The direct impacts of long wait times in elections, such as lost wages for voters and suppressed turnout, are well-documented. Drawing upon the service operations literature, we hypothesize that such operational inefficiencies may have far-reaching consequences beyond the immediate voter experience, in particular, the spread of political misinformation. Using a novel dataset that combines granular measures of voter wait times from cellphone location data, social media content, and demographic information at the county level, we find evidence that longer wait times are associated with greater sharing of political fake news on Reddit in the aftermath of the 2016 US presidential election. Importantly, this relationship exhibits significant heterogeneity based on the racial composition of counties, with more diverse counties experiencing greater increases in misinformation spread due to wait times. To shed light on the mechanisms underlying this link, we leverage individual-level survey data on voters’ polling place experiences and perceptions of electoral integrity. Our analysis reveals that experiencing long wait times erodes voters’ confidence in the integrity of the election process, particularly at the local level. These findings underscore the societal importance of efficient election operations management and highlight how they can have profound implications for the health of democracy. Our work introduces a new factor—the operational efficiency of election administration—into the study of political misinformation, thus opening up avenues for operations management research to contribute to a pressing social challenge.
This paper presents a novel disturbance-torque-estimation-augmented model predictive control (MPC) framework to perform momentum management on NASA's Solar Cruiser solar sail mission. Solar Cruiser represents a critical step in the advancement of large-scale solar sail technology and includes the innovative use of an active mass translator (AMT) and reflectivity control devices (RCDs) as momentum management actuators. The coupled nature of these actuators has proven challenging in the development of a robust momentum management controller. Recent literature has explored the use of MPC for solar sail momentum management with promising results, although exact knowledge of the disturbance torques acting on the solar sail was required. This paper amends this issue through the use of a Kalman filter to provide real-time estimation of unmodeled disturbance torques. Furthermore, the dynamics model used in this paper incorporates key fidelity enhancements compared to prior work, including Solar Cruiser's four-reaction-wheel assembly and the offset between its center of mass and center of pressure. More realistic operation scenarios involving the tracking of large angle slew maneuvers under attitude-dependent solar radiation force and torque are also performed to further validate the proposed method compared to prior work. Simulation results demonstrate that the proposed policy successfully manages angular momentum growth under slew maneuvers that exceed the operational envelope of the current state-of-the-art method. The inclusion of the disturbance torque estimate is shown to greatly improve the reliability and performance of the proposed MPC approach. This work establishes a new benchmark for Solar Cruiser's momentum management capabilities and paves the way for MPC-based momentum management of other solar sails making use of an AMT and/or RCDs.
In the post-pandemic era, multi-sourcing is rapidly becoming the preferred approach for companies to drive optimized cost, quality and turnaround times. However, multi-sourcing systems present a myriad of challenges in designing an effective planning model. In this article, we propose a robust capacity planning model with multiple supply sources, and demonstrate how the capacity plan can be efficiently solved via a parsimonious mean–variance approach. We apply the max-min criterion to design a distributionally robust multi-source capacity plan. We use a new approach to deriving a set of optimality conditions on the robust capacity plan. These conditions enable us to derive the worst distribution, the optimal robust capacity vector, and the worst-case expected profit in closed form. The new approach bypasses numerous tedious intermediate procedures in the traditional distributionally robust optimization literature and allows us to derive the optimal solution based on exogenous cost parameters. This closed-form solution appears to be hitherto unknown and has important ramifications for the multi-sourcing capacity planning problem. Our findings reveal that, despite the complexity of multi-sourcing, the worst-case demand scenario can be reduced to just n + 1 distinct outcomes, structured by two key sequences derived from supplier characteristics. The optimal capacity plan has a clear structure–allocations align with the midpoints between these demand scenarios. Surprisingly, we also find that most of the robustness benefits of a full supplier portfolio can be achieved by engaging just the two best-matched sources. This provides a practical and cost-effective road map for robust capacity planning in multi-sourcing environments.
