys adoption and adaption among other things. This research would look at the customer from the perspective of cultural intelligence and technological adaptability. A semi-structured interview with 20 participants was conducted to examine the self-checkout counter and the frontline service provider. According to the sub-cultural differences in the service providers background geography and level of technology implementation the data of this study is gathered from a variety of geographical locations. The result reveals the four distinct roles played by the service provider in terms of performance technology assistance and the ability to comprehend how self-checkout and frontline service personas influence the shopping experience and perception of quality in a hypermarket setting respectively.