Companies face increasing pressure to maximize return on capital investments and reduce time required to bring new products from design to large-scale production. This study presents an analysis of the evolution of industrial automation systems, with particular emphasis on Programmable Logic Controllers (PLCs) and Distributed Control Systems (DCS), examining their technical and economic impacts on industrial organizations. The research employs systematic literature review to document the historical trajectory from the first computer-based industrial control system installed in 1959 to contemporary virtualized systems of Industry 4.0 and 5.0. Results demonstrate that DCS implementation generated substantial revenue, with companies like Honeywell obtaining US$ 100 million in the first year alone after launching TDC 2000 in 1975. Analysis reveals that automated systems increase productivity in a two-to-one ratio compared to manual processes, while providing significant improvements in operational safety, quality control, and production flexibility. The study concludes that well-implemented automation offers measurable return on investment, but the optimal balance of benefits depends fundamentally on alignment between automation strategy and specific business objectives of each organization. The projection of US$ 209 billion in revenue for the industrial automation market by 2020 evidences the growing economic relevance of these technologies.
The foundation model industry exhibits unprecedented concentration in critical inputs: semiconductors, energy infrastructure, elite talent, capital, and training data. Despite extensive sectoral analyses, no comprehensive framework exists for assessing overall industrial vulnerability. We develop the Artificial Intelligence Industrial Vulnerability Index (AIIVI) grounded in O-Ring production theory, recognizing that foundation model production requires simultaneous availability of non-substitutable inputs. Given extreme data opacity and rapid technological evolution, we implement a validated human-in-the-loop methodology using large language models to systematically extract indicators from dispersed grey literature, with complete human verification of all outputs. Applied to six state-of-the-art foundation model developers, AIIVI equals 0.82, indicating extreme vulnerability driven by compute infrastructure (0.85) and energy systems (0.90). While industrial policy currently emphasizes semiconductor capacity, energy infrastructure represents the emerging binding constraint. This methodology proves applicable to other fast-evolving, opaque industries where traditional data sources are inadequate.
There are different views in the literature regarding economic growth and development. Growth and development hypotheses have been addressed from many different perspectives. It is aimed to examine the view of production with the Heavy Industry Initiative proposed by Prof. Dr. Necmettin Erbakan a crucial part of Material Development in Türkiye. In this context, an examination has been conducted on GDP, Mining, Manufacturing, Energy, and Chemical Industry Productions in Türkiye between 1995-2023, focusing on percentage changes. The stationarity of our data set has been checked, and a VAR estimation has been performed by determining the lag length. Long-term relationships have been identified through the Johansen Cointegration test, and after confirming that there are no issues of autocorrelation and changing variance, variance decomposition has been carried out. Thus, inferences have been made regarding the effects of the identified sectors on GDP. Based on the analysis results, it has been observed that there is a stronger relationship between GDP and the Manufacturing Sector compared to the Mining, Energy, and Chemical Sectors, and it has been concluded that sectoral shocks diminish over time. Additionally, VAR Impulse-Response Analysis has been conducted among the variables. In this context, it has been observed that the variables in the dataset have an impact on each other in both the short and long term. It has been concluded that the shocks were more pronounced in the initial periods, while the economy reached a balance over time. In this regard, it has been confirmed that the heavy industry initiative exhibited different responses sectorally, while also being significant in terms of economic growth and development.
This study analyzes how Europe can decarbonize its industrial sector while remaining competitive. Using the open-source model PyPSA-Eur, it examines key energy- and emission-intensive industries, including steel, cement, methanol, ammonia, and high-value chemicals. Two development paths are explored: a continued decline in industrial activity and a reindustrialization driven by competitiveness policies. The analysis assesses cost gaps between European green products and lower-cost imports, and evaluates strategies such as intra-European relocation, selective imports of green intermediates, and targeted subsidies. Results show that deep industrial decarbonization is technically feasible, led by electrification, but competitiveness depends strongly on policy choices. Imports of green intermediates can lower costs while preserving jobs and production, whereas broad subsidies are economically unsustainable. Effective policy should focus support on sectors like ammonia and steel finishing while maintaining current production levels.