Prior to the Internet, many industries catering to mass audiences, particularly entertainment industries, tended to have very skewed sales distributions with most sales volume accruing to a small share of products (titles) generally known by terms such as “blockbusters.” The long tail hypothesis claims that increased Internet usage will weaken the dominance of blockbusters in these industries, so that the large number of obscure products, in aggregate, will become an important, perhaps primary, portion of the market. Such a change would have significant implications for firms’ operations. We test this hypothesis for the book industry with two analyses. First, using unusually rich data on book sales, we examine the temporal share of sales taken by niche titles as Internet retailing grew from obscurity to ubiquity. Changes in the distribution of sales by title popularity are weakly consistent with the direction of the hypothesis but not with the magnitude. Second, we exploit the 2011 bankruptcy of the Borders bookstore chain, and the resulting sudden shutdown of all its physical stores as a natural experiment. This analysis also finds a shift in sales away from the most successful titles. But the major beneficiaries are not the most obscure titles but instead titles that would regularly be found in most bookstores. Both analyses support a version of the long tail where the share of obscure titles changes from one very small value to a somewhat greater but still very small value. These results are inconsistent with proposed radical changes in management practices to accommodate a presumed large increase in product variety.
To mitigate supply and demand uncertainties, firms often design highly flexible production networks. This study investigates process flexibility in production systems subject to supply disruptions and stochastic demands with differentiated profit margins. In particular, we model supply disruptions as failures of arcs in the network, with node-based disruptions treated as a special case in which all arcs connected to a node simultaneously fail. To address the cost implications of such disruptions, we investigate the design of process flexibility under a robust optimization framework. We first develop a greedy algorithm under deterministic demand to efficiently evaluate the worst-case disruption scenario, and demonstrate the significant advantage of the alternate long-chain design under such disruptions. Subsequently, under stochastic demand, by introducing the marginal profit group index under disruption (MPGID), we characterize the worst-case total profit as a function of both the flexibility design and demand uncertainty, modeled through a partwise independently symmetric perturbation set. This representation enables direct performance comparisons across different flexibility configurations under disruption risk. For the case involving two products with distinct profit margins under supply disruption risk, we demonstrate that the alternate long-chain design outperforms all other long-chain configurations in terms of worst-case profitability. In addition, in certain cases, arc-based disruptions can be just as devastating as plant-node disruptions, particularly when they lead to the loss of high-margin demand. However, our fragility analysis reveals that this design becomes increasingly vulnerable as disruption risks intensify. To address this issue, we propose an MPGID-based heuristic that systematically generates flexible designs to mitigate both supply and demand uncertainties.
The advent of Industry 4.0 has introduced transformative opportunities for the SME sector, particularly in enhancing operational efficiency, fostering sustainability, and enabling digital connectivity within manufacturing processes. This study examines key barriers faced by SMEs in adopting Industry 4.0 technologies, including high initial investments, skills gaps in digital competencies, and logistical limitations in production infrastructure. It explores a collaborative platform model that facilitates resource sharing among SMEs to reduce individual costs, optimize technology access, and improve competency development. Findings highlight that while shared platforms can successfully mitigate certain financial and operational barriers, the scalability of such models faces limitations due to stakeholder interests and infrastructure constraints. This research provides insights into the critical role of technological advancements and sustainable practices in supporting SMEs’ Industry 4.0 transition and emphasizes the need for adaptive strategies to maintain integration viability as production demands evolve.
This study empirically investigates the relationship between shared-goal congruence, customer relationship management (CRM), and customer relationship advantages on firm performance. Data were collected from 200 supply chain practitioners and operations staff working in retail stores in Indonesia using purposive and non-probability sampling techniques. The hypotheses were assessed using SEM-AMOS 24, after conducting Exploratory Factor Analysis, reliability, and validity tests. The results show that CRM significantly impacts firm performance, mediated by Green Supply Chain Management (GSCM). Additionally, a positive relationship exists between goal congruency and firm performance, also mediated by GSCM. Supplier relationship management was found to have a significant effect on both GSCM and firm performance. The theoretical contribution of this study emphasizes that the integration of sustainable principles in supply chain management through GSCM improves firm performance in various aspects, including financial, environmental, and social. The implementation of GSCM, supported by strong management commitment and a shift in organizational culture, enhances energy and resource efficiency, process and product innovation, environmental risk reduction, customer satisfaction, continuous improvement, and cost reductions, leading to competitive advantages through faster and more accurate decision-making.
