Hasil untuk "Management. Industrial management"

Chintamani Bagwe

RegTech is a rapidly rising financial services sector focused on using cutting-edge technology to improve the process of regulatory compliance. RegTech solutions are characterized by numerous features and benefits that can considerably contribute to helping organizations operate effectively in the increasingly regulated environment, when it comes to compliance and risk management. This paper sheds light on why RegTech will be one of the most promising markets, driven by the rising cost of compliance and the growing reliance on technology in crisis management. Moreover, this paper will examine the advantages of using such solutions to strike a balance between compliance and operational efficiencies. This paper will deepen the understanding of regulatory compliance, introduce RegTech, and examine the benefits of using these solutions to achieve compliance.

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.

Anwesha Bhattacharyya, Ye Yu, Hanyu Yang et al.

The success of OpenAI's ChatGPT in 2023 has spurred financial enterprises into exploring Generative AI applications to reduce costs or drive revenue within different lines of businesses in the Financial Industry. While these applications offer strong potential for efficiencies, they introduce new model risks, primarily hallucinations and toxicity. As highly regulated entities, financial enterprises (primarily large US banks) are obligated to enhance their model risk framework with additional testing and controls to ensure safe deployment of such applications. This paper outlines the key aspects for model risk management of generative AI model with a special emphasis on additional practices required in model validation.

Wang Qian

Current humanistic design of urban public spaces focuses on specific design elements while ignoring the conflicts and couplings between multiple user needs. This leads to spatial strategies stuck in local optima and lacking overall balance and adaptability. This paper constructs a multi-objective optimization model that integrates user preferences, multidimensional spatial indicators, and behavioral simulation. This model collects field data such as heat maps, path trajectories, and dwell time, identifies user types through K-means clustering, and models their spatial preferences using fuzzy membership functions. Design variables are set in Grasshopper; an optimization function is constructed; the optimal solution is searched using NSGA-III. Finally, pedestrian simulation is performed in AnyLogic, and the optimization results are corrected for function deviation to improve the coordination and adaptability of the design. Experimental results show that this strategy framework significantly improves spatial coordination, increasing weighted average satisfaction from 0.61 to 0.81 (+32.8%), reducing safety risks by 30.8% to 63.2%, and increasing interaction promotion by 71.2%. Multi-dimensional indicators verify the effectiveness of the optimization strategy in balancing user needs, alleviating local conflicts, and enhancing spatial adaptability, providing a quantitative basis and practical path for systematically solving the local optimal problem of humanized design of public spaces.

Alexander Yu. Nikiforov, Nikolai A. Usachev, Alexander V. Ermakov

V. Ya. Tsvetkov

The development of society is accompanied by an increase in the complexity of management objects and management mechanisms. To counteract the growth of complexity, new management models and methods should be introduced. New methods include semasiological management which uses a model approach and induction principle. It borrows the ideas of semasiology from linguistics and forms management decisions on the basis of application of information management units. Despite the fact that this complicates the preliminary process of preparing for management, it also gives an advantage in the comparability of different management decisions and technologies. Semasiological management allows, when reconfiguring management, not to create management models anew, but to modernise them by replacing management information units or forming new combinations of these units. Semasiological management is related to onomasiological information modeling and requires its use. In addition, it can be used in automated management, smart management, and digital twin management. Semasiological management requires special organisation and specific training, such as a special management language. The research proposes a variant of semasiological management which is based on the application of the theory of information units.

Kapil Panda

Portfolio optimization is a task that investors use to determine the best allocations for their investments, and fund managers implement computational models to help guide their decisions. While one of the most common portfolio optimization models in the industry is the Markowitz Model, practitioners recognize limitations in its framework that lead to suboptimal out-of-sample performance and unrealistic allocations. In this study, I refine the Markowitz Model by incorporating machine learning to improve portfolio performance. By using a hierarchical clustering-based approach, I am able to enhance portfolio performance on a risk-adjusted basis compared to the Markowitz Model, across various market factors.

