Abstract The fourth industrial revolution (Industry 4.0) is changing not only the manufacturing industry but also the construction industry and its connected supply chains. Construction supply chains (CSCs) have specific characteristics, such as being temporary organizations that require high coordination efforts to align the processes of supply chain actors. The concept of proximity is used to analyze synchronization between suppliers and the construction site. This article presents a framework for explaining Industry 4.0 concepts that increase or reduce proximity. We find that Industry 4.0 technologies mainly influence technological, organizational, geographical and cognitive proximity dimensions. This presents benefits and challenges for CSCs. This framework is based on the results of a systematic literature review of scientific papers and analysis of applicability through practical publications and examples from industrial case studies.
Optimization modeling underpins decision-making in logistics, manufacturing, energy, and finance, yet translating natural-language requirements into correct optimization formulations and solver-executable code remains labor-intensive. Although large language models (LLMs) have been explored for this task, evaluation is still dominated by toy-sized or synthetic benchmarks, masking the difficulty of industrial problems with $10^{3}$--$10^{6}$ (or more) variables and constraints. A key bottleneck is the lack of benchmarks that align natural-language specifications with reference formulations/solver code grounded in real optimization models. To fill in this gap, we introduce MIPLIB-NL, built via a structure-aware reverse construction methodology from real mixed-integer linear programs in MIPLIB~2017. Our pipeline (i) recovers compact, reusable model structure from flat solver formulations, (ii) reverse-generates natural-language specifications explicitly tied to this recovered structure under a unified model--data separation format, and (iii) performs iterative semantic validation through expert review and human--LLM interaction with independent reconstruction checks. This yields 223 one-to-one reconstructions that preserve the mathematical content of the original instances while enabling realistic natural-language-to-optimization evaluation. Experiments show substantial performance degradation on MIPLIB-NL for systems that perform strongly on existing benchmarks, exposing failure modes invisible at toy scale.
Bruno Santos, Rogério Luís C. Costa, Leonel Santos
Unlocking the potential of Industry 5.0 hinges on robust cybersecurity measures. This new Industrial Revolution prioritises human-centric values while addressing pressing societal issues such as resource conservation, climate change, and social stability. Recognising the heightened risk of cyberattacks due to the new enabling technologies in Industry 5.0, this paper analyses potential threats and corresponding countermeasures. Furthermore, it evaluates the existing industrial implementation frameworks, which reveals their inadequacy in ensuring a secure transition from Industry 4.0 to Industry 5.0. Consequently, the paper underscores the necessity of developing a new framework centred on cybersecurity to facilitate organisations' secure adoption of Industry 5.0 principles. The creation of such a framework is emphasised as a necessity for organisations.
David Wichner, Jeffrey Wishart, Jason Sergent
et al.
Safety Management Systems (SMSs) have been used in many safety-critical industries and are now being developed and deployed in the automated driving system (ADS)-equipped vehicle (AV) sector. Industries with decades of SMS deployment have established frameworks tailored to their specific context. Several frameworks for an AV industry SMS have been proposed or are currently under development. These frameworks borrow heavily from the aviation industry although the AV and aviation industries differ in many significant ways. In this context, there is a need to review the approach to develop an SMS that is tailored to the AV industry, building on generalized lessons learned from other safety-sensitive industries. A harmonized AV-industry SMS framework would establish a single set of SMS practices to address management of broad safety risks in an integrated manner and advance the establishment of a more mature regulatory framework. This paper outlines a proposed SMS framework for the AV industry based on robust taxonomy development and validation criteria and provides rationale for such an approach. Keywords: Safety Management System (SMS), Automated Driving System (ADS), ADS-Equipped Vehicle, Autonomous Vehicles (AV)
Fernando Miguelez, Josu Doncel, Maria Dolores Ugarte
Industrial processes generate a massive amount of monitoring data that can be exploited to uncover hidden time losses in the system. This can be used to enhance the accuracy of maintenance policies and increase the effectiveness of the equipment. In this work, we propose a method for one-step probabilistic multivariate forecasting of time variables involved in a production process. The method is based on an Input-Output Hidden Markov Model (IO-HMM), in which the parameters of interest are the state transition probabilities and the parameters of the observations' joint density. The ultimate goal of the method is to predict operational process times in the near future, which enables the identification of hidden losses and the location of improvement areas in the process. The input stream in the IO-HMM model includes past values of the response variables and other process features, such as calendar variables, that can have an impact on the model's parameters. The discrete part of the IO-HMM models the operational mode of the process. The state transition probabilities are supposed to change over time and are updated using Bayesian principles. The continuous part of the IO-HMM models the joint density of the response variables. The estimate of the continuous model parameters is recursively computed through an adaptive algorithm that also admits a Bayesian interpretation. The adaptive algorithm allows for efficient updating of the current parameter estimates as soon as new information is available. We evaluate the method's performance using a real data set obtained from a company in a particular sector, and the results are compared with a collection of benchmark models.
