Search Results (103)

Background: Digital transformation is increasingly present in modern logistics, especially in the context of sustainability and circularity pressures. The integration of technologies such as Internet of Things (IoT), Radio Frequency Identification (RFID), and automated platforms involves not only infrastructure but also a strategic vision, a flexible organizational culture, and the ability to support decisions through artificial intelligence (AI)-based systems. Methods: This study proposes an extended conceptual model using structural equation modelling (SEM) to explore the relationships between five constructs: technological change, strategic and organizational readiness, transformation environment, AI-enabled decision configuration, and operational redesign. The model was validated based on a sample of 217 active logistics specialists, coming from sectors such as road transport, retail, 3PL logistics services, and manufacturing. The participants are involved in the digitization of processes, especially in activities related to operational decisions and sustainability. Results: The findings reveal that the analysis confirms statistically significant relationships between organizational readiness, transformation environment, AI-based decision processes, and operational redesign. Conclusions: The study highlights the importance of an integrated approach in which technology, organizational culture, and advanced decision support collectively contribute to the transition to digital and circular logistics chains. Full article

We live in an era where digitalization and omnichannel strategies significantly transform retail landscapes, and accurate spatial analytics from Geographic Information Systems (GIS) can deliver substantial competitive benefits. Nonetheless, despite evident practical advantages for specific targeting strategies and operational efficiency, the degree of GIS integration into academic marketing literature remains ambiguous. Clarifying this uncertainty is beneficial for advancing theoretical understanding and ensuring retail strategies fully leverage robust, data-driven spatial intelligence. To examine the intellectual development of the field, co-occurrence analysis, topic mapping, and citation structure visualization were performed on 4952 peer-reviewed articles using the Bibliometrix R package (version 4.3.3) within R software (version 4.4.1). The results demonstrate that although GIS-based methods have been effectively incorporated into fields like site selection and spatial segmentation, traditional marketing research has not yet entirely adopted them. One of the study’s key findings is the distinction between “author keywords” and “keywords plus,” where researchers concentrate on novel topics like omnichannel retail, artificial intelligence, and logistics. However, “Keywords plus” still refers to more traditional terms such as pricing, customer satisfaction, and consumer behavior. This discrepancy presents a misalignment between current research trends and indexed classification practices. Although the mainstream retail research lacks terminology connected to geomarketing, a theme evolution analysis reveals a growing focus on technology-driven and sustainability-related concepts associated with the Retail 4.0 and 5.0 paradigms. These findings underscore a conceptual and structural deficiency in the literature and indicate the necessity for enhanced integration of GIS and spatial decision support systems (SDSS) in retail marketing. Full article

Food not only fulfills basic human survival needs but also significantly impacts health and culture. Research on food-related topics holds substantial theoretical and practical significance, with food image recognition being a core task in fine-grained image recognition. This field has broad applications and promising prospects in smart dining, intelligent healthcare, and smart retail. With the rapid advancement of artificial intelligence, deep learning has emerged as a key technology that enhances recognition efficiency and accuracy, enabling more practical applications. This paper comprehensively reviews the techniques and challenges of deep learning in food image recognition. First, we outline the historical development of food image recognition technologies, categorizing the primary methods into manual feature extraction-based and deep learning-based approaches. Next, we systematically organize existing food image datasets and summarize the characteristics of several representative datasets. Additionally, we analyze typical deep learning models and their performance on different datasets. Finally, we discuss the practical applications of food image recognition in calorie estimation and food safety, identify current research challenges, and propose future research directions. Full article

This research explores how artificial intelligence (AI) can be leveraged to optimize packaging design, reduce operational costs, and enhance sustainability in e-commerce. As packaging waste and shipping inefficiencies grow alongside global online retail demand, traditional methods for determining box size, material use, and logistics planning have become economically and environmentally inadequate. Using a three-phase framework, this study integrates data-driven diagnostics, AI modeling, and real-world validation. In the first phase, a systematic analysis of current packaging inefficiencies was conducted through secondary data, benchmarking, and cost modeling. Findings revealed significant waste caused by over-packaging, suboptimal box-sizing, and poor alignment between product characteristics and logistics strategy. In the second phase, a random forest (RF) machine learning model was developed to predict optimal packaging configurations using key product features: weight, volume, and fragility. This model was supported by AI simulation tools that enabled virtual testing of material performance, space efficiency, and damage risk. Results demonstrated measurable improvements in packaging optimization, cost reduction, and emission mitigation. The third phase validated the AI framework using practical case studies—ranging from a college textbook to a fragile kitchen dish set and a high-volume children’s bicycle. The model successfully recommended right-sized packaging for each product, resulting in reduced material usage, improved shipping density, and enhanced protection. Simulated cost-saving scenarios further confirmed that smart packaging and AI-generated configurations can drive efficiency. The research concludes that AI-based packaging systems offer substantial strategic value, including cost savings, environmental benefits, and alignment with regulatory and consumer expectations—providing scalable, data-driven solutions for e-commerce enterprises such as Amazon and others. Full article