Pablo Durán Gómez, Fernando Echevarría Camarero, Ana Ogando-Martínez et al.

The decreasing costs of solar photovoltaic (PV) technology have led to an exponential growth in the use of PV self-consumption systems. This development has encouraged the consideration of battery energy storage systems (BESS) as a potential means of achieving even more independence from the fluctuating grid electricity prices. As PV technology and energy storage costs continue to decline, both technologies will likely play an increasingly important role in the renewable energy sector. The profitability of batteries in PV self-consumption systems is largely influenced by the price of consumed electricity and the price at which surplus energy is remunerated. However, strategies in PV-BESS self-consumption systems typically do not take electricity prices into consideration as a variable for decision making. This study simulates and analyzes battery operation strategies that take into account electricity prices. The simulations are performed using real industrial consumption data and real electricity prices and tariffs, they cover the entire lifespan of the batteries, and include aging and degradation due to use and cycling. A techno-economic model is used to evaluate the advantages of incorporating these battery operational strategies into an actual PV-BESS system. The results demonstrate that the proposed strategies enhance the savings that batteries can provide.

K Pradeep Reddy, Venkateswarlu Chandu, Sambhana Srilakshmi et al.

In today’s commercial world, ecological concerns have become increasingly essential. A lot of governments care about environmental issues. Sustainable development that doesn’t harm the environment is a major concern for companies today. The term “green marketing” describes the strategy of promoting and selling goods and services because of their positive impact on the natural world. Either the product or service itself is environmentally friendly, or the manufacturing process, packaging, and marketing are modified to be more eco-friendly. Concerns about how products harm the environment have recently been expressed by both manufacturers and consumers. Lead-free paint, organic foods, and low-power (or “energy-efficient”) electrical equipment are examples of products that consumers and manufacturers are focusing on as being “green” or ecologically friendly. Additionally, the importance of the green marketing idea is becoming more and more apparent to marketers. Though numerous green marketing studies have been conducted globally, there hasn’t been much academic research on consumer perception and preferences in India. This study explores consumer green ideals, environmental knowledge, green behaviours, and green products in addition to providing a brief review of environmental challenges. This article emphasises consumer views of and favorites for green marketing tactics and goods through the use of a planned questionnaire. To study was lead on 702 respondents. Customers demonstrated a high level of knowledge about eco marketing tactics and products. The respondents also showed strong environmental values. Research has offered helpful insights for green product marketers owing to the great perceived eco cost amongst customers, and it emphasises essential for creating marketing communication campaigns promoting green products.

Navaneethakrishna Makaram, Sarvagya Gupta, Matthew Pesce et al.

In drug-resistant epilepsy, a visual inspection of intracranial electroencephalography (iEEG) signals is often needed to localize the epileptogenic zone (EZ) and guide neurosurgery. The visual assessment of iEEG time-frequency (TF) images is an alternative to signal inspection, but subtle variations may escape the human eye. Here, we propose a deep learning-based metric of visual complexity to interpret TF images extracted from iEEG data and aim to assess its ability to identify the EZ in the brain. We analyzed interictal iEEG data from 1928 contacts recorded from 20 children with drug-resistant epilepsy who became seizure-free after neurosurgery. We localized each iEEG contact in the MRI, created TF images (1–70 Hz) for each contact, and used a pre-trained VGG16 network to measure their visual complexity by extracting unsupervised activation energy (UAE) from 13 convolutional layers. We identified points of interest in the brain using the UAE values via patient- and layer-specific thresholds (based on extreme value distribution) and using a support vector machine classifier. Results show that contacts inside the seizure onset zone exhibit lower UAE than outside, with larger differences in deep layers (L10, L12, and L13: <i>p</i> < 0.001). Furthermore, the points of interest identified using the support vector machine, localized the EZ with 7 mm accuracy. In conclusion, we presented a pre-surgical computerized tool that facilitates the EZ localization in the patient’s MRI without requiring long-term iEEG inspection.