Metarobotics aims to combine next generation wireless communication, multi-sense immersion, and collective intelligence to provide a pervasive, itinerant, and non-invasive access and interaction with distant robotized applications. Industry and society are expected to benefit from these functionalities. For instance, robot programmers will no longer travel worldwide to plan and test robot motions, even collaboratively. Instead, they will have a personalized access to robots and their environments from anywhere, thus spending more time with family and friends. Students enrolled in robotics courses will be taught under authentic industrial conditions in real-time. This paper describes objectives of Metarobotics in society, industry, and in-between. It identifies and surveys technologies likely to enable their completion and provides an architecture to put forward the interplay of key components of Metarobotics. Potentials for self-determination, self-efficacy, and work-life-flexibility in robotics-related applications in Society 5.0, Industry 4.0, and Industry 5.0 are outlined.
Neighborhood characteristics have been broadly studied with different firm behaviors, e.g. birth, entry, expansion, and survival, except for firm exit. Using a novel dataset of foreign-invested enterprises operating in Shenzhen's electronics manufacturing industry from 2017 to 2021, I investigate the spillover effects of firm exits on other firms in the vicinity, from both the industry group and the industry class level. Significant neighborhood effects are identified for the industry group level, but not the industry class level.
Green building has become an increasingly vital topic in global environmental discussions and the construction industry. Sustainable buildings aim to reduce environmental impact, enhance energy efficiency, and create healthier, more comfortable environments. The research aims to understand global trends and practices related to green building concepts and sustainability initiatives. The research methodology employs a bibliometric approach involving the search, selection, and analysis of green building literature. Data bibliographies are analyzed using keyword frequency analysis, citation analysis, and data visualization. The results provide insights into global adoption trends and the roles of specific factors in green building. Various factors influence green building technology adoption, including perceptions of benefits, ease of use, social support, and government policies. This study emphasizes the significance of social, economic, and environmental factors in the diffusion of green building technology. The integration of the Internet of Things (IoT) can enhance efficiency. However, it's important to note that these factors may vary across regions. Further research is necessary for a more comprehensive understanding. In conclusion, this research demonstrates that the adoption of green building technology is influenced by factors such as knowledge, social trust, environmental attitudes, costs, and perceptions of benefits and ease of use. Government support and social factors also play crucial roles in adoption. The integration of the Internet of Things (IoT) holds the potential to improve efficiency. This study is relevant in both advanced and developing countries, such as Indonesia and Africa, promoting the concept of green building for global sustainability
Abstract In response to global challenges like climate change and resource depletion, national and international policies are increasingly emphasising sustainability. The bioeconomy represents a promising field to reconcile environmental integrity with economic development. The objective of this study is to examine the role of renewable resources in the construction industry within the framework of the bioeconomy, with a particular focus on Sustainable Development Goal 11: ‘Sustainable cities and communities’. The case study analyses the practical challenges and opportunities for innovation in Central Germany through a comprehensive regional analysis of construction practices using renewable resources. The methodology involved monitoring biomass flows by assessing harvest yields, area shares and biomass potentials. It also analysed the importance of the bio-based economy by examining employment data and identifying potential growth sectors. Stakeholder engagement was facilitated through knowledge transfer workshops to encourage collaboration and innovation. The results of the study highlight key trends within the industry, including the increasing prevalence of timber buildings, wood as a renewable carbon source in the chemical industry and the growing demand for wood-based packaging solutions. Policy initiatives are playing a supportive role in driving these developments, taking advantage of the region’s extensive wood resources (14.7 million m3 harvested in 2021) and the significant employment contribution of the forestry, wood and construction industries (2% of total employment, representing no less than 81,506 employees). These figures demonstrate the potential for sustainable construction practices to drive local economic growth and contribute to wider environmental objectives.
