Forecasting of engineering manpower through fuzzy associative memory neural network with ARIMA: a comparative study

The smooth working of industry depends on the availability of proper engineering manpower. If proper qualified and experienced technical personnel are not available, the industry cannot run in the most efficient way. Here, an effort is made to assess the engineering manpower requirement (personnel belonging to mechanical engineering) in certain industry group (steel manufacturing) in the state of West Bengal in India for the next 5 years. The method of auto regressive integrated moving average (ARIMA) and the fuzzy associative memory (FAM) neural network model are tested and based on error analysis (calculation of average error) the model with minimum error is selected and used for assessment of futuristic engineering manpower. Certain statistical functions, i.e. regression analysis using a least square technique based on linear, exponential, curvilinear (parabolic) equations and the tables of Orthogonal Polynomial are applied on the estimated data value calculated earlier. The particular statistical model is chosen based on the average error of estimated date generated using statistical models with the actual data over span of years. The said statistical model based on the estimated data using the selected model of ARIMA or FAM neural network can be used for the generation of futuristic forecasted engineering manpower. (C) 2002 Elsevier Science B.V. All rights reserved.