Operative Management Research Articles

On-the-go table grape ripeness estimation via proximal snapshot hyperspectral imaging

The monitoring of grapes for ripeness estimation is a practice that enables fruit harvesting at the optimal time. Hyperspectral Imaging (HSI) represents a non-destructive and high-throughput alternative to traditional laboratory analyses. Current literature approaches perform hyperspectral measurements using line scan sensors or low-resolution static snapshot cameras, which hinder a fast per-bunch ripeness characterization. We propose a framework for on-the-go collection and processing of proximal snapshot hyperspectral images to estimate single bunch ripeness parameters. Focusing on table grapes (Vitis vinifera L. cv. Red Globe), we collected images under natural illumination with a hyperspectral camera (500–900 nm) mounted on a moving vehicle in an experimental block sited in Piacenza, Italy. We investigated images collected in August and September 2021 representing two ripening stages. The composition of the imaged grape bunches was determined through laboratory chemical analyses to predict Total Soluble Solids (TSS) and anthocyanin concentration. The images were pre-processed via multimodal image registration to correct the unalignment of bands due to the vehicle motion, and the single bunches were automatically identified on false RGB images through a Mask Region-Convolutional Neural Network (Mask R-CNN) instance segmentation network. The mean spectra of the bunches were used as input features of a Partial Least Squares Regression (PLSR) model to predict the chemical parameters at single bunch and whole vine scales. The regression model of TSS had an R2 (10-fold nested cross-validation) of 0.75 and 0.85 on a per-bunch and per-vine basis, respectively. The regression model of anthocyanin had an R2 of 0.68 and 0.49 on a per-bunch and per-vine basis, respectively. The results suggest the potential of using snapshot hyperspectral images for high-throughput analysis of a per-bunch grape ripeness estimation. The method described in this study could give valuable information to improve grape ripening monitoring and management of harvest operations and even allow for precise and automated robotic harvesting.

Integrated water resource management in the Segura Hydrographic Basin: An artificial intelligence approach

Managing resources effectively in uncertain demand, variable availability, and complex governance policies is a significant challenge. This paper presents a paradigmatic framework for addressing these issues in water management scenarios by integrating advanced physical modelling, remote sensing techniques, and Artificial Intelligence algorithms. The proposed approach accurately predicts water availability, estimates demand, and optimizes resource allocation on both short- and long-term basis, combining a comprehensive hydrological model, agronomic crop models for precise demand estimation, and Mixed-Integer Linear Programming for efficient resource distribution. In the study case of the Segura Hydrographic Basin, the approach successfully allocated approximately 642 million cubic meters (hm3) of water over six months, minimizing the deficit to 9.7% of the total estimated demand. The methodology demonstrated significant environmental benefits, reducing CO2 emissions while optimizing resource distribution. This robust solution supports informed decision-making processes, ensuring sustainable water management across diverse contexts. The generalizability of this approach allows its adaptation to other basins, contributing to improved governance and policy implementation on a broader scale. Ultimately, the methodology has been validated and integrated into the operational water management practices in the Segura Hydrographic Basin in Spain.

Heat transfer study on a stator-permanent magnet electric motor: A hybrid estimation model for real-time temperature monitoring and predictive maintenance

When electrical machines operate without a specific cooling system, the surrounding environment plays a crucial role in the rise of temperature and the duty cycle of operation. More clearly, a natural convection cooling system with a low value of heat transfer coefficient carries the risk of thermal breakdown, insufficient safety, and reliability. This paper studies the heat transfer aspects of a low-power flux switching permanent magnet (FSPM) motor under natural convection cooling to implement a novel real-time sensor-less temperature monitoring system. Thermal and electromagnetic experiments are carried out to create foundations for transient and steady-state numerical models. A data-driven, deep learning algorithm estimates the core and permanent magnet (PM) eddy current losses in real-time, besides the already available copper and friction losses. Subsequently, a two-node thermal equivalent circuit in a hybrid model with a feed-forward neural network estimates the dynamic temperature profile of windings and PMs. It is indicated that the worst-case estimation error is below 7.5%, and the configuration is applicable under a wide range of operation states and environmental conditions. Lastly, the system, including the power source, FSPM motor, and hybrid temperature estimation unit, will be implemented in MATLAB/Simulink to investigate the fault prediction and operation management capabilities.

Symptom Resolution After Operative Management of Complications From Transvaginal Mesh: Notice of Update.

Symptom Resolution After Operative Management of Complications From Transvaginal Mesh: Notice of Update.

