Application Potential of Multidimensional Scaling for the Design of DSS in Transport Insurance

Abstract

Transport risks in supply chains have increasingly lead to significant capital losses. Insurance claims against such losses have grown accordingly, while simultaneous advances in technology lead to continuously larger volumes of data recorded. Traditional risk evaluation methods in insurance struggle to account for rising supply chain complexity which is reflected by growing amount and dimensionality of supply chain data. Therefore decision-makers in the transport insurance industry need new ways of appropriate knowledge representation to support transport insurance providers with daily tasks such as premium tariffing. This paper presents a method based on multidimensional scaling (MDS) for the identification of groups of similar claims as a first step towards the improvement of supply chain risk evaluation and forecasting. We show the application potential of transforming and visualising transport damage claims data as the basis for developing decision support systems (DSS) to support transport insurance providers in tasks such as premium tariffing as well as transport and supply chain managers in risk mitigation and prevention activities.