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Purpose The purpose of this paper is to provide a reliable and accurate instrument to assess the supply chain risk of similar comparable industries. This enables the firms which fall in this category of industry to identify and incorporate suitable risk sources under various risk constructs. This paper also provides a framework to the top management to prioritize the various risk constructs. Design/methodology/approach A systematic approach is used to develop and validate an instrument for assessing overall risk of supply chain. This includes specifying the domain and dimensionality of a construct, generation of initial pool of items, refinement of the initial items by the expert group, assessment of content validity, evaluation of reliability and construct validity of the scale items. Also a higher order measurement model of structural equation modeling is used to prioritize the various risk constructs. Findings The process yielded a robust instrument to assess overall risk of the supply chain. Through empirical verification, this instrument is shown to exhibit high levels of reliability and validity. The framework for prioritization of risk constructs revealed the importance of various supply chain risk constructs. Practical implications The framework is intended to be useful in practice for assessment of overall risk of heavy engineering industries supply chain. The procedure will be extended for development of risk assessment instrument for other industries which shares a common risk profile. Prioritization of various risk constructs with respect to the overall risk enables the top management to focus their attention to plan and manage the supply chain risks based on their relative importance. Originality/value This paper fulfils an identified need for the development of an empirically validated instrument by identifying the significant supply chain risk sources under major supply chain risk constructs for assessing the supply chain risk of industries which are similar in their risk profiles. Also provides a higher order model to identify the most influential risk constructs.
Punniyamoorthy et al. (Fri,) studied this question.