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Smart manufacturing, as a buzzword in recent years, is increasingly attracting attention in academia and industry. However, it is challenging for most manufacturing enterprises as they are uncertain about the ROI of smart manufacturing, and they are currently confronting many difficulties and hurdles. In previous studies, most researchers tended to emphasize larger companies while small and medium enterprises (SMEs) received limited attention. To comprehensively understand the barriers and challenges of smart manufacturing adoption faced by manufacturing companies especially SMEs, the author of this paper conducted a systematic review to identify more than 80 barriers and challenges which are classified into five categories: nature of SMEs, technology, organization, environment, and financial. The top factors include high investment, lack of skilled workforce, lack of strategy and awareness, and security issues. Furthermore, the author pointed out that we should not neglect some less frequently mentioned factors, such as limited bandwidth and speed of internet transfer, and the energy needed for data transmission.

Digitization makes knowledge and information a centerpiece since these connect all dimensions affected by innovation, i.e., people, processes, organization, business, and technology. Maturity Models (MMs) support managers in this evolution. The paper provides a theoretical formalization of MMs to give a practical knowledge contribution to increasing the intensiveness of information in enterprises, through digitization evolution. The paper reviews the main MMs, and presents their state of the art. Then, it defines a backbone structure common to MMs to abstract and describe their features by a meta-model. The new meta-model-driven approach guides companies in the selection of MMs, and in designing a new MM, where needed. The meta-model formalizes the two-levels inputs, the process, and the output to align the company’s motivations with the MM features, resulting in the definition of an appropriate MM for organizations. A qualitative exploratory case study shows the approach and its results, providing guidelines for future actions.

Background: The world is transitioning to Industry 4.0, representing the transition to digital, fully machine-driven environments and cyberphysical systems. Industry 4.0 comprises various technologies and innovations that enable development in multiple perspectives, which are implemented in many different sectors. Problem: The major challenges are the high cost, high rate of failure, security and privacy issues, and there is a need for highly skilled labor for applying healthcare data analysis. Aim: To resolve these issues, we employ the proposed system of Industry 4.0 smart manufacturing for IoT-enabled healthcare data analysis in virtual hospital systems with machine learning (ML) techniques. Methods: The proposed system contains five alternative solutions under smart manufacturing. First, the healthcare data analysis is applied for Weber’s syndrome. That is, this will be used to analyze Weber’s syndrome during its consistent treatment. Second, the IoT-enabled healthcare data handling system works based on edge-assisted edge computing that is used to apply IoT to the healthcare data handling system. The healthcare data analysis in virtual hospital systems uses machine learning for driving data synthesis. Finally, the Industry 4.0 smart manufacturing is applied to the IoT-enabled healthcare data analysis to realize efficient data digitization, especially in smart hospitals with smart sensors for virtual IoT-enabled devices surveillance of Weber’s syndrome. Result: The data digitization based on Industry 4.0 smart manufacturing analysis is considered for data processing, storage and transmission. The proposed system is 62% more efficient than the other analyzed methods. The identification of Weber’s syndrome is 69.8% more efficient than the existing midbrain stroke syndrome identification. The processing and storage of data results are 45.78% more efficient than the current encryption method. Finally, the priority-aware healthcare data analysis based on ML provides 63.4% efficient, faster and more accurate diagnoses in the personalized treatment.

Corporate Industries apply new technologies for manufacturing and production applications. This makes the process depend upon multiple computers, robotic applications, and varying specifications, which reduces efficiency and speed. These technological challenges are incredibly diverse since an extensive range of processing technologies is available. When it comes to human-centered automation, it’s all about the scientific knowledge and data encapsulation for robots to interact within this field. Robots must operate in various ecosystems and interact closely with non-professional customers. Current technology is not well adapted to this scenario, requiring sustainable management separated from available technology. In this article, the human-centered Industrial Robot using Artificial Intelligence (AI-HCIR) is suggested as a tool to resolve such issues. Several manufacturing protocols are presented to efficiently produce products that simplify human work and involve different needs. The HCIR scrutinizes it for time allocation and manufacturing time allocation. For this, a suitable processing environment and a detailed simulation is conducted. The proposed AI-HCIR achieves a 33.68% error rate, 24% throughput, and 18.7% efficiency in the smart city environment. In contrast with conventional methods, the proposed method obtains a better result efficiently.

