In metal fabrication industry, assembling department plays the major role since it involves risks in assembling the components. Hence, it is always difficult for the manufacturers to identify the criteria of agile manufacturing in assembling department that effects the assembly of the fabricated metal components. Agile manufacturing is one of the innovative method of manufacturing, which focus on the customer satisfaction and also maintaining the quality and cost of the product. Metal fabrication industries generally struggle to find right criteria for better agile manufacturing process. This study focuses on the selection of suitable criteria for agile manufacturing, which requires an in-depth analysis depending on the influence they possess on the agile manufacturing. The objective of this paper is to analyze and identify the most influencing criteria for the metal manufacturing industry based on the customers’ and industrial expert’s perspective. Here we have selected ten different criteria based on the literatures available on the agile manufacturing. The criteria are segregated and ranked according to the nature and influence they possess on other criteria using decision making trial and evaluation laboratory (DEMATEL) methodology. This study also helps the metal fabrication industry to identify the most influencing criteria to implement on agile manufacturing and to have high efficiency on the production. The results show that the customer satisfaction seems to be the primary criteria that will have more influence in metal fabrication industry.

Reference evapotranspiration (ET0) is a rudimental variable in the estimation of crop water requirement, and preparation of irrigation schedule. Prediction of ET0 is a necessitous one for estimation of crop water requirement in future time step. In this paper ET0 is predicted using Artificial Neural Network (ANN) by different inputs Like Temperature, Cloud cover, Vapor pressure, Precipitation and its combinations by various models. Before prediction, the predictability of all the input time series is calculated individually and the effect of predictability on prediction is analyzed in models having single predictor. In spite of inserting additional predictor in input, the reason for increase of Root mean squared error is justified in terms of predictability in the models having multiple predictors. Also it is seen that the performance of models with multiple predictors is better when compared to single predictor models in the estimation of ET0.