Author(s): Roli Dave and Vivek Khanzode
Background: The human-machine model was developed to facilitate the study of ergonomics with cognisable technology in industrial practices. This model illustrated the components of the work system and their interactions. Industrial practices are defined as work system and incidents and accidents may be termed as work system failure. Presently, preventive and corrective maintenance theories are based on component life cycles and the extent of exploitation. The complexities of the work system have increased with increasing levels of automation. However, the interactions in the work system have given marginal importance towards the study of work system failure. Purpose: This study investigates the importance of interactions of work system components. The hierarchical structural model of work system components was developed and their classification was carried out based on the nature of interactions in the work system. Method: The components and interactions in the human-machine model have been identified by a three-pronged approach using a literature review, thought experiment and expert opinion. The contextual relationship of the components and their interactions have been studied for their hierarchical structure using of level partitioning through Interpretive Structure modelling (ISM). The classification of work system components has been carried out by using MICMAC analysis. Findings: Environment, control and controlled process are positioned at a higher level in the hierarchical structure model and require emphasis. The environment and sensor are classified as driver components and influence the work system interactions of other components. Conclusion: The integrated approach of ISM and MICMAC analysis provided the hierarchical structure and classification of work system components. The level of components indicates the significance of components in the work system and the driving and dependence power of components indicate nature of components to focus to prevent work system failure. Practical Implication: The outcome of the study would be useful to the work system designer and operator to minimize the work system failure. Original Contribution: This is the first study to bring out the significance and classification of work system interactions. The finding is strengthened with a computer-based statistical approach without individual opinion biases