Industry 4.0 initiatives include the concept of smart manufacturing with the help of sensors and intelligent systems. Smart manufacturing optimizes the internal process and decision making with no/minor intervention from humans. Nevertheless, the adoption of IIoT in industry setting remains a challenge and very minimal work has been in progress. This work is aimed to showcase adoption of IIoT in the Slip Control System/Anti-Lock Braking System. It optimizes the monitoring and decision making of various processes with the help of intelligent systems and IoT in the perspective of Industrial IoT 4.0. It mainly focuses on the various conditions and parameters such as line breakdown notifications, pressure levels, etc. They are monitored through the cloud platform and an alert system is developed in the cloud to warn the user regarding the pressure level. The instant alert of line breakdown notifications reduces the downtime of the plant, improves production and monitoring of the pressure system in the plant and safety check of the plant is in place at any instant.

In the semiconductor and display industries, the automated material handling system (AMHS) represents an important component of fabrication facilities, which typically cost more than multi-billions of US dollars to build. One unit of fabrication facility consists of hundreds of processing steps with hundreds of expensive toolsets, and a production unit is completed after traveling over 9km for more than a month in the facility. Since all material handlings within the fabrication facility are performed by AMHS with minimal human intervention, the proper capacity of AMHS plays a very important role in fabrication operation. If the capacity is too large, it wastes the capacity of the production process as it potentially occupies too much space and investment. On the other hand, if the capacity of AMHS is too small, products under processing cannot be delivered to the expensive process toolsets on time, which causes a drop in productivity. This paper proposes an analytical method for assessing capacity planning strategies for the AMHS under various ramp-up scenarios. It proposes an analytical model consisting of three cost elements including fixed, operating, and delay costs measured by Erlang’s loss system to evaluate various investment alternatives based on the cost-of-ownership approach. We carefully prepared input data based on expert opinions to conduct an experiment that considers investment estimates for the three hypothetical alternatives. The experimental results illustrate that one-step strategy or lead strategy should be used depending on the fabrication facility’s ramp-up speed, which can be analyzed by the model proposed by this paper.

Industry 4.0 allows the integration of intelligent technologies into manufacturing processes to promote operational benefits, but it is influenced by some barriers. The purpose of this study is to identify the benefits, barriers, and organizational factors that can influence the adoption of Industry 4.0 in manufacturing through a systematic literature review. We found and analyzed 10 benefits, 9 barriers, and 8 organizational factors and we also propose a conceptual framework. The factors analyzed can help create more consistent theoretical models for the adoption of Industry 4.0 in manufacturers and highlight the opportunities and challenges of the implementation.

Cyber Physical System (CPS) has provided an outstanding foundation to build advanced industrial systems and applications by integrating innovative functionalities through Internet of Things (IoT) and Web of Things (WoB) to enable connection of the operations of the physical reality with computing and communication infrastructures. A wide range of industrial CPS-based applications have been developed and deployed in Industry 4.0. In order to understand the development of CPS in Industry 4.0, this paper reviews the current research of CPS, key enabling technologies, major CPS applications in industries, and identifies research trends and challenges. A main contribution of this review paper is that it summarizes the current state-of-the-art CPS in Industry 4.0 from Web of Science (WoS) database (including 595 articles) and proposes a potential framework of CPS systematically.

Business model innovation is crucial for transforming Chinese smart manufacturing listed businesses in light of the local and global double-cycle economic model background. Numerous studies have shown that various factors affecting business model innovation are driven by each other to form multiple complex systems. In this paper, we analyze the driving mechanisms of business model innovation grouping in innovative manufacturing listed companies in a global context by exploring the internal and external factors from the perspective of internal and external drivers. At the same time, the fuzzy set qualitative comparative analysis (fsQCA) method is combined to find and found different abilities to the business model innovation mechanism of innovative manufacturing listed companies.

Red Collar Group (RCG) is an apparel manufacturer specialized in personalized suits. It took over 10 years for the company to achieve smart manufacturing, so the company could produce personalized suits in the same production line with the efficiency of mass manufacturing. This chapter introduces the key actions carried out by RCG to achieve its transformation, namely, accumulate data systematically, establish standards for data collection, and redefine business processes.