Object detection algorithms have evolved from two-stage to single-stage architectures, with foundation models achieving sustained improvements in accuracy. However, in intelligent retail scenarios, small object detection and occlusion issues still lead to significant performance degradation. To address these challenges, this paper proposes an improved model based on YOLOv11, focusing on resolving insufficient multi-scale feature coupling and occlusion sensitivity. First, a multi-scale feature extraction network (MFENet) is designed. It splits input feature maps into dual branches along the channel dimension: the upper branch performs local detail extraction and global semantic enhancement through secondary partitioning, while the lower branch integrates CARAFE (content-aware reassembly of features) upsampling and SENet (squeeze-and-excitation network) channel weight matrices to achieve adaptive feature enhancement. The three feature streams are fused to output multi-scale feature maps, significantly improving small object detail retention. Second, a convolutional block attention module (CBAM) is introduced during feature fusion, dynamically focusing on critical regions through channel–spatial dual attention mechanisms. A fuseModule is designed to aggregate multi-level features, enhancing contextual modeling for occluded objects. Additionally, the extreme-IoU (XIoU) loss function replaces the traditional complete-IoU (CIoU), combined with XIoU-NMS (extreme-IoU non-maximum suppression) to suppress redundant detections, optimizing convergence speed and localization accuracy. Experiments demonstrate that the improved model achieves a mean average precision (mAP50) of 0.997 (0.2% improvement) and mAP50-95 of 0.895 (3.5% improvement) on the RPC product dataset and the 6th Product Recognition Challenge dataset. The recall rate increases to 0.996 (0.6% improvement over baseline). Although frames per second (FPS) decreased compared to the original model, the improved model still meets real-time requirements for retail scenarios. The model exhibits stable noise resistance in challenging environments and achieves 84% mAP in cross-dataset testing, validating its generalization capability and engineering applicability. Video streams were captured using a Zhongweiaoke camera operating at 60 fps, satisfying real-time detection requirements for intelligent retail applications. Full article

The online shopping consulting service (online customer service) is an important component of online shopping, and the rapid advancement of artificial intelligence technology has allowed online retailers to apply this technology to customer service. This study explores an online retailer’s customer service innovation decisions. We develop a stylized model to investigate an online retailer’s choices of two different online shopping consulting service strategies, as well as the impact of the different strategies on both online retailer and consumers. This study demonstrates that providing AI customer service with higher performance and higher probability of consumer shopping problem resolution may not always benefit the online retailer. Existing studies have already shown that consumers prefer human customer service. However, this study shows that consumers do not always prefer human customer service, and they prefer AI customer service in some situations. Moreover, this study reveals that the probability of consumers’ shopping problems being resolved and the consulting cost can affect the online retailer’s choice of online customer service strategies. Full article

This paper proposes an innovative, intelligent shopping cart system with an interdisciplinary approach using Bluetooth low energy (BLE) beacons. The research integrates online and offline retail strategies by presenting campaigns and ads to the customers during in-store navigation. In a testing environment, BLE beacons are strategically positioned to monitor the purchasing process and deliver relevant insights to retailers. The technology anonymously logs customers’ locations and the duration of their browsing at each sales shelf. Through the analysis of client movement heatmaps, retailers may discern high-traffic zones and modify product placement to enhance visibility and sales. Additionally, the system provides an additional revenue model for store owners through location specific targeted ads displayed on a tablet mounted on the cart. Unlike previous BLE-based tracking solutions, this research bridges the gap between customer movement analytics and real-time targeted advertising in retail settings. The system achieved an accuracy of 82.4% when the aisle partition length was 3.00 m and 91.7% when the aisle partition length was 6.00 m. This system, which can generate additional income for store owners by generating 0.171 USD in a single test simulation as a result of displaying ads to three test customers in a two-partitioned aisle layout, offers a new and scalable business model for modern retailers. Full article