Dimitris Kallis, Moysis Symeonides, Marios D. Dikaiakos
The widespread adoption of IoT has driven the development of cyber-physical systems (CPS) in industrial environments, leveraging Industrial IoTs (IIoTs) to automate manufacturing processes and enhance productivity. The transition to autonomous systems introduces significant operational costs, particularly in terms of energy consumption. Accurate modeling and prediction of IIoT energy requirements are critical, but traditional physics- and engineering-based approaches often fall short in addressing these challenges comprehensively. In this paper, we propose a novel methodology for benchmarking and analyzing IIoT devices and applications to uncover insights into their power demands, energy consumption, and performance. To demonstrate this methodology, we develop a comprehensive framework and apply it to study an industrial CPS comprising an educational robotic arm, a conveyor belt, a smart camera, and a compute node. By creating micro-benchmarks and an end-to-end application within this framework, we create an extensive performance and power consumption dataset, which we use to train and analyze ML models for predicting energy usage from features of the application and the CPS system. The proposed methodology and framework provide valuable insights into the energy dynamics of industrial CPS, offering practical implications for researchers and practitioners aiming to enhance the efficiency and sustainability of IIoT-driven automation.
Adriana Olteanu, Carla Georgia Marian, Radu Nicolae Pietraru
This research underscores the potential of integrating wearable devices with cloud computing to enhance communication, streamline operations, and improve productivity in factory environments. The findings highlight the transformative impact of such technologies on industrial workflows, paving the way for future advancements in smart manufacturing solutions. This study introduces a smartwatch-based monitoring and alert system designed to optimize factory operations by leveraging cloud-based technologies. Through a structured analysis and design process, we defined the system’s functional requirements and architecture, carefully selecting technologies to meet operational objectives. The proposed framework enhances workplace communication by implementing a bidirectional notification system that fosters seamless interactions between employees and supervisors, as well as among colleagues. The development of an intuitive smartwatch application ensures that factory operators remain connected, responsive, and engaged with their tasks. Additionally, the system facilitates real-time monitoring, task management, and work hour tracking, simplifying reporting procedures and improving workforce efficiency. By utilizing cloud infrastructure, the solution offers scalability and robust security, adapting to evolving industrial demands.
Markus Voggenreiter, Florian Angermeir, Fabiola Moyón
et al.
In recent years, DevOps, the unification of development and operation workflows, has become a trend for the industrial software development lifecycle. Security activities turned into an essential field of application for DevOps principles as they are a fundamental part of secure software development in the industry. A common practice arising from this trend is the automation of security tests that analyze a software product from several perspectives. To effectively improve the security of the analyzed product, the identified security findings must be managed and looped back to the project team for stakeholders to take action. This management must cope with several challenges ranging from low data quality to a consistent prioritization of findings while following DevOps aims. To manage security findings with the same efficiency as other activities in DevOps projects, a methodology for the management of industrial security findings minding DevOps principles is essential. In this paper, we propose a methodology for the management of security findings in industrial DevOps projects, summarizing our research in this domain and presenting the resulting artifact. As an instance of the methodology, we developed the Security Flama, a semantic knowledge base for the automated management of security findings. To analyze the impact of our methodology on industrial practice, we performed a case study on two DevOps projects of a multinational industrial enterprise. The results emphasize the importance of using such an automated methodology in industrial DevOps projects, confirm our approach's usefulness and positive impact on the studied projects, and identify the communication strategy as a crucial factor for usability in practice.
Aimira Baitieva, David Hurych, Victor Besnier
et al.
Automating visual inspection in industrial production lines is essential for increasing product quality across various industries. Anomaly detection (AD) methods serve as robust tools for this purpose. However, existing public datasets primarily consist of images without anomalies, limiting the practical application of AD methods in production settings. To address this challenge, we present (1) the Valeo Anomaly Dataset (VAD), a novel real-world industrial dataset comprising 5000 images, including 2000 instances of challenging real defects across more than 20 subclasses. Acknowledging that traditional AD methods struggle with this dataset, we introduce (2) Segmentation-based Anomaly Detector (SegAD). First, SegAD leverages anomaly maps as well as segmentation maps to compute local statistics. Next, SegAD uses these statistics and an optional supervised classifier score as input features for a Boosted Random Forest (BRF) classifier, yielding the final anomaly score. Our SegAD achieves state-of-the-art performance on both VAD (+2.1% AUROC) and the VisA dataset (+0.4% AUROC). The code and the models are publicly available.
