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The purpose of this paper is to examine the patterns of reported cargo thefts at non-secure parking facilities in Europe, the Middle East, and Africa (EMEA) with respect to stolen value, frequency, incident category, and modi operandi.

This study is based on a system-theoretical approach that emphasizes on a holistic rather than an atomistic view. The research method used in this paper is deductive; the analysis is based on data obtained from the incident information service (IIS), a database of transport-related crimes from the Transported Asset Protection Association (TAPA) in the EMEA region. The results are analysed and discussed within a frame of reference based on supply chain risk management (SCRM) and criminology theories.

We found that 97 per cent of all attacks during a stop occur at non-secure parking locations. Cargo thefts at these locations are more of a volume crime than high-value thefts. Seasonal variations were seen in these thefts, and the most common type was an intrusion on weekdays during winter.

This study is limited by the content of and the classifications within the TAPA EMEA IIS database.

This paper is directly relevant to the current EU discussions regarding the creation of a large number of secure parking facilities in the region.

This is one of the first papers in the field of SCRM that utilizes actual crime statistics reported by the industry to analyse the occurrence of cargo theft by focusing on the non-secure parking aspect in the transport chain.

The purpose of this study is to reveal the magnitude of empty container movements (ECM) arising from cargo seasonality by means of long-term datasets of Turkish terminals. Trade imbalance is one of the well-known major reasons of ECM. Cargo seasonality apart from some other operational drivers and market effect, i.e. commercial decisions of the ship operators, is the major operational driver in Turkish terminals effecting ECM. Furthermore, this study highlights the significance of market effect, leading to take measures for more effective empty container operations in terms of decision makers leading the ship operators.

Time series analysis of full container datasets was performed through X-13ARIMA-SEATS methodology, implementing seasonal adjustment.

The results indicate that 17 of 112 time series in hand, based on a terminal/hinterland, container type and “in and out” foreign trade, exhibit cargo seasonality. Roughly, the amount of ECM originating from cargo seasonality in Turkish terminals represents 10 per cent of total ECM except trade imbalance in those terminals where seasonality is present. This reveals that ECM arising from market effect should not be underestimated.

Reefer container traffic could not be sorted from the datasets.

This paper focuses on one of the major reasons of ECM, cargo seasonality. It brings a novel point of view and interpretations which were not suggested previously about ECM, motivating to overcome inefficiency in container operations.

The purpose of this paper is to describe the seasonal patterns of reported cargo theft value and frequency in Europe, Middle East, and Africa (EMEA) countries with respect to different transport chain locations.

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 Incident Information Service (IIS), a transport-related crime database of Transported Asset Protection Association (TAPA) EMEA; and the result is analyzed and discussed within a frame of reference based on supply chain risk management and criminology theories.

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.

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.

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.

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.

This paper inspects in detail the seasonality (deterministic) in container freight rates, and compares seasonality patterns in different freight rate indices. A deterministic seasonality unit root test is performed to achieve set objectives. This study concludes that all the indices (tested in this paper) exhibit significant deterministic seasonality. For January and August, there is no seasonal effect observed in all five series. At the same time, all the indices except Exports from Europe Rate Index (EEI) exhibit significant seasonal patterns in February, September, and December. All five indices exhibit significant seasonality during May, and the coefficient sign shows a drop in the freight rates. During March, October, and November; it is observed that only EEI exhibit significant seasonal patterns. The results could be beneficial for carriers and agents who are involved in the containerised freight transport business. Also, shippers could get a clear idea about the freight rates' nature across various trade routes.

Resilient route selection for oversized cargoes, one of the general bulk cargoes, has not been adequately optimized in terms of using the Arctic route. This study solves the problem of selecting the optimal shipping routes for oversized cargoes from Busan (South Korea) to Balkhash (Kazakhstan).

The study used the consistent fuzzy preference relation (CFPR) method, which is used to solve multi-criteria decision-making (MCDM) and uncertainty problems, to tackle the route selection. This method involves three procedures: first, the critical factors and alternative routes were obtained by the previous literature and an in-depth interview of experts of oversized cargo-handling with more than 20 years of working experience; second, the weightings for each critical factor were identified using the CFPR calculation process and third, alternative routes were evaluated using weighted critical factors.

The Northern Sea Route (NSR) combined with the inland waterways of Russia and Kazakhstan was first suggested for bulk carriers that handle oversized cargoes. The NSR could be a suitable route from Busan to Cape Kamenny of the Russian transshipment seaport, where oversized cargoes will be transferred to the river barge at Cape Kamenny, covering 4,913 km from the latter to Balkhash of Kazakhstan via the Ob/Irtysh River.

This study equips stakeholders in route selection for cargoes with strategies and methods to improve transportation efficiently and enhance shipping routes between Asia and the Commonwealth of Independent States (CIS). In addition to oversized cargoes, coal and timber from Russia can be transported to Asia using inland waterways and the NSR, which can also be used to transport plant equipment for petroleum refineries among Asian countries.

This is the first study to evaluate the suitability of the Artic route for oversized cargoes from South Korea to Kazakhstan. It provides a comprehensive evaluation framework of multimodal shipping routes and offers references for decision-makers when dealing with similar problems.