Developing a cross-functional team coordination framework: A model for optimizing business operations

In today's complex business environment, effective cross-functional team coordination is crucial for optimizing operations and achieving organizational goals. This study introduces a comprehensive framework designed to enhance cross-functional team coordination through structured operational management strategies. By integrating key principles of collaboration, communication, and accountability, the framework aims to bridge departmental silos and promote a culture of teamwork. The proposed model is structured around three core components: communication channels, collaborative tools, and performance metrics. First, it emphasizes the establishment of robust communication channels that facilitate real-time information sharing and feedback among team members from diverse functions. This fosters transparency and aligns objectives, ensuring that all team members are on the same page. Second, the framework advocates for the implementation of collaborative tools that streamline project management and task allocation. These tools enable team members to track progress, manage workloads, and share resources effectively, thereby enhancing overall productivity. By utilizing technology that supports remote collaboration, organizations can also adapt to the increasing trend of virtual teams, ensuring that geographical barriers do not hinder coordination. Finally, the model incorporates performance metrics that allow teams to evaluate their effectiveness and make data-driven decisions. By establishing clear key performance indicators (KPIs) related to team outcomes, organizations can continuously monitor progress and identify areas for improvement. This iterative approach not only optimizes current operations but also fosters a culture of continuous improvement within cross-functional teams. The framework is designed for applicability across various industries, demonstrating its versatility in addressing the unique challenges faced by organizations. By adopting this structured approach to cross-functional coordination, businesses can enhance their operational efficiency, reduce project delays, and improve overall team morale. This study contributes to the existing literature on operational management by providing a practical model that organizations can implement to optimize their business operations through improved cross-functional teamwork.

A data-driven operations management model: Implementing MIS for strategic decision making in tech businesses

In today’s rapidly evolving technological landscape, data-driven decision-making is critical for business growth and competitiveness. This paper presents a comprehensive operations management model that integrates Management Information Systems (MIS) to facilitate strategic decision-making in technology firms. The proposed model emphasizes the transformation of raw data into actionable insights, enabling organizations to optimize their operations and respond effectively to market dynamics. By systematically incorporating MIS into operational frameworks, the model provides a structured approach to data collection, analysis, and dissemination. It identifies key performance indicators (KPIs) that are essential for monitoring business performance and aligning operational activities with strategic objectives. Through case studies of leading tech companies, the model illustrates how data-driven methodologies can lead to improved forecasting, resource allocation, and overall operational efficiency. The framework is designed to be adaptable, allowing technology firms to customize the MIS components based on their unique business needs and market conditions. Key features of the model include real-time data analytics, predictive modeling, and user-friendly dashboards that enhance visibility across departments. These components empower decision-makers with timely and relevant information, fostering a culture of informed decision-making at all organizational levels. Moreover, the model addresses common challenges faced by tech businesses, such as data silos and integration issues. By promoting a centralized data repository and standardized reporting mechanisms, it enhances collaboration among teams and ensures consistency in data interpretation. The implications of this model extend beyond operational efficiency, influencing strategic initiatives such as product development, market expansion, and customer engagement. In conclusion, this data-driven operations management model not only supports strategic decision-making in technology firms but also positions them for sustainable growth in a competitive marketplace. By leveraging MIS effectively, organizations can harness the power of data to drive innovation and enhance overall business performance.

Designing circular supply chains with digital technologies for competitive sustainability: An operation management perspective

The objective of this research is to investigate how digital technologies, specifically blockchain, artificial intelligence (AI), and the Internet of Things (IoT), can be integrated into circular supply chains to enhance sustainability and preserve a competitive advantage. The goals of circular supply chains, which are founded on the ideas of the circular economy, are to reduce waste, promote resource regeneration, and lessen their negative effects on the environment. The study highlights the critical role that digital technologies play in guaranteeing transparency, promoting real-time monitoring, and optimizing resource distribution. Blockchain offers traceability and trust, IoT boosts visibility and operational efficiency within supply chains, and AI improves demand forecasting and resource efficiency. Though the benefits are obvious, there are significant obstacles to overcome, including high implementation costs, complex technological issues, scalability issues, and regulatory barriers. The research demonstrates how these digital technologies can effectively promote sustainability while enhancing operational efficiency through case studies of well-known companies like HP, Walmart, and Philips. The report also highlights important future trends and makes suggestions for companies looking to develop their workforces, invest in digital technologies, and work together on circular economy platforms. In the end, digital tool adoption is essential for creating supply chains that are more sustainable and competitive in the modern global economy.