Efe C. Balta, Mohammad H. Mamduhi, John Lygeros et al.

In this paper, we consider a cyber-physical manufacturing system (CPMS) scenario containing physical components (robots, sensors, and actuators), operating in a digitally connected, constrained environment to perform industrial tasks. The CPMS has a centralized control plane with digital twins (DTs) of the physical resources, computational resources, and a network manager that allocates network resources. Existing approaches for allocation of network resources are typically fixed with respect to controller-dependent run-time specifications, which may impact the performance of physical processes. We propose a dynamic network management framework, where the network resource allocation schemes are controller-aware. The information about the controllers of the physical resources is implemented at the DT level, and metrics, such as regret bounds, take the process performance measures into account. The proposed network management schemes optimize physical system performance by balancing the shared resources between the physical assets on the plant floor, and by considering their control requirements, providing a new perspective for dynamic resource allocation. A simulation study is provided to illustrate the performance of the proposed network management approaches and compare their efficiencies.

Xuan V. Nguyen, Devi D. Nelakurti, Engin Dikici et al.

<b>Background</b>: Distinguishing between the spinal cord and cerebrospinal fluid (CSF) non-invasively on CT is challenging due to their similar mass densities. We hypothesize that patch-based machine learning applied to dual-energy CT can accurately distinguish CSF from neural or other tissues based on the center voxel and neighboring voxels. <b>Methods</b>: 88 regions of interest (ROIs) from 12 patients’ dual-energy (100 and 140 kVp) lumbar spine CT exams were manually labeled by a neuroradiologist as one of 4 major tissue types (water, fat, bone, and nonspecific soft tissue). Four-class classifier convolutional neural networks were trained, validated, and tested on thousands of nonoverlapping patches extracted from 82 ROIs among 11 CT exams, with each patch representing pixel values (at low and high energies) of small, rectangular, 3D CT volumes. Different patch sizes were evaluated, ranging from 3 × 3 × 3 × 2 to 7 × 7 × 7 × 2. A final ensemble model incorporating all patch sizes was tested on patches extracted from six ROIs in a holdout patient. <b>Results</b>: Individual models showed overall test accuracies ranging from 99.8% for 3 × 3 × 3 × 2 patches (N = 19,423) to 98.1% for 7 × 7 × 7 × 2 patches (N = 1298). The final ensemble model showed 99.4% test classification accuracy, with sensitivities and specificities of 90% and 99.6%, respectively, for the water class and 98.6% and 100% for the soft tissue class. <b>Conclusions</b>: Convolutional neural networks utilizing local low-level features on dual-energy spine CT can yield accurate tissue classification and enhance the visualization of intraspinal neural tissue.

Fuli Zhou, Yandong He, Felix T. S. Chan et al.

With the increasing demand of individual customption and awareness of cost reduction in express delivery organizations, the Chinese express industry faced with serious challenges especially under the background of government’s strict restrictions on environment and transportation. Therefore, a new service mode called joint distruction (JD) is being tried by the logistics industry, which is expected to address the challenges on online shopping. However, the insufficient understanding of JD adoption factors and their complicated interactions blocks the effectively implementation of the joint distribution. This study aims at identifying potential factors for JD adoption and promoting an effective joint distribution by discovering the interactive relationships among addressed factors. Firstly, potential ingredients for the adoption and implementation of JD are summarized from the literature and industrial interviews. Then, 23 variables are selected and classified into as objectives, drivers, barriers and affected operations. The Interpretive Structural Modeling (ISM) approach is then employed to analyze the crucial factors and the mutual influences amongst 23 variables. Finally, a case study is performed to construct the hierarchical structure of factors toward joint distribution adoption using the proposed ISM-modeling steps. The perplex hierarchical co-relationships are also identified by categorizing the driving variables and dependent variables. Results can assist express enterprises to promote the novel joint distribution mode and acheive higher efficiency of logistics operation by better understanding on crucial factors of JD adoption and implementation.

