Assessing the implementation feasibility of intelligent production systems based on cloud computing, industrial internet of things and business social networks

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jiabao Sun ◽  
Ting Yang ◽  
Zhiying Xu
Keyword(s):  
Social Networks ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Resource Sharing ◽  
Manufacturing Systems ◽  
Production Systems ◽  
Industrial Internet Of Things ◽  
Content Type ◽  
Industrial Internet ◽  
The Impact

PurposeThe increasing demands for customized services and frequent market variations have posed challenges to managing and controlling the manufacturing processes. Despite the developments in literature in this area, less consideration has been devoted to the growth of business social networks, cloud computing, industrial Internet of things and intelligent production systems. This study recognizes the primary factors and their implications for intelligent production systems' success. In summary, the role of cloud computing, business social network and the industrial Internet of things on intelligent production systems success has been tested.Design/methodology/approachIntelligent production systems are manufacturing systems capable of integrating the abilities of humans, machines and processes to lead the desired manufacturing goals. Therefore, identifying the factors affecting the success of the implementation of these systems is necessary and vital. On the other hand, cloud computing and the industrial Internet of things have been highly investigated and employed in several domains lately. Therefore, the impact of these two factors on the success of implementing intelligent production systems is examined. The study is descriptive, original and survey-based, depending on the nature of the application, its target and the data collection method. Also, the introduced model and the information collected were analyzed using SMART PLS. Validity has been investigated through AVE and divergent validity. The reliability of the study has been checked out through Cronbach alpha and composite reliability obtained at the standard level for the variables. In addition, the hypotheses were measured by the path coefficients and R2, T-Value and GOF.FindingsThe study identified three variables and 19 sub-indicators from the literature associated that impact improved smart production systems. The results showed that the proposed model could describe 69.5% of the intelligence production systems' success variance. The results indicated that business social networks, cloud computing and the industrial Internet of things affect intelligent production systems. They can provide a novel procedure for intelligent comprehensions and connections, on-demand utilization and effective resource sharing.Research limitations/implicationsStudy limitations are as below. First, this study ignores the interrelationships among the success of cloud computing, business social networks, Internet of things and smart production systems. Future studies can consider it. Second, we only focused on three variables. Future investigations may focus on other variables subjected to the contexts. Ultimately, there are fewer experimental investigations on the impact of underlying business social networks, cloud computing and the Internet of things on intelligent production systems' success.Originality/valueThe research and analysis outcomes are considered from various perspectives on the capacity of the new elements of Industry 4.0 for the manufacturing sector. It proposes a model for the integration of these elements. Also, original and appropriate guidelines are given for intelligent production systems investigators and professionals' designers in industry domains.

Industrial internet of things for smart manufacturing applications using hierarchical trustful resource assignment

Work ◽  
2021 ◽  
pp. 1-11
Author(s):  
Duan Pingli ◽  
Bala Anand Muthu ◽  
Seifedine Nimer Kadry
Keyword(s):  
Internet Of Things ◽  
Manufacturing Industry ◽  
Production Systems ◽  
Intelligent Manufacturing ◽  
Smart Manufacturing ◽  
The Internet ◽  
Resource Assignment ◽  
Industrial Internet Of Things ◽  
Assignment Method ◽  
Industrial Internet

BACKGROUND: The manufacturing industry undergoes a new age, with significant changes taking place on several fronts. Companies devoted to digital transformation take their future plants inspired by the Internet of Things (IoT). The IoT is a worldwide network of interrelated physical devices, which is an essential component of the internet, including sensors, actuators, smart apps, computers, mechanical machines, and people. The effective allocation of the computing resources and the carrier is critical in the industrial internet of Things (IIoT) for smart production systems. Indeed, the existing assignment method in the smart production system cannot guarantee that resources meet the inherently complex and volatile requirements of the user are timely. Many research results on resource allocations in auction formats which have been implemented to consider the demand and real-time supply for smart development resources, but safety privacy and trust estimation issues related to these outcomes are not actively discussed. OBJECTIVES: The paper proposes a Hierarchical Trustful Resource Assignment (HTRA) and Trust Computing Algorithm (TCA) based on Vickrey Clarke-Groves (VGCs) in the computer carriers necessary resources to communicate wirelessly among IIoT devices and gateways, and the allocation of CPU resources for processing information at the CPC. RESULTS: Finally, experimental findings demonstrate that when the IIoT equipment and gateways are valid, the utilities of each participant are improved. CONCLUSION: This is an easy and powerful method to guarantee that intelligent manufacturing components genuinely work for their purposes, which want to integrate each element into a system without interactions with each other.

