Cargo Securing - Comparison of Different Quality Roads

Vlkovský, Martin; Veselík, Petr

doi:10.11118/actaun201967041015

Cited by 13 publications

(6 citation statements)

References 11 publications

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“…Th e number of individual supply chain elements and their interconnections create a complex system which might also carry an inherent risk (Vlkovský, 2019). Th erefore, it is substantial to identify the supply chain´s overall capabilities to defi ne threats and sources of disruption, with the potential to improve resilience and long-term operational sustainability.…”

Section: Supply Chain Disruption Approachesmentioning

confidence: 99%

Increase supply chain resilience by applying early warning signals within big-data analysis

Nagy

Foltin

2022

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Purpose: The current environment of globally interconnected supply chains and the dynamics of changes and potential threats significantly reduce the time for a possible response. At the same time, there is a growing demand for information necessary to mitigate the consequences. To minimise the damage and increase the resilience of supply chains, it is necessary to identify sources of threats promptly, the extent of possible damage and the possibility of preventing or minimising their impact. The aim of the paper is to structure supply chain threats and search for appropriate datasets. Methodology: The paper analyses the state-of-the-art through a comprehensive literature review and demonstrates secondary data about how free-access business data can be used as Early Warning Signals to forecast supply chain disruptive events, with a particular focus on international maritime transportation. Results: It was confirmed that companies can access many open datasets, and collecting and aggregating these data can improve their preparedness for future disruptive events. As the most important issue, the authors defined the selection of proper datasets and interpreting results with foresight. Conclusions: Identifying and analysing the relevant Early Warning Signals by companies to prevent supply chain disruptions are essential for keeping their supply chains sustainable and their resilience on a sufficient level. It was proved that general business indicators (PMI delivery time, container capacity, inflation rate, etc.) can help to signal the increasing possibility of maritime traffic problems in ports and container unavailability as usual supply chain disruption types in recent years. Therefore, the companies in the supply chains need to find, collect and analyse the appropriate data, which are, in some cases, free and available. However, it is a substantial task for data analysts to identify the most relevant data and work out the analytical methodology which can be applied as Early Warning Systems (EWS).

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Section: Supply Chain Disruption Approachesmentioning

confidence: 99%

Increase supply chain resilience by applying early warning signals within big-data analysis

Nagy

Foltin

2022

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“…Listed publications discuss, among other things, the differences between the parametric approach of the relevant standards and directives (usage of averaged coefficients), the practical requirements for cargo securing, and the cargo layout and the choice of its securing system. On the basis of our own executed transport experiments and analyses (e.g., [20][21][22]), it is apparent that, particularly in the case of lesser quality roads, selected vehicles (e.g., those with off-road capabilities), or in the case of vehicles with a cargo of less than 50% of the utility weight of the vehicle, the shock values may significantly exceed the limits stipulated by regulations [5]. This fact can have an impact not only on the costs associated with transport, but in extreme situations it can also lead to a traffic accident and have impacts not only economical, but also impacts associated with human health or the environment [23,24].…”

Section: Literature Reviewmentioning

confidence: 99%

Improvement of Road Safety through Appropriate Cargo Securing Using Outliers

et al. 2021

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The article focuses on evaluating a transportation experiment that intends to improve road safety by analyzing transport shocks that significantly affect the system of securing the load, vehicle, driver, and other aspects. Within Europe, improper or insufficient securing of loads causes up to 25% of all cargo vehicle accidents. Our transport experiment consists of eight rides of a Tatra truck. The measured values of shocks (acceleration coefficients) are statistically evaluated. Three hypotheses are established for these purposes. First, it is proven that the probability distributions of the shocks values differ statistically significantly among individual rides, namely in their shape and median value. Thus further statistical analyses are performed separately for the individual rides, axes, and the accelerometer locations. These analyses prove significant exceedances of the normatively set limits given by the EN 12195-1:2010 standard, which is potentially risky. Especially for the z axis and y axis, the set 20% limit was exceeded in 75.0% and 56.3% of cases, respectively. In the case of the x axis, the established 20% limit was practically not exceeded at all. The analysis of exceeding the permitted limits (the statistical evaluation of the second and third established hypothesis) is based on boxplots that graphically describe the individual rides, as well as on the found contaminated log-normal distribution of the values of the acceleration coefficients in the individual rides. The last hypothesis regarding exceeding the double value of the permitted limit is rejected. Methods of statistical comparison are used during data analysis. The probability distribution of acceleration coefficients is modeled using a contaminated log-normal distribution.

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“…In order to obtain the data, three measurements (transportation experiments) were taken using respective vehicles on respective types of roads without cargo. For the first two transportation experiments, from which datasets formally designated as d 1 and d 2 were obtained, an off-road truck Tatra 810-V-1R0R26 13 177 6×6.1R (further in the text only "Tatra 810") [22] was used. Tatra 810 has gross weight of 13,000 kg and curb weight 8,500 kg.…”

Section: Transportation Experimentsmentioning

confidence: 99%

Impact of Vehicle Type and Road Quality on Cargo Securing

Vlkovský¹

2020

Komunikácie

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The article is concerned with comparison of the three transportation experiments using the two vehicle types (Tatra 810 and Tatra 815 MK IV) on two road types (higher and lower quality). From the transportation experiments, three datasets were obtained to be compared mutually and to the normatively determined values of acceleration coefficients, as well. In the article, the calculation of the probability of excess and double excess of the normatively determined limits is also included. For the purpose of the comparison, statistical tests of equality and three different parameters are employed. Statistically significant differences between the datasets are the outcome of the article, i.e. there are statistically significant differences between the vehicles and roads tested with regard to generated shocks. The results are primarily usable in the cargo securing system optimisation in road transportation and transport safety.

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Cargo Securing - Comparison of Different Quality Roads

Cited by 13 publications

References 11 publications

Increase supply chain resilience by applying early warning signals within big-data analysis

Increase supply chain resilience by applying early warning signals within big-data analysis

Improvement of Road Safety through Appropriate Cargo Securing Using Outliers

Impact of Vehicle Type and Road Quality on Cargo Securing

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