This work outlines Frederick Winslow Taylor's philosophy and practical applications of The Principles of Scientific Management. Taylor argues that the greatest loss suffered by the country is the waste of human effort due to industrial inefficiency, a problem even larger than the conservation of material resources. The primary goal is to secure maximum prosperity for the employer (permanent success and high dividends) and maximum prosperity for each employee (high wages and development to maximum efficiency). The key to this prosperity is maximum productivity. Taylor contends that the widespread practice of "soldiering" (deliberately working slowly) and inefficient "rule-of-thumb" methods stems from the underlying antagonism between labor and management. Scientific Management presumes that the true interests of the two sides are identical and proposes four fundamental principles: 1) Develop a true science for each element of a man's work, replacing old rule-of-thumb methods; 2) Scientifically select and then train, teach, and develop the workman; 3) Heartily cooperate with the men to ensure all work is done in accordance with the developed science; 4) Establish an almost equal division of work and responsibility between the management and the workmen, with the management taking over planning and preparatory duties. The essence of this cooperation is for management to daily assign a carefully planned, high-paying task to each workman and teach him how to perform it using scientific methods. Taylor provides practical illustrations, from simple labor like shoveling and bricklaying to complex machine work, demonstrating that scientific analysis can double productivity and lead to a 30% to 100% wage increase for workmen, resulting in industrial peace and prosperity.
D. Campos, R. Gómez-García, Ana A. Vilas-Boas
et al.
The management of industrial fruit by-products is important not only to decrease the volume of food waste accumulated in the landfills but also to develop strategies through reuse with the purpose to valorise and add economic value. The disposal of food waste leads to different global issues in different sectors, such as social, environmental and economical. These by-products represent a rich source of valuable compounds (polyphenols) with high antioxidant activity, which can be extracted through biotechnological methodologies for future industrial applications. In this context, the management of fruit by-products is challenged to move from a linear economy to a circular economy. Therefore, the purpose of this review is to provide a critical view of an integrated valorisation of fruit by-products to overcome a global issue, via the production of antioxidant extracts with high economic value. A case study of pineapple processing industrialization in a circular economy is explored and discussed.
Nowadays, driven by the rapid development of smart mobile equipments and 5G network technologies, the application scenarios of Internet of Things (IoT) technology are becoming increasingly widespread. The integration of IoT and industrial manufacturing systems forms the industrial IoT (IIoT). Because of the limitation of resources, such as the computation unit and battery capacity in the IIoT equipments (IIEs), computation-intensive tasks need to be executed in the mobile edge computing (MEC) server. However, the dynamics and continuity of task generation lead to a severe challenge to the management of limited resources in IIoT. In this article, we investigate the dynamic resource management problem of joint power control and computing resource allocation for MEC in IIoT. In order to minimize the long-term average delay of the tasks, the original problem is transformed into a Markov decision process (MDP). Considering the dynamics and continuity of task generation, we propose a deep reinforcement learning-based dynamic resource management (DDRM) algorithm to solve the formulated MDP problem. Our DDRM algorithm exploits the deep deterministic policy gradient and can deal with the high-dimensional continuity of the action and state spaces. Extensive simulation results demonstrate that the DDRM can reduce the long-term average delay of the tasks effectively.
The penetration of renewable energies is increasing in power systems all over the world. The volatility and intermittency of renewable energies pose real challenges to energy systems. To overcome the problem, demand-side flexibility is a practical solution in all demand sectors, including residential, commercial, agricultural, and industrial sectors. This paper provides a comprehensive review of industrial demand response opportunities in energy-intensive industries. Flexibility potentials are discussed from (1) viewpoints of power flexibility for cement manufacturing and aluminum smelting plants (2) viewpoints of joint power-heat flexibility for oil refinery industries. The flexibility potentials of industrial processes are classified based on their compatibility with time responses on long, mid, and short advance notices of different electricity market floors and ancillary service markets. Challenges and opportunities of industrial demand management are classified from viewpoints of power systems and industry owners. Software tools and solution methodologies of industrial energy models are surveyed for energy researchers. The studies show that cement manufacturing plants have great potentials in providing peak-shaving and valley-filling in crushers and cement mills with up to 10% and 16.9% reduction in energy consumption cost and power consumption, respectively. The aluminum smelting plants can provide up to 34.2% and 20.70% reduction in energy consumption cost and power consumption by turning down/off the variable voltage smelting pots.
