Hasil untuk "Production management. Operations management"

Martín Abadi, P. Barham, Jianmin Chen et al.

TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. Tensor-Flow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer: whereas in previous "parameter server" designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and demonstrate the compelling performance that TensorFlow achieves for several real-world applications.

W. Hopp, M. Spearman

R. Zhong, George Q. Huang, Shulin Lan et al.

Alireza Ghahtarani, Ahmed Saif, Alireza Ghasemi

Asset Liability Management (ALM) represents a fundamental challenge for financial institutions, particularly pension funds, which must navigate the tension between generating competitive investment returns and ensuring the solvency of long-term obligations. To address the limitations of traditional frameworks under uncertainty, this paper implements Distributionally Robust Optimization (DRO), an emergent paradigm that accounts for a broad spectrum of potential probability distributions. We propose and evaluate three distinct DRO formulations: mixture ambiguity sets with discrete scenarios, box ambiguity sets of discrete distribution functions, and Wasserstein metric ambiguity sets. Utilizing empirical data from the Canada Pension Plan (CPP), we conduct a comparative analysis of these models against traditional stochastic programming approaches. Our results demonstrate that DRO formulations, specifically those utilizing Wasserstein and box ambiguity sets, consistently outperform both mixture-based DRO and stochastic programming in terms of funding ratios and overall fund returns. These findings suggest that incorporating distributional robustness significantly enhances the resilience and performance of pension fund management strategies.

Bingzheng Chen, Jan Dhaene, Chun Liu et al.

This paper develops a dynamic equilibrium model of the insurance market that jointly characterizes insurers' underwriting, investment, recapitalization, and dividend policies under model uncertainty and financial frictions. Competitive insurers maximize shareholder value under a subjective worst-case probability measure, giving rise to liquidity-driven underwriting cycles and flight-to-quality behavior. While an equilibrium typically fails to exist in such dynamic liquidity management framework with external financial investment, we show that incorporating model uncertainty restores equilibrium existence under plausible parameter conditions. Moreover, the model uncovers a novel relationship between the correlation of insurance and financial market risks and the equilibrium insurance price: negative loadings may emerge when insurance gains and financial returns are positively correlated, contrary to conventional intuition.

J. Cochran, L. Cox

A. Majumdar, S. Sinha

Abstract Southeast Asian countries have become the production hub of lean textile and apparel supply chain. Textile supply chain consumes huge amount of natural resources and emits polluting effluents and gases creating serious environmental and human health concerns. Green design, green procurement, green operations and green transportation are the major areas of green supply chain management. This paper attempts to analyze the important barriers of green textile and apparel supply chain management in Southeast Asian countries. Twelve important barriers have been identified through literature review and questionnaire survey. Interpretive structural modeling (ISM) has been used to decipher the contextual relationships among the barriers. Complexity of green process and system design was found to be the most elementary barrier having the maximum driving power. Lack of consumer support and encouragement, lack of guidance and support from regulatory authorities and high implementation and maintenance cost are the other elementary barriers of green textile supply chain. Lack of green suppliers is the most dependent barrier which is influenced by all other barriers considered in this research. Elimination of root causes or driving barriers are paramount to save the environment. Concerted efforts in terms of green technological innovation, consumers’ awareness and support of the regulatory bodies are needed for effective implementation of green supply practices in textile and apparel supply chains.

Yuanyao Ye, H. Ngo, Wenshan Guo et al.

The growing global population's demand for ammonium has triggered an increase in its supply, given that ammonium plays a crucial role in fertilizer production for the purpose of food security. Currently, ammonia used in fertilizer production is put through what is known as the industrial Haber Bosch process, but this approach is substantially expensive and requires much energy. For this reason, looking for effective methods to recover ammonium is important for environmental sustainability. One of the greatest opportunities for ammonium recovery occurs in wastewater treatment plants due to wastewater containing a large quantity of ammonium ions. The comprehensively and critically review studies on ammonium recovery conducted, have the potential to be applied in current wastewater treatment operations. Technologies and their ammonium recovery mechanisms are included in this review. Furthermore the economic feasibility of such processes is analysed. Possible future directions for ammonium recovery from wastewater are suggested.

Lixiran Yu, Hongfei Tao, Qiao Li et al.

