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Demand forecasting in the clothing industry is very complex due to the existence of a wide range of product references and the lack of historical sales data. To the authors' knowledge, there is an inadequate number of literature studies to forecast the demand with the adaptive network based fuzzy inference system for the clothing industry. The purpose of this paper is to construct a decision support system for demand forecasting in the clothing industry.

The adaptive‐network‐based fuzzy inference system (ANFIS) is used for forecasting demand in the clothing industry.

The results of the proposed study showed that an ANFIS‐based demand forecasting system can help clothing manufacturers to forecast demand more accurately, effectively and simply.

In this study, the demand is forecast in terms of clothing manufacturers by using ANFIS. ANFIS is a new technique for demand forecasting, it combines the learning capability of the neural networks and the generalization capability of the fuzzy logic. The input and output criteria are determined based on clothing manufacturers' requirements and via literature research, and the forecasting horizon is about one month. The study includes the real life application of the proposed system and the proposed system is tested by using real demand values for clothing manufacturers.

According to literature research and conversations with apparel manufacturers' specialists, there is not any common analytic method for demand forecasting in apparel industry and to the authors' knowledge, there is not adequate number of study in literature to forecast the demand with adaptive network-based fuzzy inference system (ANFIS) for apparel manufacturers. The purpose of this paper is constructing an effective demand forecasting system for apparel manufacturers.

The ANFIS is used forecasting the demand for apparel manufacturers.

The results of the proposed study showed that an ANFIS-based demand forecasting system can help apparel manufacturers to forecast demand accurately, effectively and simply.

ANFIS is a new technique for demand forecasting, combines the learning capability of the neural networks and the generalization capability of the fuzzy logic. In this study, the demand is forecasted in terms of apparel manufacturers by using ANFIS. The input and output criteria are determined based on apparel manufacturers' requirements and via literature research and the forecasting horizon is about one month. The study includes the real-life application of the proposed system, and the proposed system is tested by using real demand values for apparel manufacturers.

The primary aim of this research is to investigate the application of open source geographic information system software, geographical resources analysis support system (GRASS) for landslide hazard assessment.

Five parameters affecting landslide occurrence derived from topographical, geological and land use maps of Cameron highland were used for the assessment.

The results showed that about 93 percent of the study area falls under zone II that is of low hazard, with less than 7 percent on zone III with moderate hazard and only less than 1 percent falls under zone IV, which is of high hazard.

The accuracy of the landslide hazard map needs to be assessed by cross‐correlation with landslide occurrence in the field.

The map produced showed the potential application of GRASS as a tool for producing landslide hazard assessment map.

The major outcome of this research is the possible use of open source GIS software in the application of landslide hazard assessment. The capability of GRASS in performing such environmental assessment will certainly attract many researchers and organizations with limited budgets, especially in developing countries such as Malaysia.