This review paper explores the integration of phase change materials (PCMs) in building insulation systems to enhance energy efficiency and thermal comfort. Through an extensive analysis of existing literature, the thermal performance of PCM-enhanced building envelopes is evaluated under diverse environmental conditions. This review highlights that PCMs effectively moderate indoor temperatures by absorbing and releasing heat during phase transitions, maintaining a stable indoor climate. This paper also delves into the detailed concepts of PCMs, including their classification and various applications within building insulation. It is noted that different types of PCMs have unique thermal properties and potential uses, which can be tailored to specific building requirements and climatic conditions. Furthermore, cost–benefit and environmental assessments presented in the reviewed studies suggest that incorporating PCMs into building materials offers significant potential for reducing energy consumption and mitigating environmental impacts. These assessments indicate that PCMs can lead to substantial energy savings by decreasing the reliance on heating and cooling systems, thereby lowering overall energy costs and carbon emissions. However, despite the promising outlook, this review identifies a need for further research to optimize PCM formulations and integration methods. This optimization is essential for overcoming current challenges and facilitating the widespread adoption of PCMs in the construction industry. Addressing issues such as long-term durability, compatibility with existing building materials, and cost-effectiveness will be crucial for maximizing the benefits of PCMs in enhancing energy efficiency and sustainability in buildings. Overall, this review underscores the transformative potential of PCMs in building insulation practices. By providing a comprehensive overview of PCM classifications, applications, and their impacts on energy efficiency and environmental sustainability, this paper lays the groundwork for future advancements and research directions in the field of PCM-enhanced building technologies.
The emerging prefabricated decoration (PD) sector in China offers a compelling avenue for revolutionizing the construction industry, aligning with the imperatives of sustainability and industrialization. However, the outlook for the diffusion of PD in China's construction industry is less than promising. The development of PD is closely related to its value creation and the intricate interplay of stakeholder strategies and their ramifications on PD's value co-creation necessitates thorough exploration. Based on evolutionary game theory, this study elucidates the dynamic interactions among local governments, developers, and decoration contractors in China's evolving PD landscape. A tripartite evolutionary game model was established, investigating the evolutionary stable equilibriums and the corresponding strategies. Then, an empirical analysis in Chongqing City validates the game models and primary conclusions. Scrutinizing subsidy thresholds, local subsidy biases, PD proportion in prefabrication, and binary innovation allocation, the research unveils nuanced insights pertinent to boosting PD's value co-creation. Findings advocate an assembly rate threshold of subsidy (65%–70 %) and a shift towards incentivizing decoration contractors to catalyze PD adoption. Additionally, excessive PD proportion and immature product investments may hinder industry development, prompting stakeholders to recalibrate strategies dynamically. These findings characterize the mechanisms of stakeholder value co-creation during the PD diffusion, enriching the implications of value co-creation theory in the emerging industry of the construction sector. They also furnish stakeholders committed to promoting the diffusion of PD in the domestic and international construction sectors with practical strategic guidance, particularly tailored for cities in the early stages of PD development.
