Omnichannel retailing has changed the way consumers search for, evaluate, and purchase products. At the same time, product returns remain a costly and persistent operational challenge. Conventional wisdom holds that adding sales channels, particularly combining physical and non-physical touchpoints, should improve information about product fit and reduce return rates. However, it is unclear how the shopping experience across multiple channels influences what consumers buy and whether they return those purchases. There is also no clear consensus on why return rates increase in some omnichannel contexts but not others. We address this knowledge gap by focusing on consumers’ multichannel experience (MX) rather than mere channel adoption, and distinguishing two types of MX: remote-only (those who have used only non-physical channels such as online and catalog) versus hybrid (those who have used both non-physical and physical channels). We empirically analyze over 292,000 transactions from a European furniture retailer operating showroom, online, and catalog channels. Using a series of fixed-effects regressions with Heckman selection to address potential channel-selection bias, a control function approach to account for endogeneity due to omitted variable bias associated with MX, and a parallel mediation analysis, we find that consumers with MX are significantly more likely to return products than their counterparts with only single-channel experience (i.e., showroom, online, or catalog). This elevated return propensity is largely driven by MX shoppers’ greater tendency to buy niche products and experience goods, which carry higher uncertainty and return risk. Moreover, consumers with remote-only MX exhibit higher return rates than those with hybrid MX, and this gap is especially pronounced among consumers living farther from a physical showroom store. We conduct multiple robustness checks to verify the results. We also replicate the main findings with a new transaction data set from a U.S. retailer (leveraging a natural experiment). Theoretically, our work challenges assumptions that multichannel access inherently lowers returns, highlighting how MX can increase return risk through its effect on purchase behavior. Managerially, our findings inform product–channel matching strategies and omnichannel design, and point to targeted interventions based on consumer type (remote-only vs. hybrid) and geography (distance to stores) to ameliorate returns.
Customized mystery bag/box products are examples of a new business model of combining probabilistic selling and customization in practice. We develop an analytical framework to study the economic value of customized mystery bag/box products and explore the interrelationship between probabilistic selling and customization. We define probabilistic selling and customization to be complements (substitutes) as that customization increases (decreases) the value of probabilistic selling. We find that probabilistic selling and customization may be either complements or substitutes, which depends on the customization level and the product marginal cost. As long as customization level is low and product marginal cost is high, they are generally complements. The complementarity benefit comes from the fact that customization can enhance probabilistic selling’s price improvement value when the customization level is sufficiently low. In addition, the complementarity benefit reaches the maximum when the product marginal cost is sufficiently large and the customization is at an intermediate level. We further show that probabilistic selling and customization may still be complements when the customization level is endogenously determined with investment cost, when the customization service is strategically chosen by consumers, when the inventory decision is optimized with demand uncertainty, or when customization is achieved by product assortment planning.
Neverauskienė Laima Okunevičiūtė, Sakorskaitė-Narkun Eglė, Stasiukynas Andrius
The significance of this study lies in assessing the resilience of the air transport sector to economic crises, which is crucial in today’s context where sustainable environmental development has become a new reality, and the development of aviation services is insufficient for achieving stable and resilient economic growth in the sector. The air transport sector significantly contributes to regional economic growth and the development of various other business sectors. This article evaluates the resilience of the air transport sector in the Baltic countries in the context of economic crises. The novelty of the research stems from the air transport sector undergoing entirely new and unexplored processes, significantly influenced by the COVID-19 pandemic and previous crises, such as the September 11 attacks and the Great Financial Crisis. There is a lack of a comprehensive evaluation model to assess the resilience of the air transport sector under economic instability. The article aims to assess the resilience of the air transport sector in the Baltic countries in the context of economic crises. The article describes external factors influencing the economic growth of the air transport sector. It examines the perspectives of different authors and institutions on the development of the air transport sector. The study analyses national and international documents, development plans, and investments related to enhancing the resilience of the air transport sector to economic crises. To assess the resilience of the air transport sector in the Baltic countries in the context of economic crises, an empirical study was conducted using the following methods: Pearson correlation coefficient method to evaluate the relationship between Revenue Passenger Kilometers (RPK) and Available Seat Kilometers (ASK) indicators of the sole national airline in the Baltic States, “Air Baltic”, which belongs to Latvia, crucial for ensuring comprehensive stability; a multi-criteria evaluation using Simple Additive Weighting (SAW) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods to assess the current state and recovery level of the Baltic air transport sector after the COVID-19 pandemic; and an exponential smoothing method to forecast the growth of the Baltic air transport sector from 2024 to 2027, providing a realistic scenario.
