Retail Industry Research Articles

Multi-Modal product recognition in retail environments: Enhancing accuracy through integrated vision and OCR approaches

This research presents a transformative artificial intelligence solution that addresses critical operational challenges in modern retail environments. Our integrated system combines advanced computer vision and text recognition technologies to automate product identification, inventory tracking, and checkout processes. In response to the retail industry's pressing needs for automation amid labor shortages and rising operational costs, we developed and implemented a comprehensive solution that demonstrates significant business value. The system achieved a 94.6% accuracy rate in product recognition while processing 50-60 items per second, enabling real-time inventory management and automated checkout capabilities. Field testing across multiple retail locations showed a 35% reduction in inventory management time, a 40% decrease in checkout wait times, and a 25% improvement in stock accuracy. The solution encompasses a robust dataset of 538 distinct products, including challenging categories such as liquor bottles and grocery items, and features sophisticated optimization techniques that ensure consistent performance in diverse retail environments. Implementation of this system can lead to substantial operational cost savings, enhanced customer experience, and improved inventory accuracy. Our research demonstrates how AI-driven automation can address the retail industry's current challenges while providing a scalable foundation for future innovations in retail operations management.

Harnessing the Power of ML/AI: Transforming Data into Actionable Insights

Artificial intelligence (AI) and machine learning (ML) have become game-changing technologies transforming how businesses glean insights from massive amounts of data. This in-depth article examines the core ideas, practical applications, and implementation techniques of ML/AI in various industries. The article discusses the crucial elements of strategic implementation, such as problem identification, data preparation, and model development lifecycles, while examining the progress of machine learning methodologies from supervised and unsupervised to deep learning. It delves further into the key instruments and technologies advancing machine learning, emphasizing the value of strong frameworks and effective data processing infrastructure. The article illustrates the observable effects of ML/AI deployment with thorough case studies from the retail, financial services, and healthcare industries. The article's conclusion examines new developments in explainable AI, edge AI, federated learning, and autoML, offering predictions for their future.

Barriers to product return in a circular supply chain: a case from a retailing industry

Abstract The circular supply chain (CSC) plays a pivotal role in delivering sustainable products to end users, profoundly affecting organizational performance and societal welfare. In this ecosystem, effective Product Return Management (PRM) is crucial, as consumers play a pivotal role in returning used products for reuse or recycling. Despite its significance in waste reduction and attaining environmental and economic goals, scant research has explored product return barriers within CSC. This study bridges this gap by investigating and prioritising these barriers to enhance the circular economy. Employing a systematic literature review (SLR), we extracted and screened 13 product return barriers in CSC using the Grey Delphi method. Subsequently, we introduced an enhanced version of the Group Grey-Best–Worst Method (GG-BWM), a novel approach that harmonises expert judgments by addressing uncertainties through Grey Theory and incorporating both individual-level and group-level inconsistency ratios. The findings highlight lack of motivation, lack of consumer awareness, and lack of proper infrastructure as the most critical barriers. Furthermore, sensitivity analysis conducted across 11 scenarios emphasises the significance of our results. This study contributes to the group multiple criteria decision making (MCDM) methods and CSC literature, while offering actionable insights practitioners seeking to improve the effectiveness of their CSC strategies.

Enhancing supply chain resilience: The role of security practices and performance in mitigating disruptions in ghana’s manufacturing sector

At the current stage, the retail industry is undergoing unprecedented changes. From traditional physical stores to online shopping platforms, and then to the new retail model that integrates online and offline, customers’ demand for shopping experience is constantly changing. To meet these demands, retailers need to constantly explore and apply new technologies to optimize the retail environment and enhance customer experience. Biomechanics is the study of the internal and external mechanical behavior of living organisms, which is concerned with the structure, function and motion laws of living organisms. Applying the knowledge of biomechanics to retail environment design can effectively improve customers’ shopping experience. Based on this, this paper takes intelligent container as an example, gives a visual solution of detecting goods in intelligent container based on deep neural network, and proposes a twin-based pairwise image difference detection algorithm named DiffNet as the core algorithm of intelligent container solution, which aims to help enterprises deploy intelligent container flexibly, safely and at low cost. Enhance the customer’s offline self-service shopping experience.