Jongsung Lee, Seung-Kweon Hong

This study measured the time it took to select a target moving along a circular trajectory with a computer mouse. The time was changed according to the speed of the target, the width of target and the distance from the starting point to the target. However, the effect of these independent variables on the dependent variable was different from what was expected. In the previous studies, it was assumed that the faster the moving target speed, the longer the target selection time, because increased target speed had the effect of narrowing the effective target width. However, as a result of the experiment, the target selection time was rather shortened when the moving speed of the target was increased. This may be because the subjects intend to speed up target selection while decreasing the accuracy of target selection in order to adapt to a fast-moving target. The modified Fitts’ model for the moving target selection time proposed in a previous study did not take these user responses into account. A more modified model is required to more accurately describe the selection time of moving target.

Mao Guan, Xiao-Yang Liu

Deep reinforcement learning (DRL) has been widely studied in the portfolio management task. However, it is challenging to understand a DRL-based trading strategy because of the black-box nature of deep neural networks. In this paper, we propose an empirical approach to explain the strategies of DRL agents for the portfolio management task. First, we use a linear model in hindsight as the reference model, which finds the best portfolio weights by assuming knowing actual stock returns in foresight. In particular, we use the coefficients of a linear model in hindsight as the reference feature weights. Secondly, for DRL agents, we use integrated gradients to define the feature weights, which are the coefficients between reward and features under a linear regression model. Thirdly, we study the prediction power in two cases, single-step prediction and multi-step prediction. In particular, we quantify the prediction power by calculating the linear correlations between the feature weights of a DRL agent and the reference feature weights, and similarly for machine learning methods. Finally, we evaluate a portfolio management task on Dow Jones 30 constituent stocks during 01/01/2009 to 09/01/2021. Our approach empirically reveals that a DRL agent exhibits a stronger multi-step prediction power than machine learning methods.

Sareh Nabi, Houssam Nassif, Joseph Hong et al.

Adding domain knowledge to a learning system is known to improve results. In multi-parameter Bayesian frameworks, such knowledge is incorporated as a prior. On the other hand, various model parameters can have different learning rates in real-world problems, especially with skewed data. Two often-faced challenges in Operation Management and Management Science applications are the absence of informative priors, and the inability to control parameter learning rates. In this study, we propose a hierarchical Empirical Bayes approach that addresses both challenges, and that can generalize to any Bayesian framework. Our method learns empirical meta-priors from the data itself and uses them to decouple the learning rates of first-order and second-order features (or any other given feature grouping) in a Generalized Linear Model. As the first-order features are likely to have a more pronounced effect on the outcome, focusing on learning first-order weights first is likely to improve performance and convergence time. Our Empirical Bayes method clamps features in each group together and uses the deployed model's observed data to empirically compute a hierarchical prior in hindsight. We report theoretical results for the unbiasedness, strong consistency, and optimal frequentist cumulative regret properties of our meta-prior variance estimator. We apply our method to a standard supervised learning optimization problem, as well as an online combinatorial optimization problem in a contextual bandit setting implemented in an Amazon production system. Both during simulations and live experiments, our method shows marked improvements, especially in cases of small traffic. Our findings are promising, as optimizing over sparse data is often a challenge.

Flavio Angelini, Katia Colaneri, Stefano Herzel et al.

We study the optimal asset allocation problem for a fund manager whose compensation depends on the performance of her portfolio with respect to a benchmark. The objective of the manager is to maximise the expected utility of her final wealth. The manager observes the prices but not the values of the market price of risk that drives the expected returns. The estimates of the market price of risk get more precise as more observations are available. We formulate the problem as an optimization under partial information. The particular structure of the incentives makes the objective function not concave. We solve the problem via the martingale method and, with a concavification procedure, we obtain the optimal wealth and the investment strategy. A numerical example shows the effect of learning on the optimal strategy.

