Smart manufacturing systems are growing based on the various requests for predicting the reliability and quality of equipment. Many machine learning techniques are being examined to that end. Another issue which considers an important part of industry is data security and management. To overcome the problems mentioned above, we applied the integrated methods of blockchain and machine learning to secure system transactions and handle a dataset to overcome the fake dataset. To manage and analyze the collected dataset, big data techniques were used. The blockchain system was implemented in the private Hyperledger Fabric platform. Similarly, the fault diagnosis prediction aspect was evaluated based on the hybrid prediction technique. The system's quality control was evaluated based on non-linear machine learning techniques, which modeled that complex environment and found the true positive rate of the system's quality control approach.
Mobile Edge Computing (MEC) paradigm is designed to meet the user requirements by providing cloud services at the edge of the user network. Blockchain technology with the Edge Computing (EC) paradigm is reliable in delivering the edge services depending on user requirements and improving the distributed management of resources at ease. In this article, blockchain-assisted data offloading for Availability Maximization (BDO-AM) is introduced. This proposed approach is presented to thwart the non-probabilistic (NP) hardness problem of data availability due to prolonging backlogs. This approach classifies the different instances of data availability and delivery for the edge-connected end-user services/applications. The classification is preceded by using Naïve Bayes' classification to identify the offloading instances to prevent unnecessary backlogs. The probability of data transmission, delivery, and offloading are independently analyzed for their likelihood, and the appropriate available time instances are allocated in a distributed manner. This validation helps to maximize data delivery by reducing the data drops and service delays.
This study builds the implementation of the traceability process by conducting simulation tests using business process simulations with the implementation of blockchain technology to track drugs. This research focus involved stakeholders, including the pharmaceutical industry, pharmaceutical wholesalers (distributors/wholesalers), health services (drug stores, hospitals), consumers. Simulation methods are used to describe the distribution and traceability of drugs. Finally, the research contribution in incorporating blockchain technology to supply chain management could potentially help in drug traceability. This study provides an overview of blockchain technology capabilities to find out which stakeholders and assets are transacted on the blockchain system. A decentralized Autonomous Organization is an approach to organizing data on the blockchain that defines all stakeholders identities associated with different addresses. This process can organize each address's transactions on a special blockchain platform in this study using multichain. Furthermore, transactions that have occurred cannot be updated or deleted. This simulation also illustrates some of the blockchain characteristics that must exist, among others, transparent, distributed, immutable, and peer to peer transactions. This contribution gives supply chain management, in particular on drug distribution, stronger control over distribution.
Deepak Mathivathanan, K. Mathiyazhagan, Nripendra P. Rana, Sangeeta Khorana · 5 authors
Blockchain is an emerging technology with a wide array of potential applications. This technology, which underpins cryptocurrency, provides an immutable, decentralised, and transparent distributed database of digital assets for use by firms in supply chains. However, not all firms are appropriately suited to adopt blockchain in the existing supply chain primarily due to their lack of knowledge on the benefits of this technology. Using Total Interpretive Structural Modelling (TISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC), this paper identifies the adoption barriers, examines the interrelationships between them to the adoption of blockchain technology, which has the potential to revolutionise supply chains. The TISM technique supports developing a contextual relationship-based structural model to identify the influential barriers. MICMAC classifies the barriers in blockchain adoption based on their strength and dependence. The results of this research indicate that the lack of business awareness and familiarity with blockchain technology on what it can deliver for future supply chains, are the most influential barriers that impede blockchain adoption. These barriers hinder and impact businesses decision to establish a blockchain-enabled supply chain and that other barriers act as secondary and linked variables in the adoption process.
