What Industry 4.0 Means for Just-In-Sequence Supply in Automotive Industry?

Juhász, János; Bányai, Tamás

doi:10.1007/978-3-319-75677-6_19

Cited by 7 publications

(5 citation statements)

References 30 publications

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“…Hence, the trust level among the devices can be increased exponentially when friends (IoT devices) interact with each other. This phenomenon was discussed by Rabadiya, Makwana [7] and Atzori, Iera [10], among others, in earlier studies.…”

Section: Introductionmentioning

confidence: 73%

“…Once data collection is completed after processing, analysis, and mining, the acquired data can provide diverse intelligent services. However, from the perspective of scalability, it has been observed that IoT search engines are not scalable with respect to the number of devices [7], and hence, cannot handle a huge number of received queries. In the IoT, there exists the possibility of fake objects, such as people who cannot get the right services, and objects may work as malware, hacking data from other objects.…”

Section: Introductionmentioning

confidence: 99%

“…In the IoT, there exists the possibility of fake objects, such as people who cannot get the right services, and objects may work as malware, hacking data from other objects. The attacker nodes in that network include smart dust and fake particles that can steal secure information concerning military situations (e.g., at the border of a country) [7]. In order to avoid such problems, the social Internet of Things proposes the integration of social networking and the IoT.…”

Section: Introductionmentioning

confidence: 99%

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Towards Trust and Friendliness Approaches in the Social Internet of Things

2019

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The Internet of Things (IoT) is an interconnected network of heterogeneous entities, such as sensors and embedded devices. During the current era, a new field of research has emerged, referred to as the social IoT, which mainly includes social networking features. The social IoT refers to devices that are capable of creating interactions with each other to independently achieve a common goal. Based on the structure, the support of numerous applications, and networking services, the social IoT is preferred over the traditional IoT. However, aspects like the roles of users and network navigability are major challenges that provoke users’ fears of data disclosure and privacy violations. Thus, it is important to provide reliable data analyses by using trust- and friendliness-based properties. This study was designed because of the limited availability of information in this area. It is a classified catalog of trust- and friendliness-based approaches in the social IoT with important highlights of important constraints, such as scalability, adaptability, and suitable network structures (for instance, human-to-human and human-to-object). In addition, typical concerns like communities of interest and social contacts are discussed in detail, with particular emphasis on friendliness- and trust-based properties, such as service composition, social similarity, and integrated cloud services.

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Section: Introductionmentioning

confidence: 73%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Towards Trust and Friendliness Approaches in the Social Internet of Things

2019

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“…Then, motivated from the gaps in the literature, a model structure was developed. Four models were proposed with direct/indirect supply and sequencing, with/without horizontal cooperation [9].…”

Section: What Industry 40 Means For Just-in-sequence Supply In Automo...mentioning

confidence: 99%

Cooperation in Logistics Technology Research: How Twinning Project Affects R+d in the Field of Logistic Systems and Networks

Bányai

Illés

Landschützer

et al. 2018

ALS

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The overall aim of the UMi-TWINN project was to reinforce the scientific excellence and innovation capacity in logistic systems of the University of Miskolc and its high-quality twinning partners for the benefit of different industries and logistics markets. To boost twinning partners' scientific excellence and innovation capacity in logistics technologies, as well as implementing a research and innovation strategy we have focused on the following three sub-topics: design of logistic systems and networks, intelligent transport systems and dynamical analysis of materials handling machines. Within the frame of this paper the authors describe the main results in the field of design of logistic systems.

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“…To adopt with an Industry 4.0 environment, extensive cutting-edge applications have been landed within logistics 4.0. Juhász and Bányai (2018) identified challenges of just-in-sequence supply in the automotive industry from the aspect of Industry 4.0 solutions and detected impacts of Industry 4.0 paradigm on just-in-sequence supply. Ivanov, Dolgui, Sokolov, Werner, and Ivanova (2016) proposed a dynamic model and algorithm for short-term supply chain scheduling problem that simultaneously considered both machine structure selection and job assignments in smart factories.…”

Section: Introductionmentioning

confidence: 99%

Resilient supplier selection in logistics 4.0 with heterogeneous information

Hasan

Jiang

Ullah

et al. 2020

Expert Systems with Applications

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Supplier selection problem has gained extensive attention in the prior studies. However, research based on Fuzzy Multi-Attribute Decision Making (F-MADM) approach in ranking resilient suppliers in logistic 4.0 is still in its infancy. Traditional MADM approach fails to address the resilient supplier selection problem in logistic 4.0 primarily because of the large amount of data concerning some attributes that are quantitative, yet difficult to process while making decisions. Besides, some qualitative attributes prevalent in logistic 4.0 entail imprecise perceptual or judgmental decision relevant information, and are substantially different than those considered in traditional suppler selection problems. This study develops a Decision Support System (DSS) that will help the decision maker to incorporate and process such imprecise heterogeneous data in a unified framework to rank a set of resilient suppliers in the logistic 4.0 environment.The proposed framework induces a triangular fuzzy number from large-scale temporal data using probability-possibility consistency principle. Large number of non-temporal data presented graphically are computed by extracting granular information that are imprecise in nature. Fuzzy linguistic variables are used to map the qualitative attributes. Finally, fuzzy based TOPSIS method is adopted to generate the ranking score of alternative suppliers. These ranking scores are used as input in a Multi-Choice Goal Programming (MCGP) model to determine optimal order allocation for respective suppliers. Finally, a sensitivity analysis assesses how the Supplier's Cost versus Resilience Index (SCRI) changes when differential priorities are set for respective cost and resilience attributes.

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What Industry 4.0 Means for Just-In-Sequence Supply in Automotive Industry?

Cited by 7 publications

References 30 publications

Towards Trust and Friendliness Approaches in the Social Internet of Things

Towards Trust and Friendliness Approaches in the Social Internet of Things

Cooperation in Logistics Technology Research: How Twinning Project Affects R+d in the Field of Logistic Systems and Networks

Resilient supplier selection in logistics 4.0 with heterogeneous information

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