Blockchain-Based Industrial Internet of Things for the Integration of Industrial Process Automation Systems

2021 ◽  
pp. 163-186
Author(s):  
Charles Tim Batista Garrocho ◽  
Célio Márcio Soares Ferreira ◽  
Carlos Frederico Marcelo da Cunha Cavalcanti ◽  
Ricardo Augusto Rabelo Oliveira
Keyword(s):  
Internet Of Things ◽  
Industrial Process ◽  
Industrial Processes ◽  
Process Automation ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Automation Systems ◽  
Machine Communication ◽  
Time Systems ◽  
The Impact

The industrial internet of things is expected to attract significant investment to the industry. In this new environment, blockchain presents immediate potential in industrial IoT applications, offering several benefits to industrial cyber-physical systems. However, works in the blockchain literature target environments that do not meet the reality of the factory and do not assess the impact of the blockchain on industrial process requirements. Thus, this chapter presents an investigation of the evolution of industrial process automation systems and blockchain-based applications in the horizontal and vertical integration of the various systems in a supply chain and factories. In addition, through an investigation of experimental work, this work presents issues and challenges to be faced for the application of blockchain in industrial processes. Evaluations and discussions are mainly focused on aspects of real-time systems in machine-to-machine communication of industrial processes.

scholarly journals Techniques for designing value propositions applicable to the concept of outcome-economy

2017 ◽  
Vol 9 (1) ◽  
pp. 56-63 ◽  
Author(s):  
Sylwia Gierej
Keyword(s):  
Internet Of Things ◽  
Value Proposition ◽  
Industrial Internet Of Things ◽  
A Value ◽  
Industrial Internet ◽  
Innovative Solutions ◽  
Value Propositions ◽  
The Impact

Abstract The purpose of this article is to analyse and present some techniques that support the design of a value proposition in the context of the outcome-economy. The proposed techniques are intended to support traditional companies in the design of innovative solutions. Also, the discussed techniques were compared to identify the most effective. The study was conducted based on the information available in the literature on the impact of the Industrial Internet of Things on the economy and creation of a value proposition.

scholarly journals Research on Intelligent Scheduling Mechanism in Edge Network for Industrial Internet of Things

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Zhenzhong Zhang ◽  
Wei Sun ◽  
Yanliang Yu
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Task Scheduling ◽  
Edge Computing ◽  
The Internet ◽  
Computing Technology ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Internet Of Things Technology

With the vigorous development of the Internet of Things, the Internet, cloud computing, and mobile terminals, edge computing has emerged as a new type of Internet of Things technology, which is one of the important components of the Industrial Internet of Things. In the face of large-scale data processing and calculations, traditional cloud computing is facing tremendous pressure, and the demand for new low-latency computing technologies is imminent. As a supplementary expansion of cloud computing technology, mobile edge computing will sink the computing power from the previous cloud to a network edge node. Through the mutual cooperation between computing nodes, the number of nodes that can be calculated is more, the types are more comprehensive, and the computing range is even greater. Broadly, it makes up for the shortcomings of cloud computing technology. Although edge computing technology has many advantages and has certain research and application results, how to allocate a large number of computing tasks and computing resources to computing nodes and how to schedule computing tasks at edge nodes are still challenges for edge computing. In view of the problems encountered by edge computing technology in resource allocation and task scheduling, this paper designs a dynamic task scheduling strategy for edge computing with delay-aware characteristics, which realizes the reasonable utilization of computing resources and is required for edge computing systems. This paper proposes a resource allocation scheme combined with the simulated annealing algorithm, which minimizes the overall performance loss of the system while keeping the system low delay. Finally, it is verified through experiments that the task scheduling and resource allocation methods proposed in this paper can significantly reduce the response delay of the application.

