Data Analytics in Rail Transportation: Applications and Effects for Sustainability

Land, Anna; Buus, Andrew; Platt, Alana

doi:10.1109/emr.2019.2951559

Cited by 11 publications

(4 citation statements)

References 5 publications

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“…The results of this study showed that digital technologies have a great impact on the sustainability of logistics in FMCG companies, especially in the economic dimension. Also, Land et al (2020) evaluated the applications of big data analytics to achieve sustainability in rail transportation by collecting data on partners of a buyer-supplier relationship. In their study, they considered one of the largest rail network operators in North America and an IoT provider that supplied hardware platforms, digital video recorders and specialized sensors.…”

Section: Theoretical Foundationsmentioning

confidence: 99%

Section: Theoretical Foundationsmentioning

confidence: 99%

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Proposing a conceptual model of the sustainable digital supply chain in manufacturing companies: a qualitative approach

Amouei

Valmohammadi

Fathi

2023

JEIM

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PurposeIn the digital age, emerging technologies have affected every industry. Information and communications technology and digital technologies have transformed traditional supply chains into smart and more resilient ones, enabling effective management of challenges. Given the importance of the two topics, namely sustainable supply chain management and Industry 4.0 in supply chain management, on the one hand, and the dearth of theoretical research performed in this area on the other, this study aims to propose a conceptual model on a sustainable digital supply chain management in manufacturing companies.Design/methodology/approachThis study utilized a qualitative approach. First, an in-depth review of the relevant literature was done. Then, following a multi-grounded theory methodology, relevant data were gathered by reviewing 92 papers and conducting nine semi-structured interviews with industry experts. These data were analyzed using the MAXQDA software.FindingsA total of 41 concepts, ten sub-components and three main components (dimensions) were extracted, and the proposed conceptual model was presented. Finally, based on this conceptual model, three propositions were suggested.Research limitations/implicationsConsidering that the present study was performed in the context of Iranian manufacturing companies, caution should be exercised in relation to the generalizability of the obtained results. Also, due to the problems in the digital technology infrastructure and the limited use of these technologies by manufacturing companies (emphasized by the interviewees), this study focused on the theoretical dimension of using digital technologies by these companies.Practical implicationsThe proposed comprehensive model can help academicians as well as practitioners to focus better and explore the variables and constructs of the model, paving the way toward successful implementation of digital technologies in the manufacturing supply chain.Originality/valueTo the best knowledge of the authors, this study is among the first of its kind which presents a holistic and comprehensive digital supply chain model aimed at guiding companies to consider sustainability from all the main dimensions and their relevant indicators.

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Section: Theoretical Foundationsmentioning

confidence: 99%

Section: Theoretical Foundationsmentioning

confidence: 99%

Proposing a conceptual model of the sustainable digital supply chain in manufacturing companies: a qualitative approach

Amouei

Valmohammadi

Fathi

2023

JEIM

View full text Add to dashboard Cite

show abstract

“…Policy: Legal issues, government support, policy measures [31], and associated regulatory constraints [82] can inhibit the adoption of digital technologies in logistics. Hence, this criterion refers to all policy measures that are required for the adoption and implementation of digital technologies.…”

Section: Vmentioning

confidence: 99%

Assessment of Digitalized Logistics for Implementation in Low-Income Countries

Tadesse

Gebresenbet

Tavasszy

et al. 2021

Future Transportation

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Integration of digitalization and automation with logistics systems promotes effective and efficient flow of goods, information, and services, contributing to economic development. The level of implementation of digitalization and automation in low-income countries is still low, however. The aim of this study is to establish which digitalized logistics practices could best be adopted by firms in low-income countries. A systematic literature review was used to identify state-of-the-art digitalization and automation technologies in logistics chains. Criteria for adopting digitalized logistics practices were also identified in the literature review. An expert survey was conducted to identify criteria weights using analytical hierarchy process (AHP). Economic benefit, infrastructure, and affordability were the criteria that were given the highest weights by the experts. Case studies that applied state-of-the-art technologies such as internet of things (IoT), radio frequency identification (RFID), blockchain, big data analytics (BDA), and sensors mainly for traceability, production operation, and warehouse and inventory management were considered as recommended practices. Identification of suitable practices considering the local conditions in low-income countries could help logistics professionals and policymakers adopt enabling technologies in logistics chains.

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“…To solve the actual vehicle routing problem in the field of logistics, some scholars proposed an adaptive data-driven modular method (Kumar and Anbanandam, 2020). Land et al (2020) select the largest railway network operator in North America to purchase railway transportation-related data when analyzing railway transportation-related big data and evaluate the impact of data analysis on railway transportation sustainability through these data. Due to the lack of big data research on the supply chain, some scholars explored the mediating role of big data analysis between sustainable supply chain and key factors (Inamdar et al , 2020; Raut et al , 2021).…”

Section: Introductionmentioning

confidence: 99%

Study on the application of big data techniques for the third-party logistics using novel support vector machine algorithm

Sun

Shi

2021

JEIM

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PurposeThis paper aims to effectively explore the application effect of big data techniques based on an α-support vector machine-stochastic gradient descent (SVMSGD) algorithm in third-party logistics, obtain the valuable information hidden in the logistics big data and promote the logistics enterprises to make more reasonable planning schemes.Design/methodology/approachIn this paper, the forgetting factor is introduced without changing the algorithm's complexity and proposed an algorithm based on the forgetting factor called the α-SVMSGD algorithm. The algorithm selectively deletes or retains the historical data, which improves the adaptability of the classifier to the real-time new logistics data. The simulation results verify the application effect of the algorithm.FindingsWith the increase of training times, the test error percentages of gradient descent (GD) algorithm, gradient descent support (SGD) algorithm and the α-SVMSGD algorithm decrease gradually; in the process of logistics big data processing, the α-SVMSGD algorithm has the efficiency of SGD algorithm while ensuring that the GD direction approaches the optimal solution direction and can use a small amount of data to obtain more accurate results and enhance the convergence accuracy.Research limitations/implicationsThe threshold setting of the forgetting factor still needs to be improved. Setting thresholds for different data types in self-learning has become a research direction. The number of forgotten data can be effectively controlled through big data processing technology to improve data support for the normal operation of third-party logistics.Practical implicationsIt can effectively reduce the time-consuming of data mining, realize the rapid and accurate convergence of sample data without increasing the complexity of samples, improve the efficiency of logistics big data mining, reduce the redundancy of historical data, and has a certain reference value in promoting the development of logistics industry.Originality/valueThe classification algorithm proposed in this paper has feasibility and high convergence in third-party logistics big data mining. The α-SVMSGD algorithm proposed in this paper has a certain application value in real-time logistics data mining, but the design of the forgetting factor threshold needs to be improved. In the future, the authors will continue to study how to set different data type thresholds in self-learning.

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Data Analytics in Rail Transportation: Applications and Effects for Sustainability

Cited by 11 publications

References 5 publications

Proposing a conceptual model of the sustainable digital supply chain in manufacturing companies: a qualitative approach

Proposing a conceptual model of the sustainable digital supply chain in manufacturing companies: a qualitative approach

Assessment of Digitalized Logistics for Implementation in Low-Income Countries

Study on the application of big data techniques for the third-party logistics using novel support vector machine algorithm

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