Translational science and related disciplines.

Translational research moves scientific discoveries and innovations across the development spectrum for a particular target or disease, trying to bridge in a multidisciplinary fashion the gap between laboratory scientific discoveries and practical, real-world applications in medicine and in healthcare. Translational research aims to move research findings across settings, specific languages, methodologies, and study designs, from laboratory to clinical practice and ultimately into community- and population-level health benefits. In contrast, translational science is a distinct field, which evolved over time toward a systematic study and practice of operationalizing the translation of content from one language, ecosystem, environment, contextual landscape, culture, discipline, area, or domain into another. It involves systematic and transdisciplinary integration of knowledge from basic science, clinical research and population science to improve human health, better longevity, and to ensure disease- and disability-free lives. Translational science often uses knowledge, operational frameworks, and specific capabilities borrowed from other specialties, disciplines, and fields such as operations management, implementation and dissemination science, quality improvement and management, project management, public health, intervention science, change management and leadership, decision science, design thinking, functional design, data science, communication and marketing science, etc. The main goal of this article is to open a series of thematic reviews in this journal, introducing the reader to the main definitions, contingencies, touchpoints, and overlapping areas between translational science and these related specialties, disciplines, and fields of study. Transdisciplinary capabilities borrowing from these related specialties can create a robust translational science machinery for health systems, research organizations, and innovation hubs.

Research on safety control technology of high-speed railway combined test based on threatening event analysis

PurposeSafety management is a key point and poses a challenge in joint testing. To detect and address potential accidents' hidden dangers early, this paper conducts research on the safety control technology for high-speed railway joint tests by incorporating the concept of hazardous events.Design/methodology/approachAiming at ensuring the safety of high-speed railway combined inspections and trials, this paper starts from the dual prevention mechanism. It introduces the concept of threatening events, defines them and analyzes the differences between threatening events and railway accidents. The paper also proposes a cause model for threatening events in high-speed railway combined inspections and trials, based on three types of hazard sources. Furthermore, it conducts research on the control strategies for these threatening events.FindingsThe research on safety control technology for high-speed railway combined operation and testing, based on the analysis of threatened events, offers a new perspective for safety management in these operations. It also provides theoretical and practical support for the transition from passive prevention to active risk pre-control, which holds significant theoretical and practical value.Originality/valueThe innovation mainly includes the following three aspects: (1) Building on the traditional dual prevention mechanism, which includes risk hierarchical management and control as well as hidden danger investigation and management, a triple prevention mechanism is proposed. This new mechanism adds the management of threatening events as the third line of defense. The aim is to more comprehensively identify and address potential security risks, thereby enhancing the efficiency and effectiveness of security management. (2) In this paper, the definition of a railway threatening event is clarified, and the causative model of a high-speed railway threatening event based on three kinds of danger sources is proposed. (3) This paper puts forward the control strategy of the high-speed railway combined operation and trial, which includes five key links: identification, reporting, analysis, rectification and feedback, which provides a new perspective for the safety management of the high-speed railway combined operation and trial and has important theoretical and application value.

Green supply chain management: a bibliometric analysis of global research trends and future directions

ABSTRACT This study presents a comprehensive bibliometric analysis of GreenSupply Chain Management (GSCM), examining its evolution andprospective directions across strategic, operational, and economicdimensions. Drawing upon organizational theory, stakeholder theory,operations management, environmental management, accounting, andeconomics, the study applies bibliometric techniques, including wordanalysis and network analysis, to discern significant researchtrends. It emphasizes the necessity of aligning strategic objectiveswith environmental sustainability and implementing operationalmeasures to achieve environmental goals while also analyzing theeconomic implications of sustainable practices. Furthermore, thestudy highlights the long-term financial advantages of thesepractices and the potential contributions of emerging technologies,such as blockchain, the Internet of Things, and ArtificialIntelligence, to the enhancement of GSCM.