Purpose: Industrial robots allow manufacturing companies to increase productivity and remain competitive. For robots to be used, they must be accepted by operators on the one hand and bought by decision-makers on the other. The roles involved in such organizational processes have very different perspectives. It is therefore essential for suppliers and robot customers to understand these motives so that robots can successfully be integrated on manufacturing shopfloors. Methodology: We present findings of a qualitative study with operators and decision-makers from two Swiss manufacturing SMEs. Using laddering interviews and means-end analysis, we compare operators' and deciders' relevant elements and how these elements are linked to each other on different abstraction levels. These findings represent drivers and barriers to the acquisition, integration and acceptance of robots in the industry. Findings: We present the differing foci of operators and deciders, and how they can be used by demanders as well as suppliers of robots to achieve robot acceptance and deployment. First, we present a list of relevant attributes, consequences and values that constitute robot acceptance and/or rejection. Second, we provide quantified relevancies for these elements, and how they differ between operators and deciders. And third, we demonstrate how the elements are linked with each other on different abstraction levels, and how these links differ between the two groups.
Digital image processing (DIP) is of great importance in validating and guaranteeing parameters that ensure the quality of mass-produced products. Therefore, this article focused on developing an industrial automation method for a zone of a production line model using the DIP. The neo-cascade methodology employed allowed for defining each of the stages in an adequate way, ensuring the inclusion of the relevant methods for its development, which finally incurred in the modeling, design, implementation, and testing of an optimal route generation system for a warehouse area, using DIP with optimization guidelines, in conjunction with an embedded platform and the connection to programmable logic controllers (PLCs) for its execution. The system was based on the OpenCV library; tool focused on artificial vision, which was implemented on an object-oriented programming (OOP) platform based on Java language. It generated the optimal route for the automation of processes in a scale warehouse area, using the segmentation of objects and the optimization of flow in networks as pillars, ending with the connection to PLCs as a method of action, which in case of implementation would eliminate constraints such as process inefficiency, the use of manpower to perform these tasks, inadequate use of resources, among others
Industrial production processes, especially in the pharmaceutical industry, are complex systems that require continuous monitoring to ensure efficiency, product quality, and safety. This paper presents a hybrid unsupervised learning strategy (HULS) for monitoring complex industrial processes. Addressing the limitations of traditional Self-Organizing Maps (SOMs), especially in scenarios with unbalanced data sets and highly correlated process variables, HULS combines existing unsupervised learning techniques to address these challenges. To evaluate the performance of the HULS concept, comparative experiments are performed based on a laboratory batch
Shumila Shahid, Abubakar Dar, Azhar Hussain
et al.
The pollution of heavy metals (HMs) is a major environmental concern for agricultural farming communities due to water scarcity, which forces farmers to use wastewater for irrigation purposes in Pakistan. Vegetables grown around the cities are irrigated with domestic and industrial wastewater from areas near mining, paint, and ceramic industries that pollute edible parts of crops with various HMs. Cadmium (Cd) is an extremely toxic metal in arable soil that enters the food chain and damages the native biota, ultimately causing a reduction in plant growth and development. However, the use of microbes and growth regulators enhances plant growth and development as well as HM immobilization into the cell wall and hinders their entry into the food chain. Thus, the integrated use of bacterial consortium along with exogenously applied jasmonic acid (JA) mitigates the adverse effect of metal stress, ultimately reducing the metal mobility into roots by soil. Therefore, the current study was conducted to check the impact of Cd-tolerant bacteria and JA on the growth, nutrient status, and uptake of Cd in the cauliflower (Brassica oleracea). Our results demonstrated that increasing concentrations of Cd negatively affect growth, physiological, and biochemical attributes, while the use of a bacterial consortium (SS7 + SS8) with JA (40 μmol L−1) significantly improved chlorophyll contents, stem fresh and dry biomass (19.7, 12.7, and 17.3%), root length and root fresh and dry weights (28.8, 15.2, and 23.0%), and curd fresh and dry weights and curd diameter (18.7, 12.6, and 15.1%). However, the maximum reduction in soil Cd, roots, and curd uptake was observed by 8, 11, and 9.3%, respectively, under integrated treatment as compared to the control. Moreover, integrating bacterial consortium and JA improves superoxide dismutase (SOD) (16.79%), peroxidase dismutase (POD) (26.96%), peroxidase (POX) (26.13%), and catalase (CAT) (26.86%). The plant nitrogen, phosphorus, and potassium contents were significantly increased in soil, roots, and curd up to 8, 11, and 9.3%, respectively. Hence, a consortium of Klebsiella strains in combination with JA is a potential phytostabilizer and it reduces the uptake of Cd from soil to roots to alleviate the adverse impact on cauliflower’s growth and productivity.