Cargo loss has been a major issue in logistics management. However, few studies have tackled the issue of cargo loss severity via business analytics. Hence, the purpose of this paper is to provide guidance about how to retrieve valuable information from logistics data and to develop cargo loss mitigation strategies for logistics risk management.

This study proposes a research design of business analytics to scrutinize the causes of cargo loss severity.

The empirical results of the decision tree analytics reveal that transit types, product categories, and shipping destinations are key factors behind cargo loss severity. Furthermore, strategies for cargo loss prevention were developed.

The proposed framework of cargo loss analytics provides a research foundation for logistics risk management.

Companies with logistics data can utilize the proposed business analytics to identify cargo loss factors, while companies without logistics data can employ the proposed cargo loss mitigation strategies in their logistics systems.

This pioneer empirical study scrutinizes the critical cargo loss issues of cargo damage, cargo theft, and cargo liability insurance through exploiting real cargo loss data.

The purpose of this paper is to explore cargo theft risk and security for different product types at different locations along a transport chain.

This study is based on a system-theoretical approach. The research method is deductive, as the analysis is based on secondary data and results from a questionnaire. The results are analysed based on supply chain risk management (SCRM) theories.

Due to substantial interaction effects, the type of product and transport chain location must be considered to determine the correct level of security. Specifically, the product type is more significant, since the general cargo theft risk is higher. Furthermore, the transport industry has three perspectives on security responses to cargo theft, namely, demanded, needed and actual security, which differ depending on the product type and transport chain location.

This database is structured according to the global Transported Asset Protection Association organisational structure, which implies that there are three main databases: Europe, Middle East and Africa, Americas, and Asia-Pacific.

This study has both research and practical implications, as it examines security within freight transport from three perspectives, linked to general cargo theft risk and goods owners’ requirements.

This study addresses the contemporary SCRM problem of cargo theft using actual crime statistics and the industry understanding of required generic security levels.

Virgin Atlantic Cargo is one of the largest air freight operators in the world. As part of a wider strategic development initiative, the company has identified forecasting accuracy as of strategic importance to its operational efficiency. This is because accurate forecast enables the company to have the right resources available at the right place and time. The purpose of this paper is to undertake an evaluation of current month-to-date forecasting utilized by Virgin Atlantic Cargo. The study employed demand patterns drawn from historical data on chargeable weight over a seven-year-period covering six of the company's routes.

A case study is carried out, where a comparison between forecasting models is undertaken using error accuracy measures. Data in the form of historical chargeable weight over a seven-year-period covering six of the company's most profitable routes are employed in the study. For propriety and privacy reasons, data provided by the company have been sanitized.

Preliminary analysis of the time series shows that the air cargo chargeable weight could be difficult to forecast due to demand fluctuations which appear extremely sensitive to external market and economic factors.

The study contributes to existing literature on air cargo forecasting and is therefore of interest to scholars examining the problems of overbooking. Overbooking which is employed by air cargo operators to hedge against “no-show” bookings. However, the inability of air cargo operators to accurately predict cargo capacity unlikely to be used implies that operators are unable to establish with an aspect of certainty their revenue streams. The research methodology adopted is also predominantly discursive in that it employs a synthesis of existing forecasting literature and real-life data for accuracy analysis.

While previous studies focused mainly on East Asia to Europe or United States trade routes, in recent years, trade among South-East Asian countries has increased notably. The price of transporting a container is not fixed and can fluctuate heavily over the course of a week. Besides, extant literature only identified seasonality patterns in the container freight market, but did not explore route-varying seasonality patterns. Hence, this study analyses container freight seasonality patterns of the six South-East Asian routes of the South-East Asian Freight Index (SEAFI) and the index itself and forecasts them.

Data of the composite SEAFI and six routes are collected from the Shanghai Shipping Exchange (SSE) including 167 weekly observations from 2016 to 2019. The SEAFI and individual route data reflect spot rates from the Shanghai Port to South-East Asia base ports. The authors analyse seasonality patterns using polar plots. For forecasting, the study utilize two univariate models, autoregressive integrated moving average (ARIMA) and seasonal autoregressive neural network (SNNAR). For both models, the authors compare forecasting results of original level and log-transformed data.

This study finds that the seasonality patterns of the six South-East Asian container trade routes are identical in an overall but exhibits unique characteristics. ARIMA models perform better than SNNAR models for one-week ahead test-sample forecasting. The SNNAR models offer better performance for 4-week ahead forecasting for two selected routes only.

Major industry players such as shipping lines, shippers, ship-owners and others should take into account the route-level seasonality patterns in their decision-making. Forecast analysts can consider using the original level data without log transformation in their analysis. The authors suggest using ARIMA models in one-step and four-step ahead forecasting for majority of the routes. The SNNAR models are recommended for multi-step forecasting for Shanghai to Vietnam and Shanghai to Thailand routes only.

This study analyses a new shipping index, that is, the SEAFI and its underlying six routes. The authors analyze the seasonality pattern of container freight rate data using polar plot and perform forecasting using ARIMA and SNNAR models. Moreover, the authors experiment forecasting performance of log-transformed and non-transformed series.