A Machine Learning Method to improve Supplier Delivery Appointments in Supply Chain Industries

Goal: The paper aims at the customization of supplier schedules upon the priority of key articles. It aims at the prediction of an appointment to a supplier based on the key aspects. The objective of this study is to investigate whether the machine learning algorithms can be used to predict the delivery dates of the products based on the trained data. Design/Methodology/Approach: The prediction method uses a machine learning approach. Prediction algorithms namely, Logistic Regression (LR), K-Nearest Neighbour (KNN), and Random Forest (RF) are used for forecasting. The appointment is assigned to a supplier based on the delivery date of a previous supplier order. If a supplier requests a prior date, then the average sales and opening stock of the products is verified and the prior date of n-3 will be assigned (‘n’ represents the assigned days of delivery). Results: Clustering is used to visualize the group of products based on the days of delivery and quantity ordered. This helps in creating the floor space for the on-time delivery of fast-moving products and reducing the manual process for the reordering team. The present work can be used for the procurement of key articles for the fulfillment of customer demands. The combination of K-Means with prediction and classification is giving lesser expected delivery date using RF compared to LR and KNN method. Limitations: The limitation of the current work is that it is applicable for small scale industries in the developing countries. Also deployment of the application in agricultural sectors allow for greater transparency and accountability in the supply chain. Practical Implications: Further, the work will also provide recommendations for retail companies looking to implement machine learning algorithms in their supply chain management. Originality/ Value: The case study developed a model for Retail industries to manage the supplier delivery appointments.

Serverless Data Engineering: Revolutionizing Retail and Finance Industries

Serverless Data Engineering: Revolutionizing Retail and Finance Industries

Reducing product waste within the retail industry: a post-COVID-19 era study on enhancing demand prediction with hybrid prediction models

ABSTRACT The retail sector faces growing challenges, particularly aligning with the European Union’s sustainable policies to minimize waste. This paper proposes a framework to address two pivotal goals: i) Introducing cutting-edge machine learning models to forecast demand within the context of a post-COVID environment. ii) Evaluating the benefits of integrating these predictive into operational strategies by measuring the reduction in overstock levels compared to traditional business practices. The hybrid Prophet-XGBoost model consistently outperformed classical and other hybridization models in terms of accuracy (lowest MAPE and WAPE), when predicting demand. This study uses data from 2019 to 2023 but excludes 2020 and 2021 due to the disruptions caused by COVID-19. Our findings reveal that relying solely on recent data from 2022 to 2023 results in lower model accuracy compared to historical imputation methods. Notably, substituting 2019 values for 2021 outperforms interpolating with data from 2022. Beyond its methodological advancements, this research introduces a novel approach to quantifying overstock reduction, contributing to both academic literature and retail practice. In this case, we observed a significant overstock issue with non-food products, likely tied to agreements between retailers and suppliers. As these products are non-perishable, retailers appear to have been less cautious in managing stock levels.

MOTIVASI HEDONIC SHOPPING PADA GENERASI Z DI KOTA BANDUNG

The phenomenon of shopping is an interesting discussion from a social and economic perspective, the development of the era has given rise to various choices of shopping methods with the aim of making it easier for buyers to access the goods and services they will buy, one of which is online shopping which is increasingly popular among young people, especially generation Z. This study aims to explain how hedonic shopping motivation is carried out by Gen Z, how they choose to shop not only to meet their needs but also the choice is based on providing emotional pleasure and satisfaction and following certain trends that are currently popular in society or commonly known as Fear of Missing Out (FOMO). This study uses the theory of hedonic shopping motivation according to Hirschman and Holbrook (1982) who developed the concept of hedonic shopping types which state that a consumer gets satisfaction from the shopping experience regardless of the benefits or uses obtained from purchasing the goods as an analytical tool for evaluating hedonic shopping motivation in generation Z in Bandung City. This descriptive research conducted aims to create a systematic, factual, and accurate description or picture of the implementation of hedonic shopping motivation in the retail industry. Based on the analysis results, it was found that hedonic shopping motivations were driven by adventure shopping, gratification shopping, role shopping, value shopping, social shopping, and idea shopping.