Kan Tulsook, Piyada Bussadee, Jantima Arnthong
et al.
Abstract Trehalose production via a one-step enzymatic route using trehalose synthase (TreS) holds significant promise for industrial-scale applications due to its simplicity and utilization of low-cost substrates. However, the development of a robust whole-cell biocatalyst expressing TreS remains crucial for enabling practical and economically viable production. In this study, a high-sugar tolerant strain of S. cerevisiae was screened and employed as a host cell for the cell surface display of TreS from Acidiplasma aeolicum. The resultant strain, S. cerevisiae I3A, exhibited remarkable surface displayed TreS activity of 3358 U/g CDW and achieved approximately 64% trehalose yield (10.8 g/L/h productivity) from maltose. Interestingly, no glucose by-product was observed during trehalose production. The S. cerevisiae I3A cells exhibited reusability for up to 12 cycles leading to potential cost reduction of trehalose products. Therefore, our study demonstrated the development of a high-sugar tolerant S. cerevisiae strain expressing TreS on its surface as a whole-cell biocatalyst for efficient and economical trehalose production with potential applications in the food and pharmaceutical industries.
Haoping Bai, Shancong Mou, Tatiana Likhomanenko
et al.
Despite progress in vision-based inspection algorithms, real-world industrial challenges -- specifically in data availability, quality, and complex production requirements -- often remain under-addressed. We introduce the VISION Datasets, a diverse collection of 14 industrial inspection datasets, uniquely poised to meet these challenges. Unlike previous datasets, VISION brings versatility to defect detection, offering annotation masks across all splits and catering to various detection methodologies. Our datasets also feature instance-segmentation annotation, enabling precise defect identification. With a total of 18k images encompassing 44 defect types, VISION strives to mirror a wide range of real-world production scenarios. By supporting two ongoing challenge competitions on the VISION Datasets, we hope to foster further advancements in vision-based industrial inspection.
Bram van den Akker, Olivier Jeunen, Ying Li
et al.
The bandit paradigm provides a unified modeling framework for problems that require decision-making under uncertainty. Because many business metrics can be viewed as rewards (a.k.a. utilities) that result from actions, bandit algorithms have seen a large and growing interest from industrial applications, such as search, recommendation and advertising. Indeed, with the bandit lens comes the promise of direct optimisation for the metrics we care about. Nevertheless, the road to successfully applying bandits in production is not an easy one. Even when the action space and rewards are well-defined, practitioners still need to make decisions regarding multi-arm or contextual approaches, on- or off-policy setups, delayed or immediate feedback, myopic or long-term optimisation, etc. To make matters worse, industrial platforms typically give rise to large action spaces in which existing approaches tend to break down. The research literature on these topics is broad and vast, but this can overwhelm practitioners, whose primary aim is to solve practical problems, and therefore need to decide on a specific instantiation or approach for each project. This tutorial will take a step towards filling that gap between the theory and practice of bandits. Our goal is to present a unified overview of the field and its existing terminology, concepts and algorithms -- with a focus on problems relevant to industry. We hope our industrial perspective will help future practitioners who wish to leverage the bandit paradigm for their application.
