• The Journal of Information Systems
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• v.31 no.3
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• pp.197-220
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• 2022

Purpose The purpose of this study is to identifies the demand pattern categorization of repair parts of Automotive After-sales Service(A/S) and proposes a demand prediction model for Auto repair parts using Long Short-Term Memory (LSTM) of artificial neural networks (ANN). The optimal parts inventory quantity prediction model is implemented by applying daily, weekly, and monthly the parts demand data to the LSTM model for the Lumpy demand which is irregularly in a specific period among repair parts of the Automotive A/S service. Design/methodology/approach This study classified the four demand pattern categorization with 2 years demand time-series data of repair parts according to the Average demand interval(ADI) and coefficient of variation (CV²) of demand size. Of the 16,295 parts in the A/S service shop studied, 96.5% had a Lumpy demand pattern that large quantities occurred at a specific period. lumpy demand pattern's repair parts in the last three years is predicted by applying them to the LSTM for daily, weekly, and monthly time-series data. as the model prediction performance evaluation index, MAPE, RMSE, and RMSLE that can measure the error between the predicted value and the actual value were used. Findings As a result of this study, Daily time-series data were excellently predicted as indicators with the lowest MAPE, RMSE, and RMSLE values, followed by Weekly and Monthly time-series data. This is due to the decrease in training data for Weekly and Monthly. even if the demand period is extended to get the training data, the prediction performance is still low due to the discontinuation of current vehicle models and the use of alternative parts that they are contributed to no more demand. Therefore, sufficient training data is important, but the selection of the prediction demand period is also a critical factor.

• Journal of Veterinary Science
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• v.23 no.5
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• pp.73.1-73.8
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• 2022

Background: Lumpy skin disease (LSD), a disease transmitted by direct and indirect contact with infected cattle, is caused by the Lumpy skin disease virus (LSDV). The disease affects cattle herds in Africa, Europe, and Asia. The clinical signs of LSD range from mild to the appearance of nodules and lesions in the skin leading to severe symptoms that are sometimes fatal with significant livestock economic losses. Objectives: This study aimed to characterize LSDV strains in the blood of infected cattle in Thailand based on the GPCR gene and determine the phylogenetic relationship of LSDV Thailand isolates with published sequences available in the database. Methods: In total, the blood samples of 120 cattle were collected from different farms in four provinces in the northeastern part of Thailand, and the occurrence of LSDV was examined by PCR based on the P32 antigen gene. The genetic diversity of LSDV based on the GPCR gene was analyzed. Results: Polymerase chain reaction assays based on the P32 antigen gene showed that 4.17% (5/120) were positive for LSDV. All positive blood samples were amplified successfully for the GPCR gene. Phylogenetic analysis showed that LSDV Thailand isolates clustered together with LSDVs from China and Russia. Conclusions: The LSD outbreak in Thailand was confirmed, and a phylogenetic tree was constructed to infer the branching pattern of the GPCR gene from the presence of LSDV in Thailand. This is the first report on the molecular characterization of LSDV in cattle in Thailand.