As omnichannel operations become increasingly important for meeting diverse customer expectations in retail, continuous innovation in service and business models is essential to maintain a competitive edge. While effective order fulfillment is key to omnichannel success, the manual picking process in physical stores, one of the major driver of fulfillment costs, still offers substantial opportunities for improvement. This article focuses on the crowdpicking model as an innovative approach to manage online order picking operations in physical stores by leveraging existing in-store customers, offering a business model with considerable potential. We explore the real-time assignment of orders to in-store customers using machine learning to identify effective assignment policies. These policies are combined with a task-decomposition strategy to reduce picking costs and enhance crowdpicker participation as a key resource. The proposed crowdpicking model and its real-time management framework are tested on real-world data. Our results show that a well-managed crowdpicking system can lower order picking costs by more than 20% and provide actionable insights for managers in designing such systems.
Media plays a crucial role in shaping public perception of firms, significantly impacting their operations and performance. Firms often attempt to influence media channels to showcase their climate action initiatives, aiming to enhance their public image, reputation, and stakeholder trust. While firms cannot directly control their media coverage, intentional or unintentional dissemination of their climate action efforts can thrust them into the media spotlight. The implications of this media spotlight remain uncertain. While firms may anticipate positive public relations benefits from such exposure, it may also raise environmental expectations for firms and risk highlighting less favorable aspects of their environmental practices. Despite previous research on the impact of media exposure on firms’ financial outcomes, a notable gap exists in understanding how the media spotlight on firms’ climate actions affects their operational and financial performance. A clear hurdle is the lack of a systematic and objective measurement of such a spotlight. Our study addresses this research gap by developing a machine learning-based framework to derive a climate action vocabulary as a novel information artifact. The vocabulary is subsequently used to measure the intensity of media attention on a firm's climate actions. Our research reveals that an increased media spotlight on climate actions, regardless of their sentiment, has an adverse effect on firms’ financial performance, primarily due to the rise in operational costs. Furthermore, we find that the impact of media spotlight is heterogeneous. Low-polluting firms experience negative financial consequences as the downside of the heightened media spotlight. In contrast, high-polluting firms and those operating in polluting industries may witness an overall positive financial impact. We also find that firms’ market orientation (business-to-business vs. business-to-consumer), durability classification, index constituency (i.e., S&P 500), and greenwashing status significantly moderate the relationship. Our research highlights key considerations for corporate leaders with respect to the drawbacks for low-polluting firms in seeking media attention for their climate actions. Given that the media captures society's limited attention, our research suggests that firms should refrain from promoting superficial narratives about climate change that distract society from more impactful sustainability conversations.
Muzeeb Shaik, Shrihari Sridhar, Chelliah Sriskandarajah
et al.
To meet sales targets with limited resources, business-to-business service firms must prioritize promising opportunities within large pipelines. Yet, both theory and practice indicate that such decisions often rely on intuition or ad hoc rules, resulting in suboptimal sales and operations planning. Drawing on the relationship management and organizational buying literature, we develop a theory-informed sales-operations framework that links buyer typology (e.g., new vs. rebid) and opportunity characteristics (e.g., size and relationship strength) to the firm’s bid and win decisions. Using archival data from a global on-site services provider encompassing 4,574 opportunities across 23 countries (2010–2021), we document a persistent tradeoff: while low-risk, relationship-based opportunities yield higher win probabilities, they are insufficient to achieve regional sales goals. We address this challenge through an ensemble machine-learning model that predicts win likelihood and a combinatorial optimization model that allocates bidding capacity strategically. The integrated framework improves predictive accuracy by 11% and could have increased realized sales by 21% while bidding on 38% fewer opportunities. Extensions incorporating stochastic programming and a Heckman-style two-stage correction enhance the framework’s robustness to uncertainty and data selection bias, providing managers with a rigorous, data-driven approach to opportunity management.