The Pakistani pharmaceutical industry cannot ignore the contribution of pharmaceutical multinational corporations (MNCs) in terms of innovation and access to advanced treatments. The sale of a pharmaceutical manufacturing plant by an MNC to a Pakistani company has again sparked a debate on why MNCs are closing manufacturing operations in the country. National firms are currently giving MNCs a tough time in Pakistan. Besides competition, MNCs face mergers and acquisitions that hinder the expansion of existing facilities. In the recent past, there has been a noticeable shift in the market shares of multinational corporations (MNCs) and local companies. The market share of national firms has gradually increased, reaching 74.51%, while the market share of MNCs has decreased, falling to 25.49%. These local companies have increasingly partnered with foreign companies through joint ventures, which has had a positive impact on their growth and market share. Nevertheless, the federal government recently deregulated prices for non-essential medicines, so it is expected that MNCs will show their commitment to Pakistan by investing in the country. Investing in new molecules and infrastructure is necessary for MNCs in order to maintain their position in the market. Infrastructure investment is crucial for the smooth operation of pharmaceutical firms. This includes the construction of state-of-the-art manufacturing facilities, the establishment of research and development centres, and the upgrading of technology.
Therapeutics. Pharmacology, Pharmacy and materia medica
Uzor Onyia, Izuchukwu Stanley Ani, Uchechukwu Frank Nwankwo
Abstract Supply chain management has shown to be an aspect of construction that is neglected, yet with great impact on project delivery time frames and the overall cost of construction projects. This research seeks to identify, assess, and propose solutions for the challenges hindering the adoption of digital technologies in construction supply chain management, hence creating a sustainable foundation for practitioners and relevant agencies to leverage the benefits of digital technologies in the management of construction supply chain. The literature review identified challenges of digital technology adoption in construction supply chain management, guiding subsequent research. A structured questionnaire captured demographic data and insights, with 171 industry professionals surveyed. Data analysis, including Likert scale ratings and Relative Impact/Feasibility Index, informed an Impact-Feasibility Matrix. This matrix categorized challenges and strategies into quadrants, aiding prioritization. A conceptual framework emerged, offering actionable insights for addressing adoption challenges. The study identified 14 challenges to digital technology adoption in construction supply chain management, ranked by respondents. These challenges were categorized based on their impact and feasibility, with strategies mapped accordingly. The resulting framework prioritizes High Impact/High Feasibility challenges for action, while Low Impact/Low Feasibility challenges may require further exploration. This strategic approach aims to address key hurdles efficiently, enhancing the adoption of digital technologies in construction supply management. The findings from this study will inform construction practitioners and relevant agencies about the challenges hindering the seamless integration of digital technologies into construction supply chain management and propose feasible strategies for addressing these challenges.
Industry 4.0 operates based on IoT devices, sensors, and actuators, transforming the use of computing resources and software solutions in diverse sectors. Various Industry 4.0 latency-sensitive applications function based on machine learning to process sensor data for automation and other industrial activities. Sending sensor data to cloud systems is time consuming and detrimental to the latency constraints of the applications, thus, fog computing is often deployed. Executing these applications across heterogeneous fog systems demonstrates stochastic execution time behavior that affects the task completion time. We investigate and model various Industry 4.0 ML-based applications' stochastic executions and analyze them. Industries like oil and gas are prone to disasters requiring coordination of various latency-sensitive activities. Hence, fog computing resources can get oversubscribed due to the surge in the computing demands during a disaster. We propose federating nearby fog computing systems and forming a fog federation to make remote Industry 4.0 sites resilient against the surge in computing demands. We propose a statistical resource allocation method across fog federation for latency-sensitive tasks. Many of the modern Industry 4.0 applications operate based on a workflow of micro-services that are used alone within an industrial site. As such, industry 4.0 solutions need to be aware of applications' architecture, particularly monolithic vs. micro-service. Therefore, we propose a probability-based resource allocation method that can partition micro-service workflows across fog federation to meet their latency constraints. Another concern in Industry 4.0 is the data privacy of the federated fog. As such, we propose a solution based on federated learning to train industrial ML applications across federated fog systems without compromising the data confidentiality.