Assessing supply side risk in supply chain with pattern matching

Abstract

The paper discusses potential application of fuzzy set theory, more specifically, pattern matching for assessing risk in supply chain. Risk factors have been evaluated using linguistic representations of the extent of risk characteristics involved, their frequency of occurrence, severity of its impact and the uncertainty involved in its control mechanism if any. For each linguistic value, there is corresponding membership function ranging over a universe of discourse. The risk characteristics having highest degree of featural value are taken as the known pattern. Each sample pattern of the other risk characteristics with their known featural values are then matched with the known pattern. The concept of multi-feature pattern matching based on fuzzy logic is used to derive the rank ordering of risk characteristics. A methodology has been developed and the same exemplified by presenting a case example with limited number of risk characteristics.