Leenawong, Chartchai

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Leenawong, Chartchai
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Leenawong, Chatchai
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Email
chartchai.le@kmitl.ac.th

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    The effects of special events on regression for subcompact car sales in Thailand
    (2016-11-01)
    Rattanametawee, Witchaya
    ;
    Leenawong, Chartchai
    ;
    Netisopakul, Ponrudee
    This research proposes a method to dealing with multiple linear regression that integrates the seasonality as well as the effects of some special or unanticipated events for sales figures. The method is then applied to the car sales figures in Thailand after having been through the 2011 national big flood and the 2011-2012 government’s initiative tax-incentive program for boosting the automobile industry. Besides Thailand’s Gross Domestic Products (GDP) and the 12-month Loan’s Interest Rate as explanatory variables, seasonal dummy variables along with the proposed special event variables and appropriate event tagging are incorporated. The statistical results obtained from the proposed regression model with seasons and events, compared to the models with neither seasons nor both yields highest adjusted coefficient of determination (R-squre) and accuracy (MAPE).
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    Computational models for studying leadership modes
    (2010-03-01)
    Wattanasiripong, Nisakorn
    ;
    Wang, Karen
    ;
    Leenawong, Chartchai
    Computational models for studying the effects of permanent and rotating leadership on the performances of teams that have full interaction among team members are proposed. In each model, the leader performs both leading roles and regular tasks as an ordinary team member. Permanent leadership refers to the situation when a team has only one leader for the entire time the team exists while rotating leadership refers to the situation when every team member rotates for the leader position. Computer simulations are used for examining the effects of the parameters in these models such as the amount of time the team exists for, the learning ability of a member, and the skill level of the leader on the expected team performance. © 2010 Pushpa Publishing House.
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    Decision Support Model and Software for Consolidated Order Assignment to Delivery Trucks
    (2017-11-15)
    Leenawong, Chartchai
    ;
    Wattanawalun, Champ
    ;
    Wongsa, Kittipish
    ;
    Lethaisong, Karnkanok
    In this research, a decision model for assisting in loading consolidated orders onto delivery trucks to distribute to scattered customers is constructed. Each truck can have more than one drop-off location. The case-study company based in Bangkok produces bubble wraps that can simply be stacked up on top of the companys small trucks. Most customer orders are less than truck-load. Thus, consolidating orders into full trucks according to customer locations is necessary to ensure minimum transportation and in-transit inventory carrying costs. 0-1 integer programming is used in the model construction. Also, a decision support system for dealing with this problem is developed for ease of use to logistics managers and planners.
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    Multiple linear regression using gradient descent: A case study on Thailand car sales
    (2017-01-01)
    Netisopakul, Ponrudee
    ;
    Leenawong, Chartchai
    Severe fluctuations in Thailand car sales had enormous impacts on the automobile and related industries. A reliable forecasting model is needed to accurately forecast the car sales for the next production batch. Using ten-year car sales data, this research proposes a machine learning approach using gradient descent (GD) to fitting multiple linear regression for Thailand car sales forecasts. The resulted forecasting accuracy is then compared with that of a normal equation method (NE) as well as that obtained from a statistical package (SP). First, two independent variables (2IVs): Thailand’s Gross Domestic Product and the 12-month Loan Rate are used in the proposed models. Then, dummy seasonal variables (Season) are added to the regression equations. Finally, dummy event flag variables (Event) are added. Totally, five sets of experiments are conducted. The experiment results show that NE produces the same regression equations as SP. Both GD and NE methods yield exactly the same results for 2IVs, but GD yields slightly less prediction accuracy than NE’s in Season and Event experiments. This research concludes that gradient descent has comparable forecasting accuracy to those from other methods. Nevertheless, when the regression contains dummy variables, caution is recommended.