View Video Presentation: https://doi.org/10.2514/6.2021-0662.vid The term Blockchain Technology (BT) refers to information technology (IT) where data is stored in numerous blocks using multiple distributed servers. Through digital automation the data is refreshed periodically at a frequency set by data architects and network designers. The servers are sometimes on the public internet, but in the aerospace world it is more within private intranet of authorized users to maintain privacy and security. A Blockchain network registers the components within aerospace echo system in a digital ledger accompanied by relevant data. It is virtually immutable. The data residing within BT cannot be changed without the awareness of the collaborative parties. Even if a hacker manages to penetrate one block, the overall integrity of the data would remain intact since the data is distributed among thousands of blocks containing cryptographically protected digital ledgers. Deep reinforced learning (DRL) is a machine learning (ML) process based on reinforced learning (RL) and neural networks (NN). DRL training algorithms adjusts its actions dynamically motivated by a system of reward and punishment. The DRL algorithm learns by interacting with its dynamic environment. The DRL software agents receive rewards for correct actions and penalties for performing incorrectly. These agents learn without human intervention by targeting maximizing the reward and minimizing the penalty. Robotic process automation (RPA) is a business process automation technology based on software agents. It can trigger automated agent-based actions. When RPA and DRL are combined, system operates software agents that can learn in dynamic environment automatically without human interventions. They act as autonomous agents that learns continuously and operates cyber infrastructure autonomously. In this paper we show the implementation process and the benefits of integrating deep reinforced learning and robotic process automation in Blockchain digital transformation for autonomous cybersecurity.
Kwok-Bun Yue, Mark Guerra, Howard Wagner, Joses Sandeep Thamarai Selvan · 11 authors
View Video Presentation: https://doi.org/10.2514/6.2021-0093.vid A single Authoritative Source of Truth (AST) of digitalized models is core to Model-Based Systems Engineering (MBSE), which enhances better understanding of the design changes, communicates design intents, analyzes design impacts, and enables automation and optimization. Important technical challenges of supporting MBSE projects include possible consortia of large numbers of organization participants, complex governance, rigorous compliance requirements, nuanced privacy rules, performance requirements, and integration with existing Systems Engineering Environments that are hybrid, complex, legacy, and potentially siloed. The ability of Blockchain Technology (BCT) for managing immutable, distributed ledgers of versatile transactions using smart contracts incited tremendous interests and activities in recent years. Although BCT can provide the foundation of an effective AST solution, very little work has been done on applying BCT on MBSE. Based on a pilot blockchain prototyping experiment on an aerospace MBSE project using Hyperledger’s Fabric, this paper examines the opportunities and challenges of applying BCT on MBSE. It summarizes the design and implementation of a prototype for exploring the opportunities and partially addressing some challenges, and provides pointers for future directions for industry and researchers.
The modern industry, production, and manufacturing core is developing based on smart manufacturing (SM) systems and digitalization. Smart manufacturing’s practical and meaningful design follows data, information, and operational technology through the blockchain, edge computing, and machine learning to develop and facilitate the smart manufacturing system. This process’s proposed smart manufacturing system considers the integration of blockchain, edge computing, and machine learning approaches. Edge computing makes the computational workload balanced and similarly provides a timely response for the devices. Blockchain technology utilizes the data transmission and the manufacturing system’s transactions, and the machine learning approach provides advanced data analysis for a huge manufacturing dataset. Regarding smart manufacturing systems’ computational environments, the model solves the problems using a swarm intelligence-based approach. The experimental results present the edge computing mechanism and similarly improve the processing time of a large number of tasks in the manufacturing system.
Over the years, the industrial and manufacturing applications have become highly connected and automated. The incorporation of interconnected smart sensors, actuators, instruments, and other devices helps in establishing higher reliability and efficiency in the industrial and manufacturing process. This has given rise to the industrial internet of things (IIoT). Since IIoT components are scattered all over the network, real-time authenticity of the IIoT activities becomes essential. Blockchain technology is being considered by the researchers as the decentralized architecture to securely process the IIoT transactions. However, there are challenges involved in effective implementation of blockchain in IIoT. This chapter presents the importance of blockchain in IIoT paradigm, its role in different IIoT applications, challenges involved, possible solutions to overcome the challenges and open research issues.