Production management. Operations management, Management. Industrial management
The rise of artificial intelligence and data science across industries underscores the pressing need for effective management and governance of machine learning (ML) models. Traditional approaches to ML models management often involve disparate storage systems and lack standardized methodologies for versioning, audit, and re-use. Inspired by data lake concepts, this paper develops the concept of ML Model Lake as a centralized management framework for datasets, codes, and models within organizations environments. We provide an in-depth exploration of the Model Lake concept, delineating its architectural foundations, key components, operational benefits, and practical challenges. We discuss the transformative potential of adopting a Model Lake approach, such as enhanced model lifecycle management, discovery, audit, and reusability. Furthermore, we illustrate a real-world application of Model Lake and its transformative impact on data, code and model management practices.
Ioannis Kontogiorgakis, Iason Tsardanidis, Dimitrios Bormpoudakis
et al.
Effective weed management is crucial for improving agricultural productivity, as weeds compete with crops for vital resources like nutrients and water. Accurate maps of weed management methods are essential for policymakers to assess farmer practices, evaluate impacts on vegetation health, biodiversity, and climate, as well as ensure compliance with policies and subsidies. However, monitoring weed management methods is challenging as they commonly rely on ground-based field surveys, which are often costly, time-consuming and subject to delays. In order to tackle this problem, we leverage earth observation data and Machine Learning (ML). Specifically, we developed separate ML models using Sentinel-2 and PlanetScope satellite time series data, respectively, to classify four distinct weed management methods (Mowing, Tillage, Chemical-spraying, and No practice) in orchards. The findings demonstrate the potential of ML-driven remote sensing to enhance the efficiency and accuracy of weed management mapping in orchards.
A key challenge facing the use of machine learning (ML) in organizational selection settings (e.g., the processing of loan or job applications) is the potential bias against (racial and gender) minorities. To address this challenge, a rich literature of Fairness-Aware ML (FAML) algorithms has emerged, attempting to ameliorate biases while maintaining the predictive accuracy of ML algorithms. Almost all existing FAML algorithms define their optimization goals according to a selection task, meaning that ML outputs are assumed to be the final selection outcome. In practice, though, ML outputs are rarely used as-is. In personnel selection, for example, ML often serves a support role to human resource managers, allowing them to more easily exclude unqualified applicants. This effectively assigns to ML a screening rather than a selection task. It might be tempting to treat selection and screening as two variations of the same task that differ only quantitatively on the admission rate. This paper, however, reveals a qualitative difference between the two in terms of fairness. Specifically, we demonstrate through conceptual development and mathematical analysis that miscategorizing a screening task as a selection one could not only degrade final selection quality but also result in fairness problems such as selection biases within the minority group. After validating our findings with experimental studies on simulated and real-world data, we discuss several business and policy implications, highlighting the need for firms and policymakers to properly categorize the task assigned to ML in assessing and correcting algorithmic biases.
M. Victoria Bitar, Silvina M. Cabrini, Hernán A. Urcola
This study fills important gaps in research by analyzing the evolution over time of productive, environmental, and socio-economic aspects of agricultural production in the Argentine Pampas, utilizing farm-level data. A longitudinal study was conducted to examine the changes that occurred in farming systems during the period 2007–2018. The study evaluated the changes in 30 farms, examining modifications in the structure and management of each farm, as well as in productive, economic, and environmental performance. Canonical correlation analysis was used to relate the changes that occurred in performance to farms' characteristics at the beginning of the study period. The results indicated that, among the farms that stayed in business, there were no significant changes in land tenure and the amount of labor employed. There was a significant increase in the average age of farmers by 7 years, along with a decrease in the percentage of farmers expecting growth, dropping from 70% to 42% over the period. Canonical correlation analysis revealed that smaller farms, with a higher number of workers at the beginning of the period, were more likely to expand their farming area during the analysis period. The findings also indicate a substantial turnover of producers, with leaving farms being succeeded by larger-scale operations. The yields of the main crops and the direct production costs increased by 16% and 48% respectively, during the period. The environmental indicators for the main crops present a mixed picture: soil organic carbon input increased by 12%, while environmental impact quotient decreased on average, by 6% for cereals but increased by 40% for soybeans, and nutrient imbalances rose. The significance of this study resides in its application of a comprehensive approach to analyze the transformation of farming systems over time.