Revolutionizing Retail: How Technology Innovation Drives Customer Experience and Market Adaptability in Indonesia

This study investigates the relationship between technology innovation, digital customer experience, and market responsiveness in Indonesia's retail industry. As the retail sector increasingly adopts omnichannel strategies, technology innovation is viewed as a critical enabler of seamless customer experiences and operational agility. This research employs a quantitative approach, utilizing Structural Equation Modeling - Partial Least Squares (SEM-PLS) to analyze data collected from 150 respondents. The findings reveal that Technology Innovation significantly enhances Digital Customer Experience, which, in turn, positively impacts Market Responsiveness. The study highlights the mediating role of digital customer experience in reinforcing the link between technology innovation and market responsiveness. These results underscore the importance of leveraging technological advancements to create comprehensive omnichannel ecosystems, enabling retailers to respond swiftly and effectively to evolving consumer demands. By providing empirical insights into the interplay between technology, customer experience, and responsiveness, this research contributes to the growing body of knowledge on retail innovation and omnichannel strategies in Indonesia. The findings offer practical implications for retail businesses aiming to strengthen their market position through enhanced digital engagement and adaptive market strategies.

The Influence of Customer Perceived Value, Satisfaction, and Trust on Loyalty among E-Shoppers in Palestine’s E-Commerce Platforms

E-commerce has grown significantly in the past few years and has significantly affected the retail industry worldwide, including Palestine. Due to the improved Internet connectivity, high usage of smartphones, and the young population of Palestine, the e-commerce industry has rapidly developed. This research examines the relationship between customer-perceived value, satisfaction, trust, and loyalty concerning Palestinian e-shoppers in the context of local e-commerce platforms. The quantitative data were collected using a structured survey of (278) online shoppers and analyzed using SEM to test the proposed model. The results show that perceived value does not directly affect customer loyalty; the relationship is mediated by customer satisfaction and trust. In detail, a perceived value improves customer satisfaction and trust, both antecedents of loyalty. The satisfaction and trust of the customers are not just important; they are the foundation of their loyalty. These findings suggest that e-commerce platforms in Palestine need to embrace a comprehensive approach that aims to achieve a high level of customer-perceived value, satisfaction, and trust. By focusing on these aspects, online retailers can successfully build and sustain customer loyalty, thus guaranteeing continued growth and market leadership in the growing Palestinian e-commerce industry. This research provides theoretical implications for consumer behaviors in emerging markets and managerial implications for e-commerce firms that seek to enhance customer retention strategies.

Integration of ESG Data in the Retail Sector (Essential for Sustainable business and Risk Management)

The ESG data integrated into the retail industry have grown as an essential driver of sustainable business practices and robust risk management strategies. With sustainability concerns being highly critical for businesses around the world, retail organizations have taken up the ESG metrics as the yardstick to measure, report, and act upon environmental and social impact, ensuring good governance. It explains how AI-driven business intelligence integrates ESG factors for the improvement of sustainability metrics in the retail industry, focusing on how such integration supports the creation of sustainable business strategies and improves financial performance. It discusses challenges and opportunities in adopting sustainability reporting and ESG metrics, drawing on global evidence from retail performance-related studies and providing insight into how ESG adoption may influence operational efficiency, consumer trust, and long-term value creation. The study also underlines the role of AI in the automation of ESG factor scoring; it thus facilitates decision-making, transparency, and risk management. This paper also articulates how ESG integration relates to better performance of the firm within a transition towards the Circular Economy and vice versa in the retail sector. Keywords:ESG, Retail Sector, Sustainable Business Practices, AI-driven Integration, Risk Management, Sustainability Reporting, Circular Economy, Business Intelligence, Firm Performance, and Corporate Social Responsibility.

Maximizing Retail Potential: The Role of Big Data Analytics

The retail industry has been transformed by technological advancements and evolving consumer behavior. This paper examines the benefits, challenges, and strategic implications of Big Data Analytics (BDA) in the retail sector through a comprehensive review of recent literature. The findings highlight three key opportunities for leveraging BDA in retail: enhancing customer relationships, improving operational efficiency, and gaining strategic advantages. However, several challenges persist, including privacy and security concerns, inadequate data-driven support systems, complexities in data visualization and processing, and insufficient governmental support. In addition to exploring the potential advantages and limitations, this study emphasizes the strategic role of BDA in facilitating data-driven decision-making for retailers. By balancing these opportunities