Ziming Gao, Yuan Gao, Yi Hu et al.

Machine Learning algorithms and Neural Networks are widely applied to many different areas such as stock market prediction, face recognition and population analysis. This paper will introduce a strategy based on the classic Deep Reinforcement Learning algorithm, Deep Q-Network, for portfolio management in stock market. It is a type of deep neural network which is optimized by Q Learning. To make the DQN adapt to financial market, we first discretize the action space which is defined as the weight of portfolio in different assets so that portfolio management becomes a problem that Deep Q-Network can solve. Next, we combine the Convolutional Neural Network and dueling Q-net to enhance the recognition ability of the algorithm. Experimentally, we chose five lowrelevant American stocks to test the model. The result demonstrates that the DQN based strategy outperforms the ten other traditional strategies. The profit of DQN algorithm is 30% more than the profit of other strategies. Moreover, the Sharpe ratio associated with Max Drawdown demonstrates that the risk of policy made with DQN is the lowest.

Rasoul Yarahmadi, Hamed Moridi, Ali Asghar Farshad et al.

Abstract: Aim and Background: Today, with the growth of different dimensions of sustainable development, managers at the organizational and governmental levels have become more and more interested in the components of sustainable development. A healthy and productive human being is at the core of sustainable development. Many factors are contributing to sustainable development, including human, economic, social, industrial, cultural, as well as health, safety, environmental and energy (HSEE) factors. In this regard, the existence of several processing indicators is important in promoting the program and organizational goals at the micro and macro levels. Since continuous evaluation and monitoring of all indicators is not feasible, developing a set of principles to facilitate managerial decision-making processes and monitoring continuous improvement of systems is an important issue in system performance management. Due to the vital role of correct selection of principles in the sustainability of the integrated management system, it is important to consider the key components involved in this choice. The present study aimed to prioritize the HSEE processing indexes in the integrated management system (HSEE IMS) of the Ministry of Industry, Mining and Trade. Methodology: The statistical population of this study is professionals and experts with occupational health, safety, environment, and energy orientation and work experience. In this study, to obtain process, safety, health, environmental and energy processing indicators, firstly, a list of environmental, safety, health, and energy indicators was prepared and evaluated, by using a set of indicators presented in scientific and credible research and articles, the Iranian Environmental Agency, HSE Ministry of Oil, HSEE Ministry of Industry, Mine and Trade, including mining and industrial organizations, including Industrial Development and Renovation Organization of Iran (IDERO), Iranian Mines and Mining Industries Development and Renovation Organization (IMIDRO) and other sources. In the present study, being SMART is the main selection criterion for indices, which are weighted as the five main affecting criteria by the AHP method. Weighted criteria were used to prioritize the eight principles of HSEE management including policy, continuous improvement, do, check, monitoring, and measurement of the system, commitment and leadership, planning and corrective action using the fuzzy TOPSIS technique. Results: The results show that the executive strategic index with a closeness coefficient of 0.937 was selected as the first priority. Continuous improvement and corrective action with the coefficient of closeness of 0.133 and 0.108 were selected from the weaker priorities of the Eight HSEE indices, respectively. Conclusion: Appropriate selection of indicators to facilitate managerial decision-making processes, optimal monitoring of these indicators with maximum efficiency and minimum cost is possible by using multi-criteria decision-making models. Based on the results, sustainable development can be achievable by ranking and prioritizing the HSEE processing indicators to facilitate managerial decision-making processes and monitor continuous improvement of systems to protect individuals, property and reduce accidents and pollution. Keywords: Prioritization, HSEE, Sustainable Development, Fuzzy TOPSIS, Index, Weighting, AHP. Introduction Preventing health, safety, the environment, and energy injuries and accidents by taking into account the health, safety of employees, customers, contractors and others requires a unified management system structure. This system tries to create a