On-demand platforms such as ride-sharing services rely heavily on economic incentives to attract, retain, and manage independent workers who have significant discretion over whether and where to work. Using an analytically tractable spatial model, we explore the impact of different pricing and commission strategies on customer demand, driver entry and retention, and their location choices. Our model yields several unique results and actionable insights. We find that flexible commission policies are more effective than fixed commission policies in allocating drivers efficiently across locations, reducing bottlenecks, and improving driver retention. We also show that commission-based interventions are more effective than price interventions in responding to labor market changes, as they directly affect driver incentives without distorting customer demand. Finally, if fairness-sensitive customers are prevalent in the market, then fixed pricing, combined with flexible commissions, becomes the optimal rule. Simulations based on actual ride patterns from New York City and Los Angeles confirm our insights.
Haochen Wu, Lesley A. Weitz, Jeffrey M. Henderson
et al.
Advanced Air Mobility (AAM) represents an evolution of the air transportation system by introducing low-altitude, potentially high-traffic environments. AAM operations will be enabled by both new aircraft, as well as new safety- and efficiency-critical supporting infrastructure. Published concepts of operations from both public and private sector entities establish notions such as federated management of the airspace and public-private partnerships for AAM air traffic, but there is a gap in the literature in terms of integrated tools that consider all three critical elements: AAM fleet operators (\emph{lower} layer), airspace service providers (\emph{middle} layer), and overall system governance from the legacy air navigation service provider (\emph{upper} layer). In this work, we explore modeling congestion management within the AAM setting using a bi-level optimization approach, focusing on (1) time-varying, stochastic AAM demand, (2) differing congestion management strategies, and (3) the impact of unscheduled, \enquote{pop-up} demand. We show that our bi-level formulation can be tractably solved using a Neural Network-based surrogate which returns solution qualities within 0.1-5.2\% of the optimal solution. Additionally, we show that our congestion management strategies can reduce congestion by 25.7-39.8\% when compared to the scenario of no strategies being applied. Finally, we also show that while pop-up demand degrades congestion conditions, our congestion management strategies fare better against pop-up demand than the no strategy scenario. The work herein contributes a rigorous modeling and simulation tool to help evaluate future AAM traffic management concepts and strategies.
Panagiotis D. Diamantoulakis, V. M. Kapinas, G. Karagiannidis
The smart electricity grid enables a two-way flow of power and data between suppliers and consumers in order to facilitate the power flow optimization in terms of economic efficiency, reliability and sustainability. This infrastructure permits the consumers and the micro-energy producers to take a more active role in the electricity market and the dynamic energy management (DEM). The most important challenge in a smart grid (SG) is how to take advantage of the users' participation in order to reduce the cost of power. However, effective DEM depends critically on load and renewable production forecasting. This calls for intelligent methods and solutions for the real-time exploitation of the large volumes of data generated by a vast amount of smart meters. Hence, robust data analytics, high performance computing, efficient data network management, and cloud computing techniques are critical towards the optimized operation of SGs. This research aims to highlight the big data issues and challenges faced by the DEM employed in SG networks. It also provides a brief description of the most commonly used data processing methods in the literature, and proposes a promising direction for future research in the field.
Inventory is the act of storing raw materials, semi-finished goods, or finished products with the aim of optimizing the smoothness of production processes or business operations within a company, whether in the manufacturing or service sector. This inventory is carried out by the orange supplier in Lembang District, West Bandung Regency. The purpose of inventory management is to consistently maintain the quality of the orange products. The research method employed is descriptive, utilizing data collection techniques through observation and interviews. The research findings indicate that the inventory model employed by the orange supplier in Lembang District is make-to-stock, using the FIFO (First In, First Out) method. Goods, in the form of oranges that enter first will be promptly withdrawn with a maximum storage period of 10 days. The next process is packaging, intended for shipment to large-scale consumers (supermarkets) or small-scale ones (vendors or individual consumers).
