Industrial companies are linked with affiliated firms, suppliers and customers through the supply chains. Such chains operate within large-scale networks directly related to distribution and warehousing. In order to meet the market demands and customer new expectations, the various components of the supply chains have to be developed i.a. through implementation of the innovative vehicles, green and blue technologies. Moreover, modernization processes of transport and logistics system need to be resistant to crucial mistakes related to innovative solutions implementation in order to exclude domino effect occurrence. The authors attempt to build topology-based approach to the modernization of transport and logistics systems. It is assumed that each innovation application is the independent element-based coalition, possessing linked object structure. The results of multi-year researches demonstrate the offered approach as a useful tool to analyze innovative changes for obsolete transport and logistics system as hybrid structure. The ways of system structure transformation, as well as possible innovative effects are considered. The preliminary results have been obtained for compositions on meso-level for the case of marine propulsion modernisation. The paper is illustrated by various examples.

Management based on reliable, complete, and up-to-date information is key to increasing a transport chain’s effectiveness and sustainability. The instability of the business environment and the increase in competition have contributed to the growth of challenges faced by managers of transport and logistics companies who make decisions using low-value information exposed to significant distortions. This article aims to investigate the impact of information distortions on decision-making quality and to determine the probability of making trustworthy decisions in freight land–sea transport chain planning. The research was carried out in several stages, which included the formulation of rules for detecting information distortions, as well as distortion clustering and evaluation of their impact on decision quality. A methodology to assess the probability of making trustworthy decisions was developed. It was shown that information value directly impacts the quality of decisions related to transport chain planning for both traditional and unique/occasional freight. In the case of significant information distortion, the manager’s ability to assess available information considerably increases, especially in crises, when the lack of time to verify information threatens the accuracy of decisions. This study’s results may be useful for transport and logistics companies’ managers, who make decisions using information obtained from various sources.

As automotive technology advances in the realm of digitization, vehicles are becoming smarter and, at the same time, more vulnerable to various threats. This paper focuses on techniques for detecting faults to mitigate the risk of freight transportation. Our observations show that vehicle uptime varies significantly even under similar operating conditions. This variation stems from differences in the wear and tear of moving and stationary parts, the characteristics of transported loads, driving styles, the quality of maintenance, etc. These factors are particularly crucial for abnormal vehicles designed to carry AILs (Abnormal Indivisible Loads). Such vehicles are especially prone to surprising threats, requiring efficient techniques for monitoring separate vehicle components and providing drivers with vital information about their operational status. The presented article proposes an original concept of an integrated three-level monitoring system based on the AOP (All-in-One Platform) principle, using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, which is a tool oriented to distinguish points from three categories: basic, boundary, and external. This is a solution not yet found in the literature. It is based on assessments of LOFs (Local Outlier Factors) and to detect anomalies in the measured values of operational parameters. The purpose of our study was to determine whether providing truck drivers with current information from an active threat warning system could help reduce unplanned downtimes.

In the coming decade, the European economy may face a number of new challenges that are capable of undermining the stability of energy supply. These challenges have by external and internal threats related to the technical availability for the transmission in LHR (Long-Haul Routes) mode and energy distribution in the SHR (Short-Haul Routes) mode. Road tank trucks play a key role in liquid fuels delivers, supplying both renewable and fossil fuels. Reduced investment by T&L companies in the new trucks purchase including road tankers increase risk on unplanned trucks downtime, resulting in delivery delays, customer frustration and higher-than-expected operating costs. As our studies have shown, for aging fleet in favor of PdM (Predictive Maintenance) strategy provides greater resilience to unexpected downtime, especially for deliveries with a high VOD (Value Of Delay) rate. Research findings indicate that such downtime can be reduce by 12-18% providing an increase of delivers efficiency in 10-15%, improves spare parts supply, eliminates the need reserve vehicles