Irrigation areas in arid regions are vital production areas for grain and cash crops worldwide. Grasping the temporal and spatial evolution of planting configurations across several years is crucial for effective regional agricultural and resource management. In view of problems such as insufficient optical images caused by cloudy weather in arid regions and the unclear spatiotemporal evolution patterns of the planting structures in irrigation areas over the years, in this study, we took the Santun River Irrigation Area, a typical arid region in Xinjiang, China, as an example. By leveraging long time-series remote sensing images from Sentinel-1 and Sentinel-2, the spectral, index, texture, and polarization features of the ground objects in the study area were extracted. When analyzing the index characteristics, we considered several widely used global vegetation indices, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), and Global Environment Monitoring Index (GEMI). Additionally, we integrated the vertical–vertical and vertical–horizontal polarization data obtained from synthetic aperture radar (SAR) satellite systems. Machine learning algorithms, including the random forest algorithm (RF), Classification and Regression Trees (CART), and Support Vector Machines (SVM), were employed for planting structure classification. The optimal classification model selected was subjected to inter-annual transfer to obtain the planting structures over multiple years. The research findings are as follows: (1) The RF classification algorithm outperforms CART and SVM algorithms in terms of classification accuracy, achieving an overall accuracy (OA) of 0.84 and a kappa coefficient of 0.805. (2) The cropland area classified by the RF algorithm exhibited a high degree of consistency with statistical yearbook data (R² = 0.82–0.91). Significant differences are observed in the estimated planting areas of cotton, maize, tomatoes, and wheat, while differences in other crops are not statistically significant. (3) From 2019 to 2024, cotton remained the dominant crop, although its proportional area fluctuated considerably, while the areas of maize and wheat tended to remain stable, and those of tomato and melon showed relatively minor changes. Overall, the region demonstrates a cotton-dominated, stable cropping structure for other crops. The newly developed framework exhibits exceptional precision in categorization while maintaining impressive adaptability, offering crucial insights for optimizing agricultural operations and sustainable resource allocation in irrigation-dependent arid zones.

Md. Zahidul Anam, Md. Hasibul Islam, Md. Tamzidul Islam et al.

The adoption of digital technologies in agriculture offers opportunities for efficiency and sustainability, particularly in emerging economies with resource and infrastructure constraints. However, challenges persist, exacerbated by crises such as COVID-19 and geopolitical instabilities, highlighting agricultural supply chains’ fragility. Industry 5.0-driven digital transformation (DT) can mitigate these challenges by enhancing food security, supply chain resilience, and environmental sustainability. This study identifies and analyzes key drivers of DT in agricultural production from a holistic perspective. Through a literature review and expert validation, 19 key drivers were identified in the context of Bangladesh. An integrated multi-criteria decision-making (MCDM) approach, combining the Fermatean fuzzy sets (FFS) with the decision-making trial and evaluation laboratory (DEMATEL) technique, was applied to examine the drivers and explore interrelations among them. The results indicate that the most influential drivers are ’commitment from regulatory bodies’, ’maximizing the use of dwindling resources’, ’fostering rural development’, and ’the need for safe food’, with prominence values of 4.175, 4.001, 3.999, and 3.888, respectively. Additionally, ’commitment from regulatory bodies’ emerges as the most impactful causal factor, having a causal weight of 1.848. These findings provide insights for policymakers and industry managers in emerging economies, supporting strategic decision-making to drive sustainable agricultural transformation and achieve the relevant sustainable development goals.

Christopher Neil Makanga, Laura Aseru Orobia, Twaha Kigongo Kaawaase et al.

PurposeThis study aimed to examine the relationship between ethical work climate and accountability in public sector organizations. The study was undertaken in municipal local governments due to their importance in the provision of public services to communities, yet they continue to face public accountability challenges, especially in developing countries.Design/methodology/approachThe study applied a quantitative research methodology, collecting data using a structured questionnaire. Data were obtained from 521 respondents in 88 municipal local governments in Uganda. The data was analyzed first using Statistical Package for Social Scientists to ascertain correlations and later transferred to the smart partial least squares (PLS) analysis tool to test study hypotheses using PLS structural equation modeling.FindingsStudy results revealed a positive and significant relationship between ethical work climate and its three conceptualized factors namely, caring, law and code and rules and public accountability. The implication is that an ethical work climate that values the provisions of laws, codes and organizational rules, along with responsible staff, is likely to uphold public accountability. The study recommended that public managers need to take into consideration the role of each ethical work climate factor, to enhance and sustain public accountability. This includes factors such as nurturing the caring staff attitude, implementing the requirements of laws and codes to which the organization and its staff subscribe, as well as the requirements of internal organizational rules.Research limitations/implicationsThe current study has some limitations that can provide a foundation for further research. The study focused only on municipal local governments, excluding other types of public sector entities. It may be found necessary to carry out related studies in other types of public sector entities. The study applied a quantitative research design to investigate the association between ethical work climate and public accountability. Whereas the methodology was found suitable by the researchers, it is also probable that other studies can be undertaken applying a qualitative or a mixed methods approach.Practical implicationsFrom the practical perspective, the study has shown that an ethical work climate has the potential to influence accountable behavior at the individual staff level and consequently at the overall organizational level. Each of the three dimensions of ethical work climate examined in the study, which are caring, law and code and rules, positively and significantly contributes to ethical behavior and accountability in public sector institutions. The culmination of all these factors provides an ethical work climate within which organizational members are expected to conduct themselves as they perform their roles to enable government institutions to be accountable to the public.Social implicationsIt is paramount for managers in public sector entities to ensure that: (1) laws, regulations and codes that directly affect their public organizations are adequately reviewed to ensure that employees are fully informed of their requirements and trained for compliance, (2) key ethical requirements on staff behavior are well elaborated within the internal policies and guidelines and widely circulated within the public organization and all staff sensitized on how to adhere to such requirements, (3) penalties for non-compliance with laws, regulations and internal policies are clearly spelled out in the internal policies, manuals and guidelines.Originality/valueThe study makes a distinct contribution to explaining public accountability in public sector organizations with a focus on municipal local governments in developing countries, to complement existing literature that has majorly been from developed countries.

