Hasil untuk "Employee participation in management. Employee ownership. Industrial democracy. Works councils"

L’Allemagne accueille de longue date un flux non négligeable de travailleurs polonais, ceux-ci formant le deuxième groupe de travailleurs étrangers par la taille et apportant une importante contribution à l’économie allemande. À partir d’un vaste ensemble de données administratives, nous analysons la structure des salaires des travailleurs polonais. Nous examinons un large éventail de facteurs (caractéristiques individuelles et de l’entreprise, performances du marché du travail et effets de réseau potentiels) et tenons compte de la sélectivité spatiale, distinguant trois types de régions (urbaines, semi-urbaines et périphériques). Nous montrons que, comparativement à la Pologne, l’Allemagne demeure attrayante pour les travailleurs polonais, en particulier les travailleurs peu qualifiés. Comme les salariés polonais sont moins bien rémunérés que leurs homologues allemands, une revalorisation salariale pourrait être un moyen de les retenir en Allemagne. Les caractéristiques relatives au réseau révèlent l’existence d’effets de concurrence parmi les immigrés polonais.

En Hongrie, le rôle de la négociation collective dans l’encadrement de l’emploi est marginal et ne cesse de régresser. Des défauts du cadre juridique sont probablement les principales causes de la faiblesse et de la baisse de la couverture conventionnelle. S’appuyant sur les normes de l’OIT et sur l’expérience d’autres pays, les auteurs analysent les règles relatives à la représentativité syndicale et proposent des réformes susceptibles d’inverser la tendance à la diminution de la couverture. Ils présentent divers aspects de la situation hongroise en matière de représentativité en s’inscrivant dans un cadre théorique européen, ce qui présente un intérêt particulier pour les pays où la syndicalisation est faible, notamment ceux d’Europe orientale. 

Dalenda Ben Ahmed

The purpose of this study is to determine the impact of employee ownership on employees’ behaviour. We explore how financial gains can influence employee satisfaction, motivation, and the reduction of absenteeism and turnover. Our study, conducted using French and Canadian companies as examples, focused on the dynamics of contributions, discounts, and the percentage of employee shares over a five-year period. The results of the study show that employee motivation, satisfaction, and the reduction of absenteeism and turnover are explained by the evolution of discount, matching contribution, and percentage of employee shares. Employee shareholders were more cooperative, involved, motivated, and satisfied with the simple fact of being employee shareholders. This work can be considered as one of the pioneer studies of the effect of employee ownership on the psychological behaviour of employee shareholders.

Pour les institutions des Nations Unies, dont l’OIT, le travail soutenable contribue à l’avènement d’économies vertes et inclusives. Les autrices examinent si cette vision dominante est reprise ou remise en cause sur le terrain en s’appuyant sur une analyse décoloniale de discours concernant des communautés touchées par l’activité minière au Brésil et au Canada. Elles constatent que, alors que les conceptions décoloniales sont centrées sur le soin à la terre et aux personnes, la reconnaissance de la dépendance écologique, le respect de la vie et le travail reproductif, les visions dominantes du travail soutenable sont souvent instrumentalisées pour légitimer des pratiques incompatibles avec cette vision.

Tom Maus, Nico Zengeler, Tobias Glasmachers

We present a novel reinforcement learning (RL) environment designed to both optimize industrial sorting systems and study agent behavior in evolving spaces. In simulating material flow within a sorting process our environment follows the idea of a digital twin, with operational parameters like belt speed and occupancy level. To reflect real-world challenges, we integrate common upgrades to industrial setups, like new sensors or advanced machinery. It thus includes two variants: a basic version focusing on discrete belt speed adjustments and an advanced version introducing multiple sorting modes and enhanced material composition observations. We detail the observation spaces, state update mechanisms, and reward functions for both environments. We further evaluate the efficiency of common RL algorithms like Proximal Policy Optimization (PPO), Deep-Q-Networks (DQN), and Advantage Actor Critic (A2C) in comparison to a classical rule-based agent (RBA). This framework not only aids in optimizing industrial processes but also provides a foundation for studying agent behavior and transferability in evolving environments, offering insights into model performance and practical implications for real-world RL applications.

Daniel Lindenschmitt, Paul Seehofer, Marius Schmitz et al.

