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Manufacturing is facing challenges in integrating information technology (IT) with operational technology (OT) and implementing Industrial Internet of Things (IIoT) concepts in the industry to increase manufacturing flexibility. This paper addresses the research gap in designing and using next-generation manufacturing execution systems (MES)/manufa...
Contexts in source publication
Context 1
... 2016) identified four design principles for a smart factory: interconnection, information transparency, decentralized decisions, and technical assistance-some of which can be used by academicians to develop reconfigurable manufacturing systems based on the principles of intelligent manufacturing control for manufacturing flexibility. We developed Fig. 3, which represents the hierarchical structure of systems in an enterprise as prescribed by the ISA-95 standard, which is evolving into a distributed platform-based architecture as the industry embraces IIoT. Therefore, in Fig. 3 we include the IIoT platform (e.g., ThingWorx IIoT Solutions Platform) in the ISA-95 structure and represent ...
Context 2
... systems based on the principles of intelligent manufacturing control for manufacturing flexibility. We developed Fig. 3, which represents the hierarchical structure of systems in an enterprise as prescribed by the ISA-95 standard, which is evolving into a distributed platform-based architecture as the industry embraces IIoT. Therefore, in Fig. 3 we include the IIoT platform (e.g., ThingWorx IIoT Solutions Platform) in the ISA-95 structure and represent it as a Level 2 system responsible for monitoring and controlling IIoT ...
Citations
... Como segundo paso es la verificación, de que si la empresa tiene implementado un "Manufacturing Execution System/Manufacturing Operations Management" (MES/ MOM), también conocido como "cabina de fabricación", es un sistema de información empresarial que proporciona información de la fábrica casi en tiempo real para ser una fuente valiosa de detalles a nivel de producto que podrían mejorar la eficiencia de la cadena de suministro (Mantravadi et al., 2023). ...
Para la investigación se recurrió a la base de datos bibliográficos de Scopus. Aplicando la revisión bibliométrica con la metodología del Enfoque Metaanalítico TEMAC, se analizó los artículos desde el 2018, apoyados con el software VOSviewer, se identificó a los autores más citados, las co-citaciones y acoplamientos más importantes, permitiendo validar un modelo integrador y un ROADMAP. La simulación de gemelos digitales en la cadena de suministros de cemento es un tema reciente con escasas investigaciones, las pocas que existen, consideran de forma aislada los diferentes procesos de producción y no de extremo a extremo y de forma integral en la Industria del Cemento. Se planteó el objetivo de proponer un modelo integrador que se concreta en la hoja de ruta ROADMAP para la implantación de un gemelo digital en la cadena de suministros en la industria del Cemento.
For the research, the Scopus bibliographic database was used. Applying
the bibliometric review with the TEMAC Meta-analytic Approach methodology,
the articles since 2018 were analyzed, supported with the VOSviewer software, the
most cited authors, the most important co-citations and couplings were identified,
allowing an integrative model to be validated. and a ROADMAP. The simulation of
digital twins in the cement supply chain is a recent topic with little research, the few
that exist, consider the different production processes in isolation and not end-to
end and comprehensively in the Cement Industry. The objective was set to propose
an integrative model that is specified in the ROADMAP for the implementation of a digital twin in the supply chain in the Cement industry.
... such as artificial intelligence, enterprise digital transformation creatively applies cutting-edge digital information technologies to production and operation systems, management models, and core business processes, resulting in disruptive innovation and change [1]. As the digitalization level of Chinese enterprises increases, many scholars have begun to pay attention to the microeconomic effects of digital transformation. ...
