International Journal of Industrial Engineering & Production Research

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 Purpose-The purpose of this paper is to review a sample of the literature relating to Interpretive Structural Modelling (ISM) and its deployment for modelling purposes in the area of supply chain management (SCM).
Design/methodology/approach- The literature is examined from the three perspectives. First, concept of ISM and examines ISM as modelling technique. Second, use of ISM by the various researchers in their research for modelling. Third, use of ISM for modelling in the area of supply chain management. Findings- ISM is a systematic application of some elementary graph theory in such a way that theoretical, conceptual and computational advantage are exploited to explain the complex pattern of conceptual relations among the variables. From the literature review, we can conclude that many researchers have used ISM for modelling the variables of: reverse logistics, vendor managed inventory, IT enabled supply chain management etc.
Research limitation/implications-The scope of this literature review is by design limited to ISM and it does not cover in investigating other modelling techniques. Literature review investigates sample of important and influential work in the area of application of ISM in the research.
Originality/Value-This study reviews a sample of recent and classic literature in this field and in doing so this paper provides some comprehensive base and clear guidance to researchers in developing, defining and presenting their research agenda for applying ISM methodology in a systematic and convincing manner.
 Key words: Interpretive Structural Modelling, SCI, SMEs, SCM

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The present study is focused to investigate the effect of the various machining input parameters such as cutting speed (v_c), feed rate (f), depth of cut, and nose radius (r) on output i.e. surface roughness (R_a and R_q) and metal removal rate (MRR) of the C40 steel by application of an artificial neural network (ANN) method.  ANN is a soft computing tool, widely used to predict, optimize the process parameters. In the ANN tool, with the help of MATLAB, the training of the neural networks has been done to gain the optimum solution. A model was established between the computer numerical control (CNC) turning parameters and experimentally obtained data using ANN and it was observed from the result that the predicted data and measured data are moderately closer, which reveals that the developed model can be successfully applied to predict the surface roughness and material removal rate (MRR) in the turning operation of a C40 steel bar and it was also observed that lower the value of surface roughness (R_a and R_q) is achieved at the cutting speed of 800 rpm with a feed rate of 0.1 mm/rev, a depth of cut of 2 mm and a nose radius of 0.4 mm.