Even though the automotive industry was among the key players of the industrial revolution in the last century, striking transformations experienced in other sectors did not have significant repercussions on this industry until a few years ago. However, general advancements in technology and Industry 4.0 have presented new opportunities for the reconfiguration of the business environment. Developments in cryptocurrencies such as bitcoin, in particular, have attracted the attention to what is known as blockchain technology. Several successful examples of blockchain applications in different industries have tempted the automotive industry to be rapidly involved with efforts in this direction. As a consequence, the application of the blockchain technology to highly diverse areas in the automotive industry was set in motion. The purpose of this chapter is to explore the application of blockchain technology in the automotive industry, to analyse its advantages and disadvantages, and to demonstrate its successful in general.
Progressively, Software development organizations are investing their resources, time and, money on Software Process Improvement (SPI) since it is beneficial in the enhancement of product quality, reduction in development time, and cost of software projects. However, the existing methodologies and approaches are time-consuming and costly and their major focus is on the SPI of Large Scale Enterprises (LSEs) therefore, we are introducing blockchain in SPI to overcome its major issues such as reliance on a central body of standardization for certification, knowledge management, high cost, resource management and change in organizational culture, etc. We have performed an exploratory case study to identify the different barriers of traditional SPI approaches. To overcome the identified issues, we have proposed and implemented a new approach by performing two case studies. The first case study was performed to identify the barriers in traditional SPI approaches and the second case study was performed to validate our proposed approach. We have performed our experiments on 55 representatives of 50 organizations. According to the results of proposed approach 56.4% of the population agreed that the SPI cost will decrease, 61.8% agreed that time of SPI will decrease and 60.3% of the population agreed that BBSPI will decrease resource utilization. Moreover, 69.1% of the population agreed with the fact that proposed BBSPI will make effective knowledge management and 83.3% of the population said that an organization can mature its processes equaliant to the central certification (CMMI, ISO) body by employing proposed BBSPI. Our results affirm that the BBSPI can reduce the time, cost, resources and helps to manage knowledge used to perform SPI. Moreover, results also depict that the BBSPI can be an efficient substitute of central bodies that could help small and medium-sized organizations to conform to common process improvement models by spending less money, time, and resources with effective knowledge management.
Erkan YALÇINKAYA, Antonio Maffei, Hakan Akillioglu, Mauro Onori
Technological advancements in the information technology domain such as cloud computing, industrial internet of things (IIoT), machine to machine (M2M) communication, artificial intelligence (AI), etc. have started to profoundly impact and challenge not only the ISA95 compliant traditional (ISA95-CTS) but also the smart manufacturing systems (SMMS). Our literature survey pinpoints that systems scalability, interoperability, information security, and data quality domains are among those where many challenges occur. Blockchain technology (BCT) is a new breed of technology characterized by decentralized verifiability, transparency, data privacy, integrity, high availability, and data protection properties. Although many researchers leveraged BCT to empower various aspects of industrial manufacturing systems, there is no study dedicated to addressing the challenges impacting the manufacturing systems compliant with the ISA95 standard. Thereby, our study aims to fill the identified research gap systematically. This paper thoroughly analyzes the challenges hampering the ISA95-CTS and SMMS and methodically addresses them with corresponding BCT capabilities. Furthermore, this paper also discusses various aspects, including the weaknesses, of BCT convergence to ISA95-CTS and SMMS.
Alexios Papacharalampopoulos, Harry Bikas, Christos K. Michail, Panagiotis Stavropoulos
Manufacturing process related functionalities, like optimization and control, are in general demanding in terms of data, computational time and efficiency. However, there are no generic certification or validation schemes that can be followed. In particular, only ISO application can verify the suitability of operations up to an extent. The current work utilizes an enhanced version of Blockchain so that functionalities at the process level can be certified as per a particular scheme. The concept of ledger is elaborated to this end, to manipulate knowledge and be able to handle it like an asset that is exchanged. Thus, a specific generic framework is proposed, herein, to reassure that the right kind of information has been exchanged during process control and optimization. Furthermore, expert distributed agents are utilized to turn knowledge into certified procedures. Encryption issues are also regarded, providing safety and security as extra characteristics. The case study of thermal process control is regarded in this sense to prove the complementary character of these concepts and the usability of the framework. Finally, the existence of additional features within this loop is discussed, like the validation of quantifying concepts like resource streams.