Strategy management analyses are created by business consultants with common analysis frameworks (i.e. comparative analyses) and associated diagrams. We show these can be largely constructed using LLMs, starting with the extraction of insights from data, organization of those insights according to a strategy management framework, and then depiction in the typical strategy management diagram for that framework (static textual visualizations). We discuss caveats and future directions to generalize for broader uses.
Traditional identity management systems, often centralized, face challenges around privacy, data security, and user control, leaving users vulnerable to data breaches and misuse. This paper explores the potential of using the Arweave network to develop an identity management solution. By harnessing Arweave's permanent storage, our solution offers the users a Self-Sovereign Identity (SSI) framework, that uses Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) to allow individuals and other entities to create, own, and manage their digital identities. Further, the solution integrates privacy-preserving technologies, including zero-knowledge proofs and the BBS(+) signature scheme, enabling selective disclosure. This approach ultimately enhances user privacy and supports compliance with European Union legislation and regulatory standards like the General Data Protection Regulation (GDPR) by design.
The workforce ageing phenomenon is recently affecting most of the Organisation for Economic Co-operation and Development (OECD) member countries, due to a general ageing of their populations and a higher average retirement age of the workforce. In this paper, the topic of ageing workforce management is addressed from a production research standpoint, with the aim of understanding how older workers can be supported and involved in a manufacturing system. First, the current state of the art related to the ageing workforce in production systems is presented. This is structured according to four main topics: (1) analysis and evaluation of ageing workers’ functional capacities, (2) consideration of ageing workers’ capacities in industrial system modelling and management, (3) analysis and exploitation of ageing workers’ expertise, (4) acknowledgement, analysis, design and integration of supporting technologies. Next, the discussion on the impact of the ageing workforce on manufacturing systems’ performances leads to the comparison of some technological advances that are related to the Industry 4.0 paradigms. Finally, a future research agenda on this topic is proposed, based on the same topics classification proposed for the literature analysis. Five different research areas are derived, suggesting future directions for appropriate research concerning the employ of older workers in production environments.
Abstract Hydrogen production is the key in utilizing an excess renewable energy. Many studies and projects looked at the energy management systems (EMSs) that allow to couple hydrogen production with renewable generation. In the majority of these studies, however, hydrogen demand is either produced for powering fuel cells or sold to the external hydrogen market. Hydrogen demand from actual industrial plants is rarely considered. In this paper, we propose an EMS based on the industrial cluster of GreenLab Skive (GLS) that can minimize the system’s operational cost or maximize its green hydrogen production. EMS utilizes a conventional and P2X demand response (DR) flexibility from electrolysis plant, hydrogen storage tank, electric battery, and hydrogen-consuming plants to design the optimal schedule with maximized benefits. A potential addition to the existing components at GLS - an ammonia plant is modelled to identify its P2X potential and assess the economic viability of its construction. The results show a potential reduction of 51.5–61.6% for the total operational cost of the system and an increase of the share of green hydrogen by 10.4–37.6% due to EMS operation.
In this paper, an algorithm for energy management system (EMS) based on multi-layer ant colony optimization (EMS-MACO) is presented to find energy scheduling in Microgrid (MG). The aim of study is to figure out the optimum operation of micro-sources for decreasing the electricity production cost by hourly day-ahead and real time scheduling. The proposed algorithm is based on ant colony optimization (ACO) method and is able to analyze the technical and economic time dependent constraints. This algorithm attempts to meet the required load demand with minimum energy cost in a local energy market (LEM) structure. Performance of MACO is compared with modified conventional EMS (MCEMS) and particle swarm optimization (PSO) based EMS. Analysis of obtained results demonstrates that the system performance is improved also the energy cost is reduced about 20% and 5% by applying MACO in comparison with MCEMS and PSO, respectively. Furthermore, the plug and play capability in real time applications is investigated by using different scenarios and the system adequate performance is validated experimentally too.