Exact Simulation of Quadratic Intensity Models

We develop efficient algorithms of exact simulation for quadratic stochastic intensity models that have become increasingly popular for modeling events arrivals, especially in economics, finance, and insurance. They have huge potential to be applied to many other areas such as operations management, queueing science, biostatistics, and epidemiology. Our algorithms are developed by the principle of exact distributional decomposition, which lies in a fully analytical expression for the joint Laplace transform of quadratic process and its integral newly derived in this paper. They do not involve any numerical Laplace inversion, have been validated by extensive numerical experiments, and substantially outperform all existing alternatives in the literature. Moreover, our algorithms are extendable to multidimensional point processes and beyond Cox processes to additionally incorporate two-sided random jumps with arbitrarily distributed sizes in the intensity for capturing self-exciting and self-correcting effects in event arrivals. Applications to portfolio loss modeling are provided to demonstrate the applicability and flexibility of our algorithms. History: Accepted by Bruno Tuffin, Area Editor for Simulation. Funding: This work was supported by the Beijing University of Posts and Telecommunications [Grant 2022RC58], the Shanghai University of Finance and Economics [Grant 2020110930], the National Natural Science Foundation of China [Grant 71401147], and Graduate Innovation Fund of Shanghai University of Finance and Economics [CXJJ-2023-387]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0323 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0323 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Comment on “Salesforce Compensation with Inventory Considerations”: A Rejoinder

In this rejoinder, we address the concern about equilibrium existence in our previous work published in 2013. By making the simple adjustment of discretizing inventory ordering amounts to prevent inventory orders in arbitrarily small increments, we show that the results from that paper hold, thus maintaining the robustness of the insights obtained. This paper was accepted by Jeannette Song, operations management.

Smart Cities and Education in the Digital Economy New Research Agenda for Operations Management in the Digital Economy

Smart Cities represent a transformative approach to urbanization, leveraging technology to enhance sustainability, efficiency, and quality of life for inhabitants. By integrating advanced data analytics, Io T (Internet of Things) devices, and interconnected infrastructure, Smart Cities aim to enhance urban living through integrated technology, promoting economic growth, sustainability, and community engagement to optimize resource utilization and enhance urban mobility, foster economic growth, and promote citizen engagement. This abstract captures the essence of how technology can revolutionize urban living, enhancing the responsiveness of cities, resilient, and adaptable to the needs of their residents.

Intelligent E-commerce under the Trend of Integration of Artificial Intelligence and E-commerce: A Case Study of Alibaba International Station

Abstract: China has fully entered the era of digital economy, and undoubtedly, the e-commerce industry needs to keep pace with the times and evolve into a new stage of development: integrating artificial intelligence with e-commerce. Nowadays, an abundance of new productivity tools have unveiled new opportunities for the e-commerce industry. This study focuses on Alibaba International, a premier global digital trading platform, to illustrate the applications of artificial intelligence tools in e-commerce, examine both the positive and negative impacts of these tools on e-commerce, and suggest strategies for enhancing e-commerce Operation Management. The aim is to offer enlightenments for small and medium-sized cross-border e-commerce businesses. Through this case analysis, it can be understood that intelligent e-commerce has provided many conveniences for the e-commerce industry, but also faces a series of challenges, such as AI generated false or infringing content, and increased operational management difficulties. In this situation, e-commerce companies should take strengthening the cultivation of intelligent talents as a guarantee, focusing on customer experience and valuing personalized product needs as advantages, and improving operational management efficiency through the reasonable use of favorable policies to bring profits to the enterprise.

Applying FMEA for Operational Risk Management: Lessons from Minestar Implementation at PT. Borneo Indobara

PT Borneo Indobara (BIB) is planning to implement Minestar, an integrated mining management system, in 2025 to enhance operational efficiency and safety. However, the implementation of this system poses several operational risks that require careful management to ensure success. The aim of this research is to develop a comprehensive risk matrix and propose effective mitigation strategies for the Minestar implementation at BIB, using the SNI ISO 31000:2018 risk management framework. The research employs a mixed-method approach, combining qualitative techniques such as semi-structured interviews and focus group discussions (FGD) with quantitative analysis using Failure Mode and Effect Analysis (FMEA). Data was collected from key stakeholders directly involved in the Minestar project to assess the severity, occurrence, and detection of each identified risk, which were then used to develop a risk matrix. The results of the study identified and prioritized several critical operational risks, with a three-dimensional risk matrix providing a clear visualization of their severity, occurrence, and detection levels. This structured approach allows BIB's management to prioritize risks and allocate resources effectively for risk mitigation. The findings of this research contribute to operational risk management in the mining industry, particularly for the implementation of advanced technologies like Minestar. The practical insights provided can serve as a reference for similar projects in the industry, helping organizations to address operational challenges proactively.