The complex integration of Industry 4.0 technologies into SMEs necessitates robust frameworks to address adoption barriers and enhance sustainability. The present study investigates the impact of artificial intelligence (AI), the Internet of Things (IoT), and blockchain on smart manufacturing, logistics, and sustainability in SMEs. Using a cross-sectional design, data were collected from 300 SMEs across manufacturing, logistics, and retail sectors through purposive sampling, focusing on technology adoption, and sustainability performance from 2018 to 2022. Data were analyzed using advanced machine learning models, including XG Boost and Random Forest, alongside Recursive Feature Elimination (RFE) for dimensionality reduction and quantile regression for an inferential analysis. Findings revealed that IoT adoption improved resource utilization efficiency, while blockchain enhanced ethical sourcing—furthermore, AI-driven predictive maintenance reduced operational downtimes. XG Boost achieved a Mean Squared Error (MSE), highlighting its superior predictive capability, while Random Forest achieved perfect fitness but risked overfitting. However, adoption varied significantly across firms due to financial and technical constraints, with SMEs reporting limited access to capital and skilled labor. This study underscores the need for policy interventions and targeted support for SMEs to bridge adoption gaps. The study advances the existing body of knowledge by highlighting the synergistic benefits of integrating Industry 4.0 technologies to enhance SME sustainability. Practical implications include policy recommendations for financial incentives, technical support, and capacity-building programs, empowering SMEs with actionable insights to overcome adoption barriers and achieve sustainable growth. These findings offer industry leaders and policymakers’ actionable insights to drive SME transformation in Industry 4.0, empowering them to make a difference. Full article

The rapid advancement and widespread adoption of artificial intelligence (AI) across diverse industries, including healthcare, finance, manufacturing, and retail, underscore the transformative potential of AI technologies. This necessitates the development of viable AI model marketplaces that facilitate the development, trading, and sharing of AI models across the pervasive industrial domains to harness and streamline their daily activities. These marketplaces act as centralized hubs, enabling stakeholders such as developers, data owners, brokers, and buyers to collaborate and exchange resources seamlessly. However, existing AI marketplaces often fail to address the demands of modern and next-generation application domains. Limitations in pricing models, standardization, and transparency hinder their efficiency, leading to a lack of scalability and user adoption. This paper aims to target researchers, industry professionals, and policymakers involved in AI development and deployment, providing actionable insights for designing robust, secure, and transparent AI marketplaces. By examining the evolving landscape of AI marketplaces, this paper identifies critical gaps in current practices, such as inadequate pricing schemes, insufficient standardization, and fragmented policy enforcement mechanisms. It further explores the AI model life-cycle, highlighting pricing, trading, tracking, security, and compliance challenges. This detailed analysis is intended for an audience with a foundational understanding of AI systems, marketplaces, and their operational ecosystems. The findings aim to inform stakeholders about the pressing need for innovation and customization in AI marketplaces while emphasizing the importance of balancing efficiency, security, and trust. This paper serves as a blueprint for the development of next-generation AI marketplaces that meet the demands of both current and future application domains, ensuring sustainable growth and widespread adoption. Full article

Digital intelligent supply chains strengthen industrial resilience and optimize economic efficiency in the timber industry. Information asymmetry and low collaboration efficiency remain key challenges across the timber supply chain. This study develops a three-party evolutionary game model examining digital collaboration between timber production and processing enterprises, finished product distribution and retail enterprises, and third-party service providers, introducing third-party service providers alongside traditional production and distribution enterprises. The model incorporates novel parameters including information sharing degree, value-added reliability gains, and free-riding coefficients to reflect real-world circumstances. Through equilibrium simulation and analysis, we identify four possible evolutionary states. The results demonstrate that successful digital collaboration in timber supply chains relies on three conditions: a high level of initial stakeholder involvement accelerates the formation of supply chain digital intelligent collaborative mechanisms, equitable benefit distribution maintains long-term cooperation, and integrated third-party services reduce implementation costs while improving information reliability. These findings provide a new perspective and reference for timber enterprises to implement digital transformation strategies. Full article

This exploratory study investigates Generative Artificial Intelligence’s (GenAI) use in strategy ideation for nanostores—i.e., small independent grocery retailers—to enhance their competitiveness while contributing to community sustainability. Nanostores, particularly in emerging countries, face intense competition and rapidly changing trends. These stores adopt various strategies by leveraging their proximity to consumers in neighbourhoods, resulting in different business configurations. While the existing literature highlights the broader nanostores’ functions, there is limited research on how they may develop comprehensive strategies to face their challenges. By employing a thing ethnography methodology, this work proposes GenAI thing interviewing—i.e., with ChatGPT 3.5 and Microsoft Copilot—through incremental prompting to explore potential strategy ideation and practices. Key findings suggest GenAI conversations can aid shopkeepers in strategy ideation through human-like written language, aligning with small business dynamics and structures. This proposition results in a GenAI ideation framework for strategy generation and definition. Moreover, this technology can enhance nanostore competitiveness and sustainability impact by enacting improved strategy practices in stakeholder engagements. Accordingly, this work’s main contribution underscores a GenAI-enabled conversational approach to facilitate nanostores’ strategy ideation and embedding in everyday business operations. Future work must address the limitations and further investigate GenAI’s influence on human understanding and technological creation, strategy ideation, adoption, and usability in nanostores. Full article