Abstract Product lifecycle management (PLM) aims to seamlessly manage all products and information and knowledge generated throughout the product lifecycle for achieving business competitiveness. Conventionally, PLM is implemented based on standalone and centralized systems provided by software vendors. The information of PLM is hardly to be integrated and shared among the cooperating parties. It is difficult to meet the requirements of the openness, interoperability and decentralization of the Industry 4.0 era. To address these challenges, this paper proposed an industrial blockchain-based PLM framework to facilitate the data exchange and service sharing in the product lifecycle. Firstly, we proposed the concept of industrial blockchain as the use of blockchain technology in the industry with the integration of IoT, M2M, and efficient consensus algorithms. It provided an open but secured information storage and exchange platform for the multiple stakeholders to achieve the openness, interoperability and decentralization in era of industry 4.0. Secondly, we proposed and developed customized blockchain information service to fulfill the connection between a single node with the blockchain network. As a middleware, it can not only process the multi-source and heterogeneous data from varied stages in the product lifecycle, but also broadcast the processed data to the blockchain network. Moreover, smart contract is used to automate the alert services in the product lifecycles. Finally, we illustrated the blockchain-based application between the cooperating partners in four emerging product lifecycle stages, including co-design and co-creation, quick and accurate tracking and tracing, proactive maintenance, and regulated recycling. A simulation experiment demonstrated the effectiveness and efficiency of the proposed framework. The results showed that the proposed framework is scalable and efficient, and hence it is feasible to be adopted in industry. With the successful development of the proposed platform, it is promising to provide an effective PLM for improving interoperability and cooperation between stakeholders in the entire product lifecycle.
Industrial policy is back in the mainstream debates. The paper provides a long-term analytical perspective of the industrial policy debate, and it critically assesses the current mainstream phase of the debate in light of three fundamental theoretical insights that developed along several decades of industrial policy theory and practice. These are related to the (i) structural interdependencies, tensions and dualism arising in the industrialisation process; (ii) variety and types of institutions for industrialisation and the importance of policy alignment; (iii) conflict management role of the government, alongside his entrepreneurial function, and the importance of government organisational capabilities. Building on this theoretical analysis, the last section of the paper provides a framework for the strategic coordination of packages of interactive industrial policy measures. The Policy Package Matrix is introduced as an operationalisation of the framework and a tool for effective industrial policy making.
Machine learning (ML) has become a cornerstone of critical applications, but its vulnerability to data poisoning attacks threatens system reliability and trustworthiness. Prior studies have begun to investigate the impact of data poisoning and proposed various defense or evaluation methods; however, most efforts remain limited to quantifying performance degradation, with little systematic comparison of internal behaviors across model architectures under attack and insufficient attention to interpretability for revealing model vulnerabilities. To tackle this issue, we build a reproducible evaluation pipeline and emphasize the importance of integrating robustness with interpretability in the design of secure and trustworthy ML systems. To be specific, we propose a unified poisoning evaluation framework that systematically compares traditional ML models, deep neural networks, and large language models under three representative attack strategies including label flipping, random corruption, and adversarial insertion, at escalating severity levels of 30%, 50%, and 75%, and integrate LIME-based explanations to trace the evolution of model reasoning. Experimental results demonstrate that traditional models collapse rapidly under label noise, whereas Bayesian LSTM hybrids and large language models maintain stronger resilience. Further interpretability analysis uncovers attribution failure patterns, such as over-reliance on neutral tokens or misinterpretation of adversarial cues, providing insights beyond accuracy metrics.