... Moderate digital transformation is conducive to improving enterprise GTFP. First, enterprises can break the "information barriers" between enterprise departments based on the application of an industrial Internet platform, improve the information transparency of enterprise processes, realize the integration of enterprise management and production control, and improve the efficiency of enterprise operation [1,35]; through the whole chain data communication and intelligent analysis, realize the comprehensive connection between enterprises and external user demand, innovation resources, and production capacity; at the same time, industrial internet platforms can also achieve comprehensive connections between energy enterprises and external user demands, innovative resources, and production capacity, helping enterprises adjust production plans promptly based on product demand, raw material supply, and capacity configuration, optimizing the allocation of various resources, thereby improving capacity utilization, promoting cost reduction, quality improvement, and efficiency enhancement for enterprises [22]. Second, enterprises promote the intelligent upgrading of the production process through the introduction of emerging technologies such as the Internet of Things, big data analysis, and artificial intelligence [7], improve the efficiency of equipment operation, and carry out all-around, real-time monitoring of the operating status of the equipment of each link and energy consumption to realize the exemplary management of energy consumption, improve the efficiency of energy utilization, reduce the cost of production, help the enterprise save energy and increase efficiency, and promote the enterprise GTFP [36][37][38]. ...
... First, from the perspective of enterprise investment efficiency, when the degree of digital transformation is within a certain level, digital transformation will improve the GTFP of enterprises by improving their investment efficiency. Enterprises can utilize digital technologies such as the Internet, big data, and cloud computing to collect, store, analyze, and process various types of internal and external information in real-time, efficiently, and comprehensively, and encode and output the data into standardized and structured information [39], building a group level data resource pool from horizontal to edge and vertically to the bottom, assist enterprises in timely understanding of market demand and analyzing market trends [1], exploring potential investment opportunities, implementing scientific and efficient investment plans, improving investment efficiency, and ultimately enhancing the GTFP level of enterprises. At the same time, using the prediction technology of artificial intelligence and machine learning, enterprises can also carry out technological analysis of investment programs, scientifically assess the benefits and risks of investment programs [40], and formulate more scientific and suitable investment strategies, thus reducing overinvestment and underinvestment behaviors of enterprises. ...
In the context of accelerated development of the digital economy, whether enterprises can drive green total factor productivity (GTFP) through digital technology has become the key to promoting high-quality development of the economy and achieving the goal of "dual-carbon", However, the relationship between digital transformation and GTFP is still controversial in existing studies. Based on the data of 150 listed companies in China's A-share energy industry from 2011 to 2021, this study empirically analyzes the impact of digital transformation on GTFP using a fixed-effect model. The study shows an inverted U-shaped nonlinear effect of digital transformation on enterprises' GTFP, and the conclusion still holds after a series of robustness tests. Mechanism analysis shows that enterprise investment efficiency and labour allocation efficiency play a significant mediating role in the above inverted U-shaped relationship, in which the inverted U-shaped relationship between digital transformation and GTFP mainly stems from the influence of enterprise investment efficiency. Heterogeneity analysis finds that the inverted U-shaped relationship between digital transformation and GTFP of enterprises is more significant in large-scale enterprises, new energy enterprises and enterprises in central and western regions. The study's findings provide important insights for enterprises to promote digital transformation and realize the green and high-quality development of the energy industry.
... Yet, challenges arise due to data being dispersed across disconnected clouds held by vendors. The company aims to gain data ownership, enabling analysis and vendor flexibility [19]. ...
... As a cooperative of 5,000+ farmers, it's limited to internal suppliers. Their goal is digital solutions emphasizing global traceability and genealogy [19]. ...
This paper explores the implementation of Industrial Internet of Things (IIoT) platforms to address challenges in adoption due to information technology (IT)/ operational technology (OT) integration difficulties. The study uses a case from food manufacturing and supply. We outline specific requirements for IIoT implementation, leveraging empirical data from Danish food manufacturers and industry reports. By applying the Quality Function Deployment (QFD) method, we assess how 10 Microsoft Azure IoT functionalities (as an illustrative example) align with industry requirements, thereby discussing design considerations. In addition, we demonstrate practical implications in Aalborg University’s Industry 4.0 learning factory to present a high-level architecture for IIoT-connected factory systems through Unified Modeling Language (UML). Our study highlights the significance of IT/OT integration for IIoT success and offers a blueprint for efficient data exchange and process control. The findings emphasize the importance of aligning technology choices with industry-specific requirements and provide future research directions for IIoT in brownfield manufacturing. Our results indicate Scalability and Device Connectivity as core functionalities for successful IIoT adoption, with each accounting for high percentages of importance of 19% and 16%, respectively. The paper contributes to Industry 4.0's data integration and enhances food industry operations. Future work will explore additional complexities to enhance IIoT implementation.