This paper presents the application of Dynamic Spectrum Management (DSM) for future 6G industrial networks, establishing an efficient controller for the Networks-in-Network (NiN) concept. The proposed architecture integrates nomadic as well as static sub-networks (SNs with diverse Quality of Service (QoS) requirements within the coverage area of an overlayer network, managed by a centralized spectrum manager (SM). Control plane connectivity between the SNs and the DSM is ensured by the self-organizing KIRA routing protocol. The demonstrated system enables scalable, zero-touch connectivity and supports nomadic SNs through seamless discovery and reconfiguration. SNs are implemented for modular Industrial Internet of Things (IIoT) scenarios, as well as for mission-critical control loops and for logistics or nomadic behavior. The DSM framework dynamically adapts spectrum allocation to meet real-time demands while ensuring reliable operation. The demonstration highlights the potential of DSM and NiNs to support flexible, dense, and heterogeneous wireless deployments in reconfigurable manufacturing environments.

Andrea Mario Lavezzi, Marco Quatrosi

In this article, we study the effects of organized crime infiltration in city councils on environmental policies implemented in Italy at the municipal level. To this purpose, we exploit the exogenous shock of the removal of a city council infiltrated by the mafia and its substitution with an external Commission, allowed in Italy by the law 164/1991. Our results suggest that after dissolution, environmental policies improve in several dimensions: the capital expenditure for sustainable development and the environment increases; the current expenditure on integrated water system increases; the percentage of sorted waste increases because, as we show, public expenditure is reallocated toward sorted waste at the expenses of unsorted waste. These results are robust to different specifications of the control group. In addition, we find significant spillover effects: the dissolution of infiltrated city councils implies an improvement in environmental policies in adjacent municipalities. Our results have a straightforward policy implication, the need to combat organized crime as a way to improve the environmental conditions of the territories plagued by its pervasive presence.

masoumeh mohammadi, Golamreza Bordbar, Ali Morovi Sharif Abadi et al.

Purpose: Employee discipline is one of the important and long-standing concepts in management, which has even continued in postmodern management approaches. The current article aimed to identify the components of the disciplinary behavior of the academic staff members using a mixed research methodology. Methodology: To identify the components and indicators of disciplinary behavior, we used meta-composition technique (study of documents) in the first stage, and in the second stage, we applied the fuzzy Delphi method with expert participants, some of whom with theoretical experience and some with practical experience. Then, from among them, twenty-five individuals were purposefully selected. Finally, for prioritizing and weighting the components and indicators of disciplinary behavior acquired from the five universities under study (Tehran, Zanjan, Yazd, Tabriz, and Ardabil universities), the Waspas and Critic methods were used in a row. Findings: The outcomes of the first and second stages of statistical computation have led to seven components of disciplinary behavior including: types of discipline, factors affecting disciplinary behavior, types of violations, disciplinary procedures, types of punishment, consequences of discipline, antecedents of discipline and disciplinary knowledge, all in all with 37 indicators.  Finally, we are presenting the priority list of the five universities as follows:  Tehran University, Zanjan University, Yazd University, Tabriz University, and Ardabil University.

Christofel Rio Goenawan

In the modern world, the development of Artificial Intelligence (AI) has contributed to improvements in various areas, including automation, computer vision, fraud detection, and more. AI can be leveraged to enhance the efficiency of Autonomous Smart Traffic Management (ASTM) systems and reduce traffic congestion rates. This paper presents an Autonomous Smart Traffic Management (STM) system that uses AI to improve traffic flow rates. The system employs the YOLO V5 Convolutional Neural Network to detect vehicles in traffic management images. Additionally, it predicts the number of vehicles for the next 12 hours using a Recurrent Neural Network with Long Short-Term Memory (RNN-LSTM). The Smart Traffic Management Cycle Length Analysis manages the traffic cycle length based on these vehicle predictions, aided by AI. From the results of the RNN-LSTM model for predicting vehicle numbers over the next 12 hours, we observe that the model predicts traffic with a Mean Squared Error (MSE) of 4.521 vehicles and a Root Mean Squared Error (RMSE) of 2.232 vehicles. After simulating the STM system in the CARLA simulation environment, we found that the Traffic Management Congestion Flow Rate with ASTM (21 vehicles per minute) is 50\% higher than the rate without STM (around 15 vehicles per minute). Additionally, the Traffic Management Vehicle Pass Delay with STM (5 seconds per vehicle) is 70\% lower than without STM (around 12 seconds per vehicle). These results demonstrate that the STM system using AI can increase traffic flow by 50\% and reduce vehicle pass delays by 70\%.

Alexander Windmann, Philipp Wittenberg, Marvin Schieseck et al.