Abstract The textile industry is one of the important and largest industry that consumes major chunk of the water in the world. This industry produces a large amount of wastewater during the processes such as sizing, de-sizing, scouring, bleaching, mercerizing, dyeing, printing, and finishing. The used water produced after such processes affects the environment heavily due to its composition such as mineral salts and oils present in suspended state, metals and metal complexes, dyes, various chemicals, some readily-biodegradable products and some constituents that are hard to biodegrade. The treatment of such hazardous effluent to reuse the water in certain water demanding processes is essential. Considering the worldwide application of the textiles, the appropriate management of water resources in the sector includes the treatment of effluent by efficient technology and the reuse of the water. This article displays an overview of waste management during textile industrial processes. It aims at giving oversight on waste minimization and reuse along with wastewater treatment methods. It also involves the cross-utilization of effluent between processes for achieving water efficiency. This review covers advanced waterless textile dyeing processes, zero liquid discharge techniques, advanced oxidation processes, biological treatment methods, which can be a sustainable and greener approach to reducing the waste generation.
Water supply for domestic and industrial purposes, Environmental sciences
Accurate estimation of tuna catch is crucial for effective pelagic fishery management and resource conservation. However, existing manual counting methods suffer from issues such as low accuracy and poor timeliness, highlighting the urgent need for an efficient and automated solution. This paper proposes an automatic tuna counting method based on the YOLOv8n-DMTNet target detection algorithm combined with the improved ByteTrack tracking algorithm. The method uses YOLOv8n as the base model, enhanced with detail-enhanced convolution and a multi-scale feature fusion pyramid network, which significantly improves detection accuracy in complex marine environments. Additionally, a dynamic, task-aligned detection head is introduced to optimize the synergy between classification and localization tasks. To further improve counting accuracy, the ByteTrack algorithm is employed for target tracking, and a region-specific counting method is designed to prevent double counting and omission due to occlusion and motion irregularities. Experimental results show that the improved YOLOv8n-DMTNet model achieves a 9.2% increase in mAP@0.5 and a 6.4% increase in mAP@0.5:0.95 compared to YOLOv8n in the tuna detection task, while reducing the number of parameters by 42.3% and computational complexity by 33.3%. The counting accuracy reaches 93.5%, and the method demonstrates superior performance in terms of accuracy, robustness, and computational resource efficiency, making it well-suited for resource-constrained fishing vessel environments. This approach provides reliable technical support for automated catch counting in pelagic fisheries.
This study contributes to developing the existing knowledge regarding data-driven sustainable supply chain management (SSCM) indicators under industrial disruption and ambidexterity. SSCM is a type of information flow management that facilitates cooperation and collaboration among supply chain players and stakeholders while considering economic, social, and environmental perspectives. Previous studies have failed to (1) generate these indicators from databases and confirm the validity of the effective indicators; (2) build a hierarchical structure with interrelationships under industrial disruption and ambidexterity; and (3) identify the indicators necessary for effective textile performance. The proposed hybrid method generates indicators from a database and based on the existing literature. This study proposes using the fuzzy Delphi method to validate these indicators in the textile industry and applies the best and worst methods to examine the most effective and ineffective indicators. Valid aspects and criteria are used to construct a hierarchical structure under conditions of industrial disruption and ambidexterity. The results show that the most important aspects are financial vulnerability, supply chain uncertainty, risk assessment, and resilience; these aspects are drivers that are guaranteed to ensure the effectiveness of SSCM under industrial disruption and ambidexterity. Financial crisis response, business continuity, supply chain integration, bullwhip effect, facility location, and supplier selection are highlighted as vital practical strategies.