... This may help companies better understand the requirements of their customers as well as academics working to enhance NLP systems. Search and recommendation engines may be enhanced using ChatGPT [35][36][37]. The model may be used to interpret the purpose of a user's inquiry and provide relevant answers. ...
... Figure 6 illustrates the stages of Industry 4.0 development. [64,65] Standards and architectures can be developed based on the manufacturing enterprise's high-level goals [57]. However, it is essential to first determine the requirements for MES to interact with different systems, such as SAP HANA, Salesforce, or Kinaxis, in the IIoT. ...
... This paper aims to answer the following question [65]: "What is the conceptual framework of smart factory capabilities with IIoT-connected MES/ MOM, and how could MES/MOM be designed to support reconfigurability in manufacturing?" (RQ1.3) ...
... Steps to enable the smart factory with IIoT-connected MES/MOM[65] ...
PhD Dissertation by Soujanya Mantravadi.
Manufacturing is facing difficulty in evolving and meeting reconfigurability needs because of the prevalence of legacy systems that are heterogeneous and inflexible. This PhD project addresses that challenge by developing design principles that are relevant to the real-world industrial context for manufacturing operations management (MOM). The thesis addresses the challenges of low interoperability, low customizability, and monolithic design of manufacturing execution systems (MES) in the Industrial Internet of things.
Using a design science research approach, the thesis develops architectural design recommendations with the support of Unified Modeling Language illustrations. This work also establishes the relevance of the ISA 95 standard in an Industry 4.0 context, which has been unclear and undocumented.
The case companies for the PhD project include three large production companies with global manufacturing footprints from the Manufacturing Academy of Denmark network. Industry needs were studied through seven semi-structured interviews and three industrial demonstrators. MES/MOM design principles were deduced using the example of AAU Smart Production Lab and the quality function deployment method.
The PhD was done through external collaboration with the University of Cambridge and this research incorporated many outside concepts from computer science to present its arguments.
Purpose
This study aims to provide a bibliometric analysis and systematic literature review of Industry 4.0 (I4.0) research in the supply chain (SC) area and to understand related contemporary research trends. I4.0 has the potential to change the way goods are manufactured, distributed and made available to customers through the digitalisation of SC. Although I4.0 originated in 2011 in Germany, its application in managing the SC has only recently started gaining momentum. Therefore, it is essential to understand the research progress and identify the current trends of I4.0 application in the SC field.
Design/methodology/approach
A bibliometric analysis was conducted to empirically analyse the literature related to I4.0 implementation in the SC. This study retrieved papers from the Scopus database, reviewing 1,155 articles from the period 2016 to 2023 (November) for bibliometric analysis. Bibliometrix, using R software, was used for the bibliometric analysis, and VOSviewer was used for network analysis.
Findings
The findings provide an overview of the most relevant journals, most productive scholars, top academic institutions and top countries contributing to I4.0 research in the SC context. The results show that the most recent research contributions are related to the topics of SC performance, sustainability, digitalisation and digital transformation. Furthermore, a detailed review of articles published in the three and above-rated journals in the Chartered Association of Business Schools list is presented.
Originality/value
The novelty of this study lies in identifying the current research trends and themes of I4.0 research in the SC area. This research benefits researchers by identifying potential research areas for I4.0 implementation in the SC and providing directions for future research.
With the development and expansion of the Internet of Things, computing at the edge is becoming increasingly important, especially for applications where real-time response is important. In this paper, we made a systematic review of the literature on analytics at the edge. We extracted data from 40 selected primary relevant studies from the complete set of 419 papers retrieved from scientific databases. In our analysis of the full text of every primary study we investigated: temporal distribution of primary studies, publication types, domain and application areas of the primary papers, used machine learning and deep learning methods. We also elaborated on the main themes of the primary studies and recommended some possible interesting future research possibilities.