Reconceptualizing value innovation for Industry 4.0 and the Industrial Internet of Things

2019 ◽  
Vol 34 (6) ◽  
pp. 1203-1209 ◽  
Author(s):  
Paul Matthyssens
Keyword(s):  
Internet Of Things ◽  
Industry 4.0 ◽  
Business Models ◽  
Industrial Internet Of Things ◽  
Value Innovation ◽  
Content Type ◽  
Innovation Research ◽  
Industrial Internet ◽  
Challenges And Opportunities ◽  
New Research

Purpose Starting from the foundations of value innovation, this paper aims to give an idea of the key drivers and barriers – internal and external to the company – and to provide insight into proven capabilities underscoring the ability to create a flow of new value initiatives. These thoughts are then confronted with the present challenges of Industry 4.0 and the Industrial Internet of Things (IIoT). The confrontation leads to the identification of five capabilities for future-proof value innovation. Design/methodology/approach Literature review based upon the work of the author with more than two decades of experience within value innovation research is included. The review is supplemented with recent literature and an overview of the challenges of Industry 4.0/IIoT, which leads into a confrontation of the present status of value innovation with future requirements. Findings Value innovation remains important specifically for established companies facing path-breaking digital disruption of their existing business models provoked by Industry 4.0 and IIoT. Five key capabilities are suggested to rejuvenate value innovation and prepare it for the Industry 4.0 challenge: capabilities for designing, adapting and marketing product service systems; capabilities for blending digital strategy and processes with value offerings; capabilities for designing and mobilizing ecosystems and integrating these into a value-based IIoT platform; capabilities for combining and integrating technological and value innovation approaches; and capabilities for linking value creation to value capturing. Research limitations/implications This paper is more of a “viewpoint” than an empirically based paper presenting new research findings. It is based on expert judgment and confrontation with extant literature. The outlook indicating five key capabilities needs further empirical corroboration. Practical implications The overview of barriers and the “toolkit” for value innovation (Figure 1) and the five capabilities for future value innovation are expected to be managerially relevant. Originality/value The paper highlights the concept of value innovation, as discussed over the past decades, and links it to recent challenges and opportunities imposed by Industry 4.0 and the IIoT. The concept of value or strategic innovation is still valid but needs a re-conceptualization in view of these developments. The paper provides five capabilities business marketers should develop to perform value innovation in an Industry 4.0 environment.

Blockchain and the Industrial Internet of Things

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nils Siegfried ◽  
Tobias Rosenthal ◽  
Alexander Benlian
Keyword(s):  
Internet Of Things ◽  
Industrial Internet Of Things ◽  
Content Type ◽  
Blockchain Technology ◽  
Distributed Ledger ◽  
Industrial Internet ◽  
Mapping Process ◽  
System Requirements ◽  
And Performance ◽  
Practical Implications

Purpose The purpose of this paper is to investigate the suitability of Blockchain technology for applications in the Industrial Internet of Things (IIOT). It provides a taxonomy of system requirements for such applications and maps these requirements against the Blockchain’s technological idiosyncrasies. Design/methodology/approach A requirement taxonomy is built in an iterative process based on a descriptive literature review. In total, 223 studies have been screened leading to a relevant sample of 48 publications that were analyzed in detail regarding posed system requirements. Subsequently, Blockchain’s capabilities are discussed for each requirement dimension. Findings The paper presents a taxonomy of six requirement dimensions. In the mapping process, areas of greater fit (e.g., reliability, nonrepudiation and adaptability) were identified. However, there are also several constraints (e.g., scalability, confidentiality and performance) that limit the use of Blockchain. Research limitations/implications Due to the limited amount of studies and the vibrant development of Blockchain technology, the results may benefit from practical evidence. Researchers are encouraged to validate the results in qualitative practitioner interviews. Focusing on literature-backed public Blockchain, idiosyncrasies of private implementations and specific distributed ledger technologies may be discussed in future studies. Practical implications The paper includes use cases for Blockchain in manufacturing and IIOT applications. Potential caveats for practitioners are presented. Originality/value This paper addresses the need to understand to which degree Blockchain is a suitable technology in manufacturing, especially in context of the IIOT. It contributes a requirement taxonomy which serves as the foundation for a systematic fit assessment.

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