Smart factory under Industry 4.0 and industrial Internet of Things (IoT) has attracted much attention from both academia and industry. In wireless industrial networks, industrial IoT and IoT devices have different quality-of-service (QoS) requirements, ranging from ultra-reliable low-latency communications (URLLC) to high transmission data rates. These industrial networks will be highly complex and heterogeneous, as well as the spectrum and energy resources are severely limited. Hence, this article presents a heterogeneous radio frequency (RF)/visible light communication (VLC) industrial network architecture to guarantee the different QoS requirements, where RF is capable of offering wide-area coverage and VLC has the ability to provide high transmission data rate. A joint uplink and downlink energy-efficient resource management decision-making problem (network selection, subchannel assignment, and power management) is formulated as a Markov decision process. In addition, a new deep post-decision state (PDS)-based experience replay and transfer (PDS-ERT) reinforcement learning algorithm is proposed to learn the optimal policy. Simulation results corroborate the superiority in performance of the presented heterogeneous network, and verify that the proposed PDS-ERT learning algorithm outperforms other existing algorithms in terms of meeting the energy efficiency and the QoS requirements.
Performing deep neural network (DNN) inference in real time requires excessive network resources, which poses a big challenge to the resource-limited industrial Internet of things (IIoT) networks. To address the challenge, in this paper, we introduce an end-edge-cloud orchestration architecture, in which the inference task assignment and DNN model placement are flexibly coordinated. Specifically, the DNN models, trained and pre-stored in the cloud, are properly placed at the end and edge to perform DNN inference. To achieve efficient DNN inference, a multi-dimensional resource management problem is formulated to maximize the average inference accuracy while satisfying the strict delay requirements of inference tasks. Due to the mix-integer decision variables, it is difficult to solve the formulated problem directly. Thus, we transform the formulated problem into a Markov decision process which can be solved efficiently. Furthermore, a deep reinforcement learning based resource management scheme is proposed to make real-time optimal resource allocation decisions. Simulation results are provided to demonstrate that the proposed scheme can efficiently allocate the available spectrum, caching, and computing resources, and improve average inference accuracy by 31.4$\%$ compared with the deep deterministic policy gradient benchmark.
The new industrial era has brought new opportunities and chances for the entire business development. Smart machines, artificial intelligence, cloud computing, the Internet of Things, big data are taking over many jobs and roles, thus leaving room for the development of new skills and abilities. The rapid technological development in terms of automation and digitization has made machines replace human work. In this sense, it is a matter of time when technology will replace traditional accountants. (Management) accountants who want to adapt and survive in the digital world have to improve their offer and change the focus from data calculation to interpretation of results and business management. Thus, by applying new digital information technology tools, management accounting can provide quality information for determining the costs of products and services, performance measurement, planning and control, strategic and operational decision-making and the like. The general objective of this paper is to review the potential impact of digital information technologies on the usefulness of cost accounting systems and organizational performance in healthcare institutions in the Republic of Serbia with the help of statistical analysis of the relationship between the selected variables. The results of the analysis show that digital technologies have a great impact on the usefulness of the cost accounting system. Also, the largest number of respondents pointed out that improved IT systems have a positive effect on increasing organizational performance.
Abstract The possibilities of using cognitive technologies in the organization of systematic industrial enterprise management are described in the article. Strategic links are defined in the development of a system of stochastic models of enterprise management based on artificial intelligence. The possibility of introduction of the Perceptron model in the industrial enterprise management with the purpose of identification of “bottlenecks” in the functionality of business activity and improvement of procedures of decision-making in the framework of creation of the program of development and technical re-equipment of the enterprise is proven. The authors offered an organizational and economic mechanism of operation of an industrial enterprise, which includes new means of implementation of managerial actions through the use of a matrix of assessment of the level of implementation of cognitive technologies. The method of determining priority directions for the implementation of cognitive technologies at an enterprise was developed based on the results of the assessment of the depth of penetration of cognitive technologies and the result obtained from their implementation, which additionally takes into account the resource ratio of the implemented technologies defined as the ratio of estimates of the actual level of competencies to what is needed to work with new cognitive technologies, which allows to obtain the planned economic and organizational effect.