A Study on Enterprise Risk Management in the Chinese Context

Abstract: The establishment of the market economy, accompanied by the emergence of risks, requires enterprises to consider not only the economy and profitability but also the risk. Therefore, enterprise risk management problems arise. Many developed countries see risk management, strategic management, and operation management as the three main management functions of modern economic organizations, which will become a critical factor in the competitiveness of businesses in the future. Therefore, the paper reviews the development of traditional risk management theory, financial risk management theory, and internal control theory, with a focus on Enterprise Risk Management (ERM). After analyzing the current situation of ERM in Chinese enterprises, the paper proposes suggestions for improving risk management methods, strengthening the internal control system, and enhancing staff quality. Finally, the paper emphasizes the importance of Enterprise Risk Management (ERM) development in China and suggests the need for a risk management system tailored to the countrys specific circumstances.

The Impact of ESG on Corporate Performance: A Case Study of Yunnan Baiyao

Abstract: With the deepening integration of the global economy and the widespread acceptance of sustainable development principles, Environmental, Social, and Governance (ESG) factors have become critical elements influencing long-term value and financial performance of enterprises. Globally, an increasing number of companies are paying attention to ESG factors and integrating them into their strategic planning and operational management. This paper employs case study analysis and accounting-related indicators analysis to evaluate the ESG practices of selected enterprises from the dimensions of environment, society, and governance. It then combines financial indicators to analyze the impact of ESG on their financial performance and the reasons behind it. Finally, suggestions are made on how to achieve sustainable development through improved ESG management.

Identification of Critical Nodes for Delay Propagation in Susceptible-Exposed-Infected-Recovered (SEIR) and Genetic Algorithm (GA) Route Networks

In response to the challenges associated with forecasting the trajectory of flight delay propagation, pinpointing pivotal nodes within the route network, and the substantial costs involved in enhancing operational efficiency, this study introduces an innovative approach to identifying critical nodes that influence delay propagation across route networks. The methodology commences by establishing a route network model for East China, leveraging the principles of complex network theory. It then incorporates the SEIR (Susceptible-Exposed-Infected-Recovered) model, typically used for analyzing the dynamics of infectious disease spread, to examine the propagation of delays between routes. Subsequently, the approach employs a GA to identify key nodes, which are then compared against those identified by network topology indices. The simulation outcomes demonstrate that the GA’s identification of key nodes offers superior insights into the overall network’s susceptibility to infection, thereby presenting operational managers with novel perspectives for analyzing the spread of flight delays.

Smart Factory Operation Management System and Innovation in the Era of Smart Manufacturing

This study is dedicated to the innovative research of smart factory operation management systems in the era of smart manufacturing. Against the backdrop of the rapid evolution of the information revolution, smart manufacturing, represented by a new generation of information technologies such as mobile Internet, big data, and artificial intelligence, is rapidly emerging. As an information-physical fusion system, smart factories interpenetrate physical and virtual resources by interweaving multi-scale production systems, forming an open, complex giant system with multiple levels and dimensions. It is found that smart factories face a complex and variable structure, and their production and operations involve a large number of cross-domain, cross-industry, and cross-region manufacturing resources, requiring the establishment of a new operation management system framework and breakthroughs in key theories and methods. This study provides comprehensive and in-depth theoretical and technical support for constructing a smart factory operation management system. It lays the foundation for the smart upgrading of the manufacturing industry.

Managerial ability and firm growth: intellectual capital components

PurposeThis study analyses the way various components of intellectual capital (IC), namely, human capital (HC), structural capital (SC), relational capital (RC) and innovation capital (INNC), act as mediators in the relationship between managerial ability (MA) and a firm’s ability to achieve growth.Design/methodology/approachThis study employs data envelopment analysis to quantify the MA of 825 Taiwanese listed electronics companies from 2017 to 2022. The proxies of firm growth are return on asset growth, operating income growth and total asset growth. This study then utilises a three-step mediation analysis methodology to examine the relationships between MA, IC and firm growth.FindingsFindings indicate that HC, SC, RC and INNC mediate the link between MA and firm growth. This suggests that competent managers can capitalise on the potential benefits of these investments to achieve firm growth.Practical implicationsCompetent managers can utilise different IC investments to grow the financial performance and strength of their businesses. Managers should continually scan, secure opportunities and adjust their investments in knowledge assets in accordance with the dynamic capabilities view. That is, managers, in general, and operations managers, in particular, can implement guidelines that prioritise IC investments in the future to expedite firms’ development.Originality/valueThis study extends the existing frameworks that study investment variables as mediators between MA and firm outcomes. Most particularly, this study adopts four components of IC for measurement. Moreover, firm performance is measured using dynamic growth indicators rather than static measures.