In Industry 4.0, Cyber-Physical Systems (CPS) generate vast data sets that can be leveraged by Artificial Intelligence (AI) for applications including predictive maintenance and production planning. However, despite the demonstrated potential of AI, its widespread adoption in sectors like manufacturing remains limited. Our comprehensive review of recent literature, including standards and reports, pinpoints key challenges: system integration, data-related issues, managing workforce-related concerns and ensuring trustworthy AI. A quantitative analysis highlights particular challenges and topics that are important for practitioners but still need to be sufficiently investigated by academics. The paper briefly discusses existing solutions to these challenges and proposes avenues for future research. We hope that this survey serves as a resource for practitioners evaluating the cost-benefit implications of AI in CPS and for researchers aiming to address these urgent challenges.

Aref Ayati, Mohammad Mahdi Hashemi, Mohsen Saffar et al.

Regarding the concepts of urban management, digital transformation, and smart cities, various issues are presented. Currently, we like to attend to location allocation problems that can be a new part of digital transformation in urban management (such as locating and placing facilities, locating and arranging centers such as aid and rescue centers, or even postal hubs, telecommunications, electronic equipment, and data centers, and routing in transportation optimization). These issues, which are seemingly simple but in practice complex, are important in urban environments, and the issue of accurate location allocation based on existing criteria directly impacts cost management, profit, efficiency, and citizen satisfaction. In recent years, researchers have used or presented various models and methods for location allocation problems, some of which will be mentioned in this article. Given the nature of these problems, which are optimization problems, this article will also examine existing research from an optimization perspective in summary. Finally, a brief conclusion will be made of the existing methods and their weaknesses, and suggestions will be made for continuing the path and improving scientific and practical research in this field.

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)

Shengye Wan, Joshua Saxe, Craig Gomes et al.

Recent research advances in Artificial Intelligence (AI) have yielded promising results for automated software vulnerability management. AI-based models are reported to greatly outperform traditional static analysis tools, indicating a substantial workload relief for security engineers. However, the industry remains very cautious and selective about integrating AI-based techniques into their security vulnerability management workflow. To understand the reasons, we conducted a discussion-based study, anchored in the authors' extensive industrial experience and keen observations, to uncover the gap between research and practice in this field. We empirically identified three main barriers preventing the industry from adopting academic models, namely, complicated requirements of scalability and prioritization, limited customization flexibility, and unclear financial implications. Meanwhile, research works are significantly impacted by the lack of extensive real-world security data and expertise. We proposed a set of future directions to help better understand industry expectations, improve the practical usability of AI-based security vulnerability research, and drive a synergistic relationship between industry and academia.

Saied Sehhat, Hamed Dehghanan, Zohreh Dehdashti Shahrokh et al.

Background & Purpose:The performance of helth centers is a multi-dimensional and complex construction and requires meeting the expectations and satisfying several key stakeholders at different levels of such service-based organizations. In order to gain a more comprehensive understanding of the evaluation of the comprehensive performance of these organizations, the current research has reviewed and classified the indicators that have been approved in various studies to assess various aspects of the performance of medical (helth) centers.Methodology: This is a qualitative applied research and it has been used in meta-composite method. The information sources of this stage included articles published in international scientific databases, which were selected based on the criteria for entering the meta-synthesis process.Findings: The model presented for evaluating the performance of medical centers includes three main levels: individual (medical staff), group (hospital departments) and organizational (hospitals). Each of these three levels includes components that evaluate different aspects of health service performance through related criteria.Conclusion: In evaluating the performance of medical centers, simultaneous attention to the performance of employees, organizational departments, and the entire organization can achieve a more comprehensive understanding and help improve the effectiveness of performance improvement programs. The findings of the present research can be used as a guiding model for such evaluations.

Mahdieh vishlaghi, Alireza Moghaddam, Reza Sepahvand et al.

Purpose: Transformation of organizations and industries requires strategic renewal, and due to the uncertainty in competitive environments, managers can provide the basis for renewal in organizations by identifying and ranking the drivers of renewal and transformation. Accordingly, the purpose of this study was to identify and categorize the drivers of strategic renewal drivers in Iranian development organizations. Methodology: The drivers of strategic renewal were identified through semi-structured interviews with 22 former and current IDRO professors and managers based on theoretical saturation technique. To categorize the identified propellants, the opinions of 58 senior and middle managers of state-owned companies affiliated to IDRO were gathered. Purposive sampling method and questionnaires were used. Findings: MAXQDA software was used, and the coding of the interviews led to the identification of 15 drivers in the formation of strategic renewal. The drivers identified by the interpretive structural method led to the formation of six levels, of which the capabilities of the organization and the power of adaptability were the most effective. Besides, the competencies of the employees and the correct analysis of the market were the most affected ones. Originality: Modeling the drivers of strategic renewal can narrow the theoretical gap in the research in this area. Moreover, the contribution of the current research for the managers and key decision makers of public companies affiliated to IDRO is that it helps them to identify the drivers of strategic renewal and to provide the basis for the renewal and transformation of their companies by investing in the most effective factors.