Precision agriculture technologies (PATs) transform crop production by enabling more sustainable and efficient agricultural practices. These technologies utilize data-driven approaches to optimize the management of crops, soil, and resources, thus enhancing both productivity and environmental sustainability. This article reviewed the application of PATs for sustainable crop production and environmental sustainability around the globe. Key components of PAT include remote sensing, GPS-guided equipment, variable rate technology (VRT), and Internet of Things (IoT) devices. Remote sensing and drones deliver high-resolution imagery and data, enabling precise monitoring of crop health, soil conditions, and pest activity. GPS-guided machinery ensures accurate planting, fertilizing, and harvesting, which reduces waste and enhances efficiency. VRT optimizes resource use by allowing farmers to apply inputs such as water, fertilizers, and pesticides at varying rates across a field based on real-time data and specific crop requirements. This reduces over-application and minimizes environmental impact, such as nutrient runoff and greenhouse gas emissions. IoT devices and sensors provide continuous monitoring of environmental conditions and crop status, enabling timely and informed decision-making. The application of PAT contributes significantly to environmental sustainability by promoting practices that conserve water, reduce chemical usage, and enhance soil health. By enhancing the precision of agricultural operations, these technologies reduce the environmental impact of farming, while simultaneously boosting crop yields and profitability. As the global demand for food increases, precision agriculture offers a promising pathway to achieving sustainable crop production and ensuring long-term environmental health.
In the context of increasing global population and climate change, modern agriculture must enhance production efficiency. Vegetables production is crucial for human nutrition and has a significant environmental impact. To address this challenge, the agricultural sector needs to modernize and utilize advanced technologies such as drones to increase productivity, improve quality, and reduce resource consumption. These devices, known as Unmanned Aerial Vehicles (UAV), with their agility and versatility play a crucial role in monitoring and spraying operations. They significantly contribute to enhancing the efficacy of precision farming.The aim of this review is to examine the critical role of drones as innovative tools to enhance management and yield of vegetable crops cultivation. This review was carried out using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) framework and involved the analysis of a wide range of research published from 2018 to 2023. According to the phases of Identification, Screening, and Eligibility, 132 papers were selected and analysed. These papers were categorized based on the types of drone applications in vegetable crop production, providing an overview of how these tools fit into the field of Precision Farming. Technological developments of these tools and data processing methods were then explored, examining the contributions of Machine and Deep Learning and Artificial Intelligence. Final considerations were presented regarding practical implementation and future technical and scientific challenges to fully harness the potential of drones in precision agriculture and vegetable crop production. The review pointed out the significance of drone applications in vegetable crops and the immense potential of these tools in enhancing cultivation efficiency. Drone utilization enables the reduction of input quantities such as herbicides, fertilizers, pesticides, and water but also the prevention of damages through early diagnosis of various stress types. These input savings can yield environmental benefits, positioning these technologies as potential solutions for the environmental sustainability of vegetable crops.
During the last decade, wireless data services have had an incredible impact on people's lives in ways we could never have imagined. The number of mobile devices has increased exponentially and data traffic has almost doubled every year. Undoubtedly, the rate of growth will continue to be rapid with the explosive increase in demands for data rates, latency, massive connectivity, network reliability, and energy efficiency. In order to manage this level of growth and meet these requirements, the fifth-generation (5G) mobile communications network is envisioned as a revolutionary advancement combining various improvements to previous mobile generation networks and new technologies, including the use of millimeter wavebands (mm-wave), massive multiple-input multipleoutput (mMIMO) multi-beam antennas, network densification, dynamic Time Division Duplex (TDD) transmission, and new waveforms with mixed numerologies. New revolutionary features including terahertz (THz) communications and the integration of Non-Terrestrial Networks (NTN) can further improve the performance and signal quality for future 6G networks. However, despite the inevitable benefits of all these key technologies, the heterogeneous and ultra-flexible structure of the 5G and beyond network brings non-orthogonality into the system and generates significant interference that needs to be handled carefully. Therefore, it is essential to design effective interference management schemes to mitigate severe and sometimes unpredictable interference in mobile networks. In this paper, we provide a comprehensive review of interference management in 5G and Beyond networks and discuss its future evolution. We start with a unified classification and a detailed explanation of the different types of interference and continue by presenting our taxonomy of existing interference management approaches. Then, after explaining interference measurement reports and signaling, we provide for each type of interference identified, an in-depth literature review and technical discussion of appropriate management schemes. We finish by discussing the main interference challenges that will be encountered in future 6G networks and by presenting insights on the suggested new interference management approaches, including useful guidelines for an AI-based solution. This review will provide a first-hand guide to the industry in determining the most relevant technology for interference management, and will also allow for consideration of future challenges and research directions.