Open access
Flexible and Reconfigurable Manufacturing Systems
Digital Transformation in Industry
Physical Unclonable Functions (PUFs) and Hardware Security
Global supply chains shift to meet exceeding expectations in supply chains lead to the need for digital technologies in supply chain management. Industry 4.0 has emerged in sustainable supply chain systems, blockchain is prominent, with the potential of immutable transparent data. Blockchains present potential to disrupt supply chains through digital transformation by enabling provenance, visibility, relationships, collaboration, lower costs, and enabling real-time trusted data. Scholars are increasingly investigating blockchain adoption, with an emphasis on technology acceptance modelling. Current research is valuable to understand potentials of blockchain technology in supply chains however, there is limited work on the building blocks for blockchain adoption in digital transformation of supply chains. This study aims to investigate through a systematic literature review and case studies, the building blocks for blockchain adoption in digital transformation of sustainable supply chains. Blockchain adoption in supply chain management is receiving increasing attention, along with highlights of critical factors. This paper offers a building block model that is in three main phases; pre-adoption, adoption, and the post adoption. The model indicates that adoption context, blockchain technology platform offerings, strategic responses, and adoption readiness are some key building blocks, particularly at the pre-adoption phase. Trust and supply chain network, firm resources, and blockchain costs are all considered critical in the considerations of the building blocks. In addition, aligning supply chain objectives with blockchain systems is critical and so is blockchain compatibility. Law and governance are amongst the two prominent challenges in blockchain adoption and should be considered as part of the building blocks for blockchain adoption.
The construction industry lacks a comprehensive and overall scheme for the use and management of building information modeling (BIM) in the whole building life cycle, especially the use of BIM technology for asset management and facility management. However, there is little understanding and implementation of enterprise BIM. BIM influences and changes the new project management mode of the whole life cycle of construction projects from design to construction. The safety, audit, responsibility and value of BIM are the forward-looking problems in current BIM, but it is difficult to realize the whole process value management based on the contribution of all parties, it is not conducive to the popularization and application of BIM. From the perspective of new technology and the whole life cycle, this paper analyzes the development and promotion of Blockchain technology to construction projects, and puts forward the fusion point of BIM and Blockchain technology: multi-user, multi-stage, multi-target data fusion and traceability mechanism; It is a consensus process model of multi-party collaboration, interest balance, non-destructive transmission of building information, responsibility traceability and customer satisfaction. It realizes the supporting framework and implementation ideas of BIM and Blockchain technology in construction projects from the aspects of design technology, construction management, material scheduling and whole life cycle construction. This paper will provide some new ideas for the whole life cycle collaborative management of digital construction.
Pratyush Kumar Patro, Raja Wasim Ahmad, Ibrar Yaqoob, Khaled Salah · 5 authors
Product recall management in the automotive industry is a challenging problem that affects human lives and the safe operation of automobiles. Product recalls can assist in removing potentially unsafe products from the marketplace and minimizing a company’s responsibility for corporate negligence. Today’s systems and technologies leveraged for product recall management in the automotive supply chain fall short in providing transparency, traceability, reliability, audit, security, and trust features. In this paper, we propose a blockchain-based approach to overcome the aforementioned problems related to product recall management. We employ the public Ethereum blockchain and integrate it with the decentralized storage of the InterPlanetary File System (IPFS) to deal with the large-sized data problem. We present the system design and six algorithms explaining the working principles, information exchange flow, and stakeholders’ detail and their sequential interactions. We discuss the implementation details, generalization aspects, and cost and security analyses to evaluate the performance of the proposed approach. The proposed solution is cost-effective, secure, and enables automakers to have end-to-end visibility of information during product recalls. We make the smart contracts’ code publicly available on GitHub.