The Forecasting of Durian Production Quantity for Consumption in Domestic and International Markets

Thailand is one of the world’s first exporting countries of fresh and processed durians. Each year in the durian season, there are excess supplies of fresh durians which directly cause a decrease in durian price. The farmers sell their durians at a price which is actually lower than the production cost. This problem recurs every year. The purpose of this research is to design and develop the models which can effectively forecast the quantity of fresh durian production. Firstly, applying the four Time Series models, secondly, applying the Back-propagation Neural Networks (BPN) model to find an accurate forecasting model that can effectively forecast the quantity of durians in Thailand in advance. The findings of the research are the model of Back-propagation Neural Networks of the structure 4-8-1 equal to the least value of Mean Absolute Percentage Error (MAPE). It is in the good level of forecasting, and can be applied to forecast the fresh durian quantity effectively. After attaining the accurate forecasting model, this is applied with the Linear Programming (LP) model to assess the value of appropriate fresh durian in each region in the following year. The data of fresh and processed durians planning can be helpful to the farmer, as they can then make the maximum profit from selling their durian

The Design of a Forecasting Support Models on Demand of Durian for Export Markets by Time Series and ANNs

Nowadays, Durian is the most important exported fruit of Thailand. The export value of durian is approximately 144.87 million USD per year and growing increasingly. The problem in this durian product has been oversupply since the product has been brought out into the market simultaneously; causing durian growers sell their product lower than cost price. In order to avoid the problem of durian exceeds the needs of consumers. Therefore, the objective of this research is to design the forecasting model of the demand of durian in export markets. This research is to find for forecasting demand of four kinds of durian: fresh durian, frozen durian, durian paste and durian chips in the next year. Firstly, applying Output models the four Time Series by Moving Average, Deseasonalised, Exponential Smoothing and Double Exponential Smoothing, secondly, applying Input models: Regression model and Artificial Neural Networks (ANNs) model. The forecast model which has the least value of Mean Absolute Percentage Error (MAPE) is the most accurate forecast model. The results of Output models reveal that the most accurate forecast model is Deseasonalised model which gives the least value of MAPE in three kinds of durian: 1) durian paste at the percentage of 8.66, 2) frozen durian at the percentage of 9.78 and 3) fresh durian at the percentage of 19.24 while Input models reveal that the most accurate forecast model is Artificial Neural Networks (ANNs) model gives the least value of MAPE of durian chips at the percentage of 29.76. After attaining the accurate forecasting model, this is applied with the Linear Programming (LP) model to assess the value of appropriate quantity for domestic and export markets of four kinds of durian for the maximum profit in the following year. The maximum profit quantity of each kinds of durian able to helpful to the durian growers are able to sales planning and processed durian that are the most profitable