The occurrence of the fourth industrial revolution (Industry 4.0) has led many industries to the path of adopting new technologies. Such technologies include blockchain, artificial intelligence (AI), and the Internet of Things (IoT). Blockchain creates the opportunity to access data and information in a decentralized manner, resulting in increased customer satisfaction. This study develops a smart newsvendor model of the food industry with consignment contracts and blockchain technology. Under a consignment policy, the central division (manufacturer) can utilize the retailer’s warehouse for storage. The producer may also have the opportunity to share the holding cost with retailers without losing the ownership of products. The main contribution of this study is to analyze the profitability of the retailing and supply chain when the blockchain technology is implemented by the food industry. Moreover, a thorough investigation of profit and loss is conducted under a consignment contract when uncertain demand is encountered. This study mainly concerns perishable food items, and increasing volatility in market demand. Two cases of probabilistic uncertainty are considered, including uniform and normal distribution. The key investigations of this study are presented in terms of (a) the effect of adopting blockchain on market demand for the food industry, (b) analysis of company profitability for perishable food items and demand uncertainty, and (c) the effect of the consignment contract under blockchain technology in the food industry. Finally, this research develops an optimization tool to numerically analyze the effect of several factors of the blockchain technology on demand. Moreover, the optimal values of the design variables and the resulting maximum profitability provide valuable insights that support industry in formulating effective policies and making informed strategic decisions. Full article

Accurate forecasting of passenger checked baggage traffic is crucial for efficient and intelligent allocation and optimization of airport service resources. A systematic analysis of the influencing factors and prediction algorithms for the baggage flow is rarely included in existing studies. To accurately capture the trend of baggage flow, a combined PCC-PCA-PSO-BP baggage flow prediction model is proposed. This study applies the model to predict the departing passengers’ checked baggage flow at Chengdu Shuangliu International Airport in China. First, in the preprocessing of the data, multiple interpolation demonstrates a better numerical interpolation effect compared to mean interpolation, regression interpolation, and expectation maximization (EM) interpolation in cases of missing data. Second, in terms of the influencing factors, unlike factors that affect the airport passenger flow, the total retail sales of consumer goods have a weak relationship with the baggage flow. The departure passenger flow and flight takeoff and landing sorties play a dominant role in the baggage flow. The railway passenger flow, highway passenger flow, and months have statistically significant effects on the changes in the baggage flow. Factors such as holidays and weekends also contribute to the baggage flow alternation. Finally, the PCC-PCA-PSO-BP model is proposed for predicting the baggage flow. This model exhibits superior performance in terms of the network convergence speed and prediction accuracy compared to four other models: BP, PCA-BP, PSO-BP, and PCA-PSO-BP. This study provides a novel approach for predicting the flow of checked baggage for airport departure passengers. Full article

The speedy progress of computer vision and machine learning engineering has inaugurated novel means for improving the purchasing experiment in brick-and-mortar stores. This paper examines the utilization of YOLOv (You Only Look Once) and DeepSORT (Deep Simple Online and Real-Time Tracking) algorithms for the real-time detection and analysis of the purchasing penchant in brick-and-mortar market surroundings. By leveraging these algorithms, stores can track customer behavior, identify popular products, and monitor high-traffic areas, enabling businesses to adapt quickly to customer preferences and optimize store layout and inventory management. The methodology involves the integration of YOLOv5 for accurate and rapid object detection combined with DeepSORT for the effective tracking of customer movements and interactions with products. Information collected in in-store cameras and sensors is handled to detect tendencies in customer behavior, like repeatedly inspected products, periods expended in specific intervals, and product handling. The results indicate a modest improvement in customer engagement, with conversion rates increasing by approximately 3 percentage points, and a decline in inventory waste levels, from 88% to 75%, after system implementation. This study provides essential insights into the further integration of algorithm technology in physical retail locations and demonstrates the revolutionary potential of real-time behavior tracking in the retail industry. This research determines the foundation for future developments in functional strategies and customer experience optimization by offering a solid framework for creating intelligent retail systems. Full article

Artificial intelligence has revolutionized retail by enhancing business decision-making. This research applies the RFM (Recency, Frequency, Monetary) framework for customer segmentation, promoting sustainable consumer behaviour and eco-friendly products. Mobility issues, such as efficient goods movement and customer access, are also pivotal in sustainable retail supply chains. A systematic literature review (SLR) and Python-based clustering techniques (K-Means, hierarchical, DBSCAN) are employed to analyse a four-year dataset of customer data. The SLR identified six key areas from 71 articles. Clustering results varied: RFM binning found four clusters, K-Means and Mean Shift found three, and hierarchical and DBSCAN found two. The study emphasizes a data-centric retail strategy and the transformative impact of machine learning on customer engagement. Full article