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.

Kewin Pączek, Damian Jelito, Marcin Pitera et al.

This paper explores the applications of the 20/60/20 rule-a heuristic method that segments data into top-performing, average-performing, and underperforming groups-in mathematical finance. We review the statistical foundations of this rule and demonstrate its usefulness in risk management and portfolio optimization. Our study highlights three key applications. First, we apply the rule to stock market data, showing that it enables effective population clustering. Second, we introduce a novel, easy-to-implement method for extracting heavy-tail characteristics in risk management. Third, we integrate spatial reasoning based on the 20/60/20 rule into portfolio optimization, enhancing robustness and improving performance. To support our findings, we develop a new measure for quantifying tail heaviness and employ conditional statistics to reconstruct the unconditional distribution from the core data segment. This reconstructed distribution is tested on real financial data to evaluate whether the 20/60/20 segmentation effectively balances capturing extreme risks with maintaining the stability of central returns. Our results offer insights into financial data behavior under heavy-tailed conditions and demonstrate the potential of the 20/60/20 rule as a complementary tool for decision-making in finance.

Fatemeh Karimi, Jalal Haghighat Monfared, Mohammadali Keramati

Introduction: Supply chain disruption is an event that disrupts the production of goods and services. Resilience refers to the ability of an organization to manage disruptions or the ability of the supply chain network to quickly return to its previous state, ultimately positively impacting the company's performance. Many companies cannot maintain productivity during disruptions, losing competitiveness, increasing business continuity risk, and incurring financial losses. Sustainability considerations in supply chain operations have become a key issue. A common concept in sustainability is the triple approach: economic, environmental, and social, which must be observed by supply chain members. Sustainable supply chain management development is not a limiting factor but an approach to improve performance.Methods: This applied research study was conducted using a mixed qualitative-quantitative analysis with a cross-sectional survey method. The qualitative sample included academic and industry experts, while the quantitative sample comprised managers, heads, and experts in the studied company's headquarters, operations, and projects. Data collection tools included documentary studies, expert surveys, and a researcher-made questionnaire. Factors were identified using the meta-synthesis technique, screened with the fuzzy Delphi technique, and validated with partial least squares. The SWARA method was used for weighting and ranking factors. Supply chain processes were defined based on the SCOR model and ranked using the WASPAS method. The thinking process tools identified limitations in the third-level bottleneck process, and improvement solutions were presented.Results and Discussion: The meta-synthesis method extracted the desired indicators, which were screened and localized using the fuzzy Delphi technique and confirmed by experts in 7 dimensions and 39 indicators. The initial model was validated with partial least squares. Among resilience and sustainability factors, the "Risk Management" dimension with a weight of 0.2241 and the "Considering the risk factor in decision-making" index with a weight of 0.1224 were the top priorities. It was concluded that risk management is crucial for business continuity and dynamism. Supply chain managers should facilitate their participation in identifying and controlling risks and opportunities while continually increasing their subordinates' knowledge and skills. Evaluations identified the "sourcing and supply process," "goods and logistics supply process," and "purchase planning" as the most critical bottleneck processes. The root of disruptions in the "purchase planning" process was found to be in the identification, estimation, and allocation of human, infrastructural, and financial resources. Conclusions: Practical suggestions for company managers and decision-makers include employing expert personnel in purchasing planning, drafting executive plans, using advanced tools for measurement, analysis, forecasting, resource allocation, identifying uncertainties, determining prerequisites, and managing main and support suppliers and changes, and reviewing and modifying the existing mechanism.

Liwei Deng, Tianfu Wang, Yan Zhao et al.

