Logistics, Volume 4, Issue 1 (March 2020) – 6 articles

Studies have shown that leading supply chain companies are associated with significantly higher company financial ratios than competitors. In contrast, little research has focused on the financial performance of the affiliated suppliers and customers of such supply chain leader (SCL) companies. Thus, the central purpose of this paper is to determine, from a financial perspective, whether suppliers and customers benefit or lose by participating in a SCL network (so called “financial spillover effects”). Companies that were ranked in the Gartner Supply Chain Top 25 were selected as SCLs. For each selected firm, the five largest suppliers and customers were identified and compared with a control sample from the same industry. In order to elaborate on existing insights into the (financial) outcome of supply chain relationships, we applied an explorative approach with abductive reasoning, while comparing the secondary data for 224 SCL supplier (56 firms) and 168 SCL customer (42 firms) firm-years with 1940 (485 firms) and 1544 (386 firms) control firm-years, respectively. The following insights are made: First, the superior financial performance of SCLs was confirmed. Second, the financial performance of suppliers and customers showed superior liquidity and activity ratios but inferior profitability ratios. Third, suppliers showed much more significant results than customers. Full article

While supply chain analytics shows promise regarding value, benefits, and increase in performance for logistics and supply chain management (LSCM) organizations, those organizations are often either reluctant to invest or unable to achieve the returns they aspire to. This article systematically explores the barriers LSCM organizations experience in employing supply chain analytics that contribute to such reluctance and unachieved returns and measures to overcome these barriers. This article therefore aims to systemize the barriers and measures and allocate measures to barriers in order to provide organizations with directions on how to cope with their individual barriers. By using Grounded Theory through 12 in-depth interviews and Q-Methodology to synthesize the intended results, this article derives core categories for the barriers and measures, and their impacts and relationships are mapped based on empirical evidence from various actors along the supply chain. Resultingly, the article presents the core categories of barriers and measures, including their effect on different phases of the analytics solutions life cycle, the explanation of these effects, and accompanying examples. Finally, to address the intended aim of providing directions to organizations, the article provides recommendations for overcoming the identified barriers in organizations. Full article

The application of different evaluation approaches in logistics requires considering many factors with different significance for making the final decision. Multi-criteria decision-making (MCDM) methods are often applied in logistics to create different strategies and evaluations. In this paper, research has been carried out in a transport system of an international transport company. An MCDM model has been created for the purpose of human resource evaluation, on which the overall efficiency of the company depends. A total of 23 drivers were evaluated on the basis of five crucial criteria in order to increase employees’ motivation through their periodic remuneration. The Full Consistency Method (FUCOM) was applied to determine the significance of the criteria, while the evaluation of potential solutions was performed using Measurement Alternatives and Ranking according to COmpromise Solution (MARCOS). After the results had been obtained, the created model was validated throughout comparisons with seven other MCDM methods. Full article

The port system is immersed in a process of digital transformation towards the concept of Ports 4.0, under the new regulatory and connectivity requirements that are expected of them. As a result of the changes that the industrial revolution 4.0 is imposing, based on new information technologies and the change of energy model, the electrification of modes of transport from alternative energies and the total digitalization of the processes is occurring. This conversion to digital, intelligent, and green ports requires the implementation of the new technologies offered by the market. The inclusion of these enabling tools has allowed the development of automated terminals under a functional approach. This article aims to offer the responsible entities a new methodology (BOT) that allows them to successfully undertake the automation of terminals, taking into account the reality of the conditions of the environment in which they are developed. By quantifying the factors that facilitate or impede implementation, it will be possible to determine the strategy to be followed and the necessary measures to be adopted in the project; constituting, therefore, a novel management and planning tool. Full article

Accurate travel time prediction is of high value for freight transports, as it allows supply chain participants to increase their logistics quality and efficiency. It requires both sufficient input data, which can be generated, e.g., by mobile sensors, and adequate prediction methods. Machine Learning (ML) algorithms are well suited to solve non-linear and complex relationships in the collected tracking data. Despite that, only a minority of recent publications use ML for travel time prediction in multimodal transports. We apply the ML algorithms extremely randomized trees (ExtraTrees), adaptive boosting (AdaBoost), and support vector regression (SVR) to this problem because of their ability to deal with low data volumes and their low processing times. Using different combinations of features derived from the data, we have built several models for travel time prediction. Tracking data from a real-world multimodal container transport relation from Germany to the USA are used for evaluation of the established models. We show that SVR provides the best prediction accuracy, with a mean absolute error of 17 h for a transport time of up to 30 days. We also show that our model performs better than average-based approaches. Full article