healthy, pleasant and joyful environment free from accident, damage and waste by integration and synergy of human resources and facilities. The present study aims to key and prioritize strategic principles in the HSEE integrated management system of the Ministry of Industry, Mine and Trade to evaluate the performance based on specific processes in subsidiary industrial-productive units. Methodology In this study, to obtain safety, health, environmental, and energy processing indexes, firstly, a list of environmental, safety, health, and energy indicators was prepared and evaluated, by using a set of indicators presented in scientific and credible research and articles, the Iranian Environmental Agency, HSE Ministry of Oil, HSEE Ministry of Industry, Mine and Trade, including mining and industrial organizations, including Industrial Development and Renovation Organization of Iran (IDERO), Iranian Mines and Mining Industries Development and Renovation Organization (IMIDRO) and other sources. Then, the SMART metrics including specificity, measurability, achievability, realism and being timely have weighted as five effective criteria by the AHP. After keying the HSEE strategic indicators of the Ministry of Industry, Mining and Trade, including: policy, continuous improvement, do, check, system monitoring, commitment and leadership, planning and corrective action were selected. Initial questionnaire was prepared based on the fuzzy TOPSIS method and key indicators and research criteria. Then, the reliability (internal consistency) and validity of the questionnaire were assessed and finalized. After completing the questionnaires and receiving the information, the expertschr('39') answers in the form of verbal statements were transformed into triangular fuzzy numbers with the capability of analysis. In the present study, to obtain effective indicators for identification and evaluation of key indicators, the five SMART criteria were weighted based on the AHP method. After weighting the research criteria using the AHP method, this ratio is used for weighting the key indexes by the fuzzy TOPSIS method to rank and prioritize. Results The results show that the face validity and content validity of the questionnaire were determined by FVR = 78.08% and CVR = 88%, respectively which have acceptable validity based on Lawshe’s model. The reliability of the research questionnaire was estimated by the appropriate Cronbachchr('39')s alpha equivalent in 0.935, illustrating the intrinsic homogeneity of the evaluated indices. Regarding weighting results, research criteria, weighting criteria and indices are presented in Tables 1-2.   Table1. Final weight of criteria by the AHP method Realism Achievability Timely Specificity Measurability Criterion 0.300 0.203 0.135 0.231 0.341 Final Weight     Table 2. Closeness coefficient and rank of HSEE processing indicators of Ministry of Industry, Mine and Trade Ranking CCi Criterion Commitment and Leadership 0.491 3 Policy 0.403 4 Planning 0.226 5 Do 0.937 1 Monitoring 0.699 2 Check 0.193 6 Corrective Action 0.108 8 Continuous Improvment 0.133 7   Conclusion Appropriate selection of indicators to facilitate managerial decision-making processes, optimal monitoring of these indicators with maximum efficiency and minimum cost is possible using multi-criteria decision-making models. This study aimed to weight, key and prioritize HSEE process indicators for the first time in Iran at the level of the largest executive-economic system. According to the results of the study, due to the high speed and efficiency of HSEE units in subsidiary organizations, do index (CCi = 0.937) was first priority and continuous improvement (CCi = 0.133) and corrective actions (CCi = 0.108) were found as the weakest priorities of the HSEE eight indicators because of weaknesses in the regular and systematic follow-up of regulatory units or lack of appropriate tools to evaluate these indicators. The results of this study showed an interesting convergence between the weight and prioritization of SMART criteria of strategic indicators of Ministry of Industry, Mine and Trade. This conclusion can help managers select key performance indicators based on SMART criteria and help them choose sustainability indicators that prevent wasting time and cost.

Nicole Bäuerle, Tomer Shushi

We generalize Quasi-Linear Means by restricting to the tail of the risk distribution and show that this can be a useful quantity in risk management since it comprises in its general form the Value at Risk, the Tail Value at Risk and the Entropic Risk Measure in a unified way. We then investigate the fundamental properties of the proposed measure and show its unique features and implications in the risk measurement process. Furthermore, we derive formulas for truncated elliptical models of losses and provide formulas for selected members of such models.