John W. Day, Charles A. Hall, Kent Klitgaard
et al.
We review impacts of climate change, energy scarcity, and economic frameworks on sustainability of natural and human systems in coastal zones, areas of high biodiversity, productivity, population density, and economic activity. More than 50% of the global population lives within 200 km of a coast, mostly in tropical developing countries. These systems developed during stable Holocene conditions. Changes in global forcings are threatening sustainability of coastal ecosystems and populations. During the Holocene, the earth warmed and became wetter and more productive. Climate changes are impacting coastal systems via sea level rise, stronger tropical cyclones, changes in basin inputs, and extreme weather events. These impacts are passing tipping points as the fossil fuel-powered industrial-technological-agricultural revolution has overwhelmed the source–sink functions of the biosphere and degraded natural systems. The current status of industrialized society is primarily the result of fossil fuel (FF) use. FFs provided more than 80% of global primary energy and are projected to decline to 50% by mid-century. This has profound implications for societal energy requirements, including the transition to a renewable economy. The development of the industrial economy allowed coastal social systems to become spatially separated from their dominant energy and food sources. This will become more difficult to maintain with the fading of cheap energy. It seems inevitable that past growth in energy use, resource consumption, and economic growth cannot be sustained, and coastal areas are in the forefront of these challenges. Rapid planning and cooperation are necessary to minimize impacts of the changes associated with the coming transition. There is an urgent need for a new economic framework to guide society through the transition as mainstream neoclassical economics is not based on natural sciences and does not adequately consider either the importance of energy or the work of nature.
Harbors and coast protective works. Coastal engineering. Lighthouses, Oceanography
Amaç: Bu çalışmanın amacı 1980-2017 yılları arası Katar’da ekolojik ayak izi ve alt bileşenlerinin durağanlığını incelemektir.
Yöntem: Katar’da ekolojik ayak izi ve alt bileşenlerinin durağanlığı kesirli frekanslı Fourier ADF ve ADF birim kök testleri ile analiz edilmiştir.
Bulgular: Kesirli frekanslı Fourier ADF test bulgularına göre inşaat alanları ayak izi, karbon salımı ayak izi ve toplam ekolojik ayak izi değişkenlerinin durağan olduğu tespit edilmiştir. ADF test bulgularına göre tarım alanı ve otlak alan ayak izi değişkenlerinin durağan olduğu, balıkçılık alanları ve orman ürünleri ayak izi değişkenlerinin ise birim köklü olduğu belirlenmiştir.
Özgünlük: Ekolojik ayak izi ve alt bileşenlerinin durağanlığının araştırıldığı çalışmalarda elde edilen sonuçlar bu alanda bir uzlaşı olmadığını göstermektedir. Ayrıca literatürde Katar için doğrudan ekolojik ayak izinin durağanlığının incelendiği herhangi bir çalışmaya rastlanmamıştır. Katar için ekolojik ayak izi ve alt bileşenlerinin durağanlıklarının güncel analiz yöntemleri ile test edildiği ilk araştırma olarak bu çalışmanın literatüre katkı sunması beklenmektedir.
Allahyar Beigi Firoozi, Mohammad Bashokouh Ajirlou, Naser Seffollahi
et al.