Christos Anagnostopoulos, Georgios Mylonas, Apostolos P. Fournaris et al.

Virtual and augmented reality are currently enjoying a great deal of attention from the research community and the industry towards their adoption within industrial spaces and processes. However, the current design and implementation landscape is still very fluid, while the community as a whole has not yet consolidated into concrete design directions, other than basic patterns. Other open issues include the choice over a cloud or edge-based architecture when designing such systems. Within this work, we present our approach for a monitoring intervention inside a factory space utilizing both VR and AR, based primarily on edge computing, while also utilizing the cloud. We discuss its main design directions, as well as a basic ontology to aid in simple description of factory assets. In order to highlight the design aspects of our approach, we present a prototype implementation, based on a use case scenario in a factory site, within the context of the ENERMAN H2020 project.

Julian Senoner, Bernhard Kratzwald, Milan Kuzmanovic et al.

To meet order fulfillment targets, manufacturers seek to optimize production schedules. Machine learning can support this objective by predicting throughput times on production lines given order specifications. However, this is challenging when manufacturers produce customized products because customization often leads to changes in the probability distribution of operational data -- so-called distributional shifts. Distributional shifts can harm the performance of predictive models when deployed to future customer orders with new specifications. The literature provides limited advice on how such distributional shifts can be addressed in operations management. Here, we propose a data-driven approach based on adversarial learning and job shop scheduling, which allows us to account for distributional shifts in manufacturing settings with high degrees of product customization. We empirically validate our proposed approach using real-world data from a job shop production that supplies large metal components to an oil platform construction yard. Across an extensive series of numerical experiments, we find that our adversarial learning approach outperforms common baselines. Overall, this paper shows how production managers can improve their decision-making under distributional shifts.

Rodrigo Hernangómez, Alexandros Palaios, Cara Watermann et al.

This paper presents two wireless measurement campaigns in industrial testbeds: industrial Vehicle-to-vehicle (iV2V) and industrial Vehicle-to-infrastructure plus Sensor (iV2I+), together with detailed information about the two captured datasets. iV2V covers sidelink communication scenarios between Automated Guided Vehicles (AGVs), while iV2I+ is conducted at an industrial setting where an autonomous cleaning robot is connected to a private cellular network. The combination of different communication technologies within a common measurement methodology provides insights that can be exploited by Machine Learning (ML) for tasks such as fingerprinting, line-of-sight detection, prediction of quality of service or link selection. Moreover, the datasets are publicly available, labelled and prefiltered for fast on-boarding and applicability.

shuvrangshu Jana, Rudrashis Majumder, Aashay Bhise et al.

This paper discusses a Decision Support System (DSS) for cases prediction, allocation of resources, and lockdown management for managing COVID-19 at different levels of a government authority. Algorithms incorporated in the DSS are based on a data-driven modeling approach and independent of physical parameters of the region, and hence the proposed DSS is applicable to any area. Based on predicted active cases, the demand of lower-level units and total availability, allocation, and lockdown decision is made. A MATLAB-based GUI is developed based on the proposed DSS and could be implemented by the local authority.

Reza Sepahvand, Masoome Momeni Mofrad, Saber Taghipour

Purpose: Eligibility for promotion is a strategic concept in the development of today's organizations, because it signifies and highlights the existence of valuable human capital, and at the same time, causes the organization’s competitive advantage. Accordingly, the present study is conducted with the aim of designing a promotability model for managers, who are considered to be one of the most valuable and strategic group of human capital in state-owned organizations. Methodology: As a mixed research, it was both qualitative and quantitative. In the qualitative part, the statistical population consisted of university professors in the fields of human resource management, organizational behavior management and strategic management, as well as expert managers of governmental organizations. Using the snowball sampling method and based on the principle of theoretical saturation, 16 subjects were selected. The required data in this section were collected by applying semi-structured interviews. Interpretive structural method was used for the modeling of managers’ promotion criteria. In this section, the statistical population consisted of managers and experts of governmental organizations in Khoramabad, 24 of whom were selected as a sample through targeted sampling method. Data required for modeling were collected by pairwise comparison questionnaires. Findings: The research findings include indicators and components of a model for managers’ promotion. Thus, the model contains 14 indicators which fall into 4 levels.  Originality: In addition to reducing the theoretical gap in the field under study, this study has provided a significant contribution and context for other researches to follow and develop it. Also, due to the lack of any similar study in the field, the results of this study, in addition to reducing the existing disagreement regarding promotion indicators, can be an appropriate device to develop strategies focusing on managers in governmental organizations.