S. Chapaloglou, Athanasios Nesiadis, Petros Iliadis
et al.
Abstract In this study, a novel algorithm for the management of the power flows of an islanded power system was developed, capable of simultaneously achieving steadier conventional unit operation and shaving the demand peak values, for the days of the year that present a night peak in their load curve. The under investigation system is composed of Diesel Generators, a PV farm and a Battery Energy Storage System (BESS) with the power system’s consumption to be relatively higher than its RES production. The proposed algorithm combines the use of a load forecasting methodology, a pattern recognition procedure and a custom optimal power flow scheduling algorithm. The prediction module was based on a feedforward artificial neural network, capable of short-term day ahead load forecasting. The forecasted day ahead load profile was then used as an input to the developed pattern recognition algorithm, in order to be classified based on its load curve shape (pattern). Subsequently, in case that the classification resulted in a clear night peak pattern, it was possible to estimate an hourly based trajectory for the diesel generators operation and derive the BESS charging setpoints, which result in the desired peak shaving and smoothing level simultaneously. In this way, it is possible to replace or substitute the highest power demand with stored renewable energy and to operate the diesel engines as steady as possible, diminishing the ramp up and the steep gradients before the night hours’ peak. The algorithm was integrated in the overall system model in APROS software, where dynamic simulations were performed. The simulation results proved that by applying the proposed algorithm, a combined effect of smoother diesel generator operation and peak shaving with renewable energy is achievable even with the absence of PV overproduction, diminishing the variability of the load to be covered from the conventional units. Such an operation aims at enabling diesel engines to be rated at a lower, than currently, maximum capacity while increasing the share of the renewable energy penetration into the grid.
Abstract Application of sustainability principles to mining is inherently challenging, as mining is the act of removing and consuming a limited resource. However, consideration of sustainability – meeting present needs without compromising needs of future generations – is increasingly being incorporated into mine development and operation as demand for minerals and products of mining such as metals and fuel and non-fuel minerals and the environmental impacts associated with minerals extraction activities continue to increase. This paper provides a review of existing research literature and thought on sustainability in mining of non-fuel minerals. A common sustainable mining framework is focused on reducing environmental impacts of mining. Strategies for assessing the sustainability of mining operations include measuring, monitoring, and working to improve various environmental performance metrics, and these are used to determine whether a mining operation is sustainable. The key metrics for environmental sustainability in mining relate to efficiencies in resource consumption, minimizing land disturbance, pollution reduction, as well as closure and reclamation of exhausted mine lands. Another sustainable mining framework transitions from the emphasis on the environmental footprint of mining operations to responsible management of non-fuel mineral resources throughout their entire life cycle, including use phase and end of life, with attendant implications for reducing the quantity of mined material and preserving reserves for future generations. This paper examines the transition to a broader context that includes the entire mineral life cycle. We propose that assessment of sustainability in mining should encompass a systems view of mined materials in society, emphasizing existing environmental sustainability metrics from mining operations as well as including “circularity” or “life cycle” metrics to assess the sustainability of production and extraction for the long term.
Abstract Thermal conductivity and thermal dissipation are of great importance for modern electronics due to the increased transistor density and operation frequency of contemporary integrated circuits. Due to its exceptionally high thermal conductivity, graphene has drawn considerable interests worldwide for heat spreading and dissipation. However, maintaining high thermal conductivity in graphene laminates (the basic technological unit) is a significant technological challenge. Aiming at highly thermal conductive graphene films (GFs), this prospective review outlines the most recent progress in the production of GFs originated from graphene oxide due to its great convenience in film processing. Additionally, we also consider such issues as film assembly, defect repair and mechanical compression during the post-treatment. We also discuss the thermal conductivity in in-plane and through-plane direction and mechanical properties of GFs. Further, the current typical applications of GFs are presented in thermal management. Finally, perspectives are given for future work on GFs for thermal management.