Identification of Stages of Industrial Sickness of Large- and Medium-Scale Units Using Certain Soft-Computational Approach

Very often, industrial sickness is identified using certain traditional techniques which rely upon a range of manual monitoring and compilation of financial records. It makes the process tedious, time consuming, and often are susceptible to manipulation. Hence, decision makers, planners, and funding agencies of such units are sometimes surrounded by uncertainty and unpredictable situations while taking decisions regarding the state of industrial health and the subsequent measures required. Therefore, certain readily available tools are required which can deal with such uncertain situations arising out of industrial sickness. It is more significant for a country like India where the fruits of developments are rarely equally distributed. In this paper, we propose an approach based on certain soft-computational tools specially using Artificial neural network (ANN) to deal with industrial sickness with specific focus on a few such units taken from a less-developed northeast (NE) Indian state like Assam. More specifically, we here propose, a soft-computational tool which formulates certain decision support mechanism to decide upon industrial sickness using eight different parameters which are directly related to the stages of sickness of such units. The mechanism primarily identifies a few stages of industrial health using various inputs provided in terms of the eight identified parameters. This decision is further compared with the results obtained from another set of ANNs where the model uses certain signals and symptoms of industrial health to decide upon the state of a unit. Specifically, we train multiple ANN blocks with data obtained from a few selected units of Assam so that required decisions related to industrial health could be taken. The system thus formulated could become an important part of planning and development. It can also contribute toward computerization of decision support systems related to industrial health and help in better management.