The current study aimed to cluster the application of digital technologies from Industry 4.0 in the agricultural food distribution network. To achieve this goal, a bibliometric technique was employed to identify prominent trends and themes in this field through the analysis of articles, authors, countries, and co-citations of authors and bibliographic pairs. Through an extensive search in the Scopus scientific database, bibliographic information for 331 valid and relevant scientific articles was acquired. This information was inputted into the bibliometric package in R software, and the most influential journal, author, university, country, and most cited authors were determined. To visualize the information, Vosviewer software was utilized for co-citation analysis of authors, cited references, and bibliographic pairs. The findings from the network analysis revealed that the studies on the application of digital technologies in the agricultural food distribution network can be categorized into five main clusters.IntroductionIndustry 4.0, viewed as a new industrial stage, has introduced complex information and communication technologies that facilitate comprehensive connections across different parts of the supply chain. The digital technologies associated with Industry 4.0 allow production lines, business processes, and teams within a supply chain to collaborate seamlessly, irrespective of location, time zone, network constraints, or any other factors. Researchers highlight that the advent of digital technologies from the fourth industrial revolution, including radio frequency identification, big data, cloud computing, smart sensors, machine learning, robotics, augmented production, artificial intelligence, augmented reality, the Internet of Things, blockchain, and similar technologies, holds immense potential for significantly enhancing production productivity. These technologies could lead to substantial innovation, competitive growth, and may contribute to improving the sustainability of the current industrial system. To meet the escalating demand for food, agricultural marketing professionals and managers globally must maximize the efficiency of the agricultural distribution network, given the widespread adoption of digital technologies. The increasing significance of this goal has prompted marketing researchers to explore the use of digital technologies in the agricultural food distribution network, leading to a substantial number of studies in this research field since 2011. In this context, the present study aimed to cluster the utilization of digital technologies in Industry 4.0 within the agricultural food distribution network. A bibliometric study was conducted to identify existing gaps in research and propose future directions. The research focuses on the application of digital technologies in the distribution network.Aligned with the research objective, fundamental questions are posed: Which publications, authors, and countries are most influential in the application of Industry 4.0 digital technologies in the agricultural food distribution network? Additionally, what scientific clusters exist in this domain?MethodologyThe objective of the current research is to conduct a bibliographic analysis of studies related to the application of digital technologies in Industry 4.0 within the agricultural food distribution network. Utilizing bibliometric techniques, a crucial measure for evaluating scientific output, a comprehensive examination of scientific literature was carried out concerning the application of digital technologies in Industry 4.0 within the agricultural food distribution network.The search was conducted within the Scopus scientific database, which encompasses a significant array of diverse journals and authoritative articles globally. The search covered three sections: title, abstract, and keywords, yielding a list of studies that exclusively included English-language articles from journals (excluding conference studies and book chapters) published between 2011 (the inception year of Industry 4.0) and 2023. By imposing these criteria, 352 original pieces of data containing bibliographic information were obtained. Subsequently, the title and abstract of each article were meticulously scrutinized to identify information relevant to the agricultural food distribution networks. Among these, 6 articles pertaining to the halal supply chain and 15 articles conducted as systematic reviews were excluded from the bibliographic information collection. The final portfolio for analysis consisted of bibliographic information from 331 articles, which was then entered into the bibliometric software package. This analysis was carried out using R software and VOSviewer software. The bibliometric software package facilitated quantitative bibliographic analysis, while the VOSviewer software was employed for visualizing and analyzing citation networks.ResultsThe quantitative findings indicate a significant increase in studies related to the adoption of digital technologies in the agricultural food distribution network, particularly after 2017. The most widely utilized digital technologies in the food distribution network include blockchain, the Internet of Things, simulation, artificial intelligence, big data, machine learning, 3D printers, sensors, and digital twins.Through the analysis of bibliographic pairs, five primary clusters were identified concerning the application of digital technologies in the agricultural food distribution network. These clusters are associated with the use of digital technologies in ensuring food quality, enhancing distribution network flexibility, establishing modular architecture within the distribution network, implementing intelligent logistics systems, and promoting sustainable distribution networks.ConclusionBased on the themes of the clusters identified in Table 7, it can be concluded that the Internet of Things and blockchain play crucial roles in real-time tracking, tracing, and monitoring of food throughout the supply chain, thereby reducing wastage. RFID technologies and digital twins are highly effective in ensuring food safety and facilitating delivery to consumers, especially in the face of environmental changes and crises such as epidemics. Another application of digital technologies lies in the modular architecture of the food distribution network. Through the use of modular architecture, various technologies can modularize tasks and extensive operations within the food distribution network. Ultimately, all these components can be centralized under blockchain technology, with diverse data stored in a vast cloud space. Consistent implementation of digital technologies in the food distribution network has the potential to establish regional warehouses, resulting in reduced distribution and delivery costs, enhanced food safety and sustainability, and the possibility of customizing food for end consumers. This, in turn, will contribute to the stability of the food network.