Antagonistic threats in the supply chain

Paper in proceeding, 2016

Purpose: Antagonistic threats, such as diesel theft and truck intrusions are heavily affecting road hauliers in Europe and is on the agenda of both transport managers and policy makers. Previous efforts have focused on analysing crime statistics and possibilities of using technology to create safe parking. The statistics used has typically been historical data on thefts, offering limited implications. Hence the purpose of this paper is firstly, to analyse diesel thefts and to demonstrate how to combine official statistics with crowdsourced data collection in research. Research Approach: We are applying hierarchal regression analysis to investigate correlations among different factors to describe transport threats. We combine a secondary data set on transport crime from the Swedish Police with primary data on observations of foreign trucks. Findings and Originality: Larger municipalities in Sweden subsequently have more transports and consistently are more struck with transport crime. Our model shows that variations in crime levels between municipalities strongly correlates to observations of foreign trucks. Research Impact: This research shows the potential of combining official statistics with crowdsourced data. Our analysis model is limited to datasets from Sweden. Another limitation is the quality of both the crowdsourced primary data and the secondary data from the Swedish Police. Practical Impact: Our findings imply that transportation is significantly more vulnerable to antagonistic threats in certain geographical areas. For policy makers and practitioners these findings give useful implications for planning of security measures. This paper is to the authors’ knowledge the first exploratory study using the combination of official statistics and crowdsourced data.