Purpose – The aim of this paper is to analyze and explain why cargo theft continues to occur in the transport network despite all implemented countermeasures. Design/methodology/approach – The research is based on a logical deductive hypothesis using theories from several scientific fields. This hypothesis is then tested empirically. Credibility is substantiated with the use of several independent official statistical sources and verified with both open-ended qualitative interviews and a quantitative, comparative, geographically controlled survey. Findings – Theft risk arises from different theft opportunities that will always be present in the transport network. The theory of crime displacement provides one likely explanation as to why the absolute reduction, instead of a theft pattern alteration, is very difficult to achieve. The result in this paper substantiates research results in criminology that indicate that causality in crime displacement is hard to establish. Research limitations/implications – This research is limited by the lack of reliable information sources about criminal activities against logistics business. Secondary sources, like official crime statistics, are at best untrustworthy but more likely filled with large parts of hidden statistics. Practical implications – The common-sense feeling about the crime displacement theory that exists in the logistics business needs to be modified. This paper maintains that the understanding of the relationship between potential perpetrators and theft preventing measures is a key issue to reduce theft problems within the transport network. Originality/value – This paper is a step towards bringing theories from criminology into the scientific field of logistics and supply chain risk management.

Purpose - To describe the seasonal patterns of reported cargo theft value and frequency in EMEA (Europe, Middle East, and Africa) countries with respect to different transport chain locations. Design/methodology/approach - This study is based on a system-theoretical approach, which emphasizes a holistic rather than an atomistic view. The research method used in this paper is deductive; the analysis is based on the data taken from IIS (Incident Information Service), a transport-related crime database of TAPA (Transported Asset Protection Association) EMEA; and the result is analyzed and discussed within a frame of reference based on supply chain risk management and criminology theories. Findings - There are seasonal variations in cargo thefts at different transport chain locations during particular months of the year as well as days of the week; however, each transport chain location has a different pattern. Indeed, hot spots, modus operandi, theft-endangered objects, and handling methods change frequently during the period under study. However, the basic theoretical frame of reference continues to be the same. Research limitations/implications - This study is based on theoretical deduction using official statistics regarding antagonistic threats. Its geographical limitation to the EMEA is owing to the limitations of the utilized database, although the frame of reference can be applied to analyze antagonistic threats against transport chains globally. Practical implications - This study is limited by the content and classification within the TAPA EMEA IIS database; nevertheless, this database is the best available one, with reports originating mainly from the industry itself, as different TAPA members anonymously report their losses. Originality/value - This paper is one of the first on supply chain risk management that uses actual crime statistics reported by the industry itself to analyze the occurrence of cargo theft by focusing on the value of the vehicle/goods stolen from transport chain locations.

Purpose – The purpose of this paper is twofold: first, to empirically test whether a “one size fits all” strategy fits the fashion e-commerce business and second, to evaluate whether consumer returns are a central aspect of the creation of profitability and, if so, to discuss the role of returns management (RM) in the supply chain strategy. Design/methodology/approach – Transactional sales and return data were analysed and used to categorise customers based on their buying and returning behaviours, measuring each customer's net contribution margins. Findings – The e-commerce business collects a vast quantity of data, but these data are seldom used for the development of service differentiation. This study analysed behaviour patterns and determined that the segmentation of customers on the basis of both sales and return patterns can facilitate a differentiated service delivery approach. Research limitations/implications – This research empirically supports the theory that customer buying and returning behaviours can be used to appropriately categorise customers and thereby guide the development of a more differentiated service approach. Practical implications – The findings support a differentiated service delivery system that utilises a more dynamic approach, conserving resources and linking the supply chain and/or organisational strategies with customers' buying and returning behaviours to avoid over and underservicing customers. Originality/value – Consumer returns are often viewed as a negative aspect of doing business; interestingly, however, the authors revealed that the most profitable customer is a repeat customer who frequently returns goods.