Portfolio management is an important yet challenging task in AI for FinTech, which aims to allocate investors' budgets among different assets to balance the risk and return of an investment. In this study, we propose a general Multi-objectIve framework with controLLable rIsk for pOrtfolio maNagement (MILLION), which consists of two main phases, i.e., return-related maximization and risk control. Specifically, in the return-related maximization phase, we introduce two auxiliary objectives, i.e., return rate prediction, and return rate ranking, combined with portfolio optimization to remit the overfitting problem and improve the generalization of the trained model to future markets. Subsequently, in the risk control phase, we propose two methods, i.e., portfolio interpolation and portfolio improvement, to achieve fine-grained risk control and fast risk adaption to a user-specified risk level. For the portfolio interpolation method, we theoretically prove that the risk can be perfectly controlled if the to-be-set risk level is in a proper interval. In addition, we also show that the return rate of the adjusted portfolio after portfolio interpolation is no less than that of the min-variance optimization, as long as the model in the reward maximization phase is effective. Furthermore, the portfolio improvement method can achieve greater return rates while keeping the same risk level compared to portfolio interpolation. Extensive experiments are conducted on three real-world datasets. The results demonstrate the effectiveness and efficiency of the proposed framework.

Martha M Asante, Gregory Epiphaniou, C. Maple et al.

Supply chains (SC) present performance bottlenecks that contribute to a high level of costs, infiltration of product quality, and impact productivity. Examples of such inhibitors include the bullwhip effect, new product lines, high inventory, and restrictive data flows. These bottlenecks can force manufacturers to source more raw materials and increase production significantly. Also, restrictive data flow in a complex global SC network generally slows down the movement of goods and services. The use of distributed ledger technologies (DLT) in SC management (SCM) demonstrates the potentials to reduce these bottlenecks through transparency, decentralization, and optimizations in data management. These technologies promise to enhance the trustworthiness of entities within the SC, ensure the accuracy of data-driven operations, and enable existing SCM processes to migrate from a linear to a fully circular economy. This article presents a comprehensive review of 111 articles published in the public domain in the use and efficacy of DLT in SC. It acts as a roadmap for current and future researchers who focus on SC security management to better understand the integration of digital technologies such as DLT. We clustered these articles using standard descriptors linked to trustworthiness, namely, immutability, transparency, traceability, and integrity.

D. Ivanov, S. Sethi, A. Dolgui et al.

Alicia Mas-Tur, S. Kraus, Mario Brandtner et al.

The Review of Managerial Science (RMS) is a leading international journal that publishes major advances related to business administration and management. The journal was launched in April 2007 and publishes eight issues per year (from 2021 onwards). The scope of RMS encompasses, but is not limited to, the functional areas of operations (such as production, operations management, and marketing), management (such as human resources management, strategic management, and organizational theory), information systems and their interrelations with capital markets (such as accounting, auditing, finance, and taxation), as well as questions of business strategy, entrepreneurship, innovation, and corporate governance. This study offers a bibliometric overview of the publication and citation structure of RMS from its inception in 2007 until 2020 in terms of topics, authors, institutions, and countries, thereby offering a comprehensive overview of the history of the journal so far. All the data for the study are from the Web of Science Core Collection database. To complement this analysis, VOSviewer software provides graphical analysis. The analysis is based on several bibliometric techniques such as co-citation analysis and bibliographic coupling.

D. Wu, D. Wu, Lipo Yang et al.

Abstract To determine how carbon emissions reduction affects supply chain operations and financing decisions, this paper examines a green supply chain, which consists of one manufacturer (playing the leading role) and one capital-constrained retailer; in this supply chain, bank financing and trade credit financing are viable. This research explores the retailer's optimal order quantity, the manufacturer's optimal wholesale price, the optimal level of carbon emissions (for both bank financing and trade credit financing), and the design of the contract to coordinate the supply chain. We find that the supply chain achieves a win-win outcome in terms of production quantity and emissions reduction when the manufacturer invests in emissions reduction. In addition, we find that a supply chain with a contract outperforms a non-contract supply chain in production quantity and emissions reduction. Furthermore, the effect is more remarkable when trade credit financing is viable.

Kawaljeet Kapoor, A. Bigdeli, Yogesh Kumar Dwivedi et al.

Disruption from the COVID-19 pandemic has caused major upheavals for manufacturing, and has severe implications for production networks, and the demand and supply chains underpinning manufacturing operations. This paper is the first of its kind to pull together research on both—the pandemic-related challenges and the management interventions in a manufacturing context. This systematic literature review reveals the frailty of supply chains and production networks in withstanding the pressures of lockdowns and other safety protocols, including product and workforce shortages. These, altogether, have led to closed facilities, reduced capacities, increased costs, and severe economic uncertainty for manufacturing businesses. In managing these challenges and stabilising their operations, manufacturers are urgently intervening by—investing in digital technologies, undertaking resource redistribution and repurposing, regionalizing and localizing, servitizing, and targeting policies that can help them survive in this altered economy. Based on holistic analysis of these challenges and interventions, this review proposes an extensive research agenda for future studies to pursue.