Most activities in hospitals require the presence of the patient. Delays in patient transport can disrupt operations, potentially resulting in idle staff, underutilized equipment, and postponed procedures, which in turn lead to lost revenue, unnecessary costs across many different areas and departments, and lower patient satisfaction. Consequently, patient transport planning is a central operational task in hospitals. This paper provides the first literature review of Operations Research and Management Science approaches for non-emergency, intra-hospital patient transport. We structure the different patient transport problems considered in the literature according to several main characteristics and introduce a five-field notation that allows for a concise representation of different problem variants. We then analyze the relevant literature with respect to different aspects related to the considered problem variant, the employed modeling and solution techniques, as well as the data used and the level of practical implementation achieved. Based on our literature analysis and semi-structured interviews with hospital practitioners, we compare current hospital practices and the existing literature, identify research gaps, and formulate an agenda for relevant future research.
The rapid evolution of web and mobile applications has necessitated robust mechanisms for managing application state to ensure consistency, performance, and user-friendliness. This comprehensive review examines the most effective Application State Management (ASM) techniques, categorized into Local State Management, State Management Libraries, and Server-Side State Management. By analyzing popular front end frameworks the study delves into local state management mechanisms. It also evaluates the state of front end management libraries, highlighting their implementations, benefits, and limitations. Server-side state management techniques, particularly caching, are discussed for their roles in enhancing data retrieval efficiency. This paper offers actionable insights for developers to build scalable, responsive applications, aiming to bridge the gap between theoretical knowledge and practical application. This study's critical analysis and recommendations aim to guide future research and development in ASM, contributing to the advancement of modern application architecture.
Anantha Sharma, Sheeba Elizabeth John, Fatemeh Rezapoor Nikroo
et al.
The growth of digital documents presents significant challenges in efficient management and knowledge extraction. Traditional methods often struggle with complex documents, leading to issues such as hallucinations and high latency in responses from Large Language Models (LLMs). ZeroG, an innovative approach, significantly mitigates these challenges by leveraging knowledge distillation and prompt tuning to enhance model performance. ZeroG utilizes a smaller model that replicates the behavior of a larger teacher model, ensuring contextually relevant and grounded responses, by employing a black-box distillation approach, it creates a distilled dataset without relying on intermediate features, optimizing computational efficiency. This method significantly enhances accuracy and reduces response times, providing a balanced solution for modern document management. Incorporating advanced techniques for document ingestion and metadata utilization, ZeroG improves the accuracy of question-and-answer systems. The integration of graph databases and robust metadata management further streamlines information retrieval, allowing for precise and context-aware responses. By transforming how organizations interact with complex data, ZeroG enhances productivity and user experience, offering a scalable solution for the growing demands of digital document management.
Zine el abidine Kherroubi, Monika Prakash, Jean-Pierre Giacalone
et al.
Modern wireless communication systems have become increasingly complex due to the proliferation of wireless devices, increasing performance standards, and growing security threats. Managing these networks is becoming more challenging, requiring the use of advanced network management methods and tools. AI-driven network management systems such as Self-Optimizing Networks (SONs) are gaining attention. On the other hand, Large Language Models (LLMs) have been demonstrating exceptional zero-shot learning and generalization capabilities across several domains. In this paper, we leverage the potential of LLMs with SONs to enhance future network management systems. Specifically, we benchmark the use of various LLMs such as GPT-4, Llama, and Falcon, in a zero-shot setting based on their real-time network configuration recommendations. Our results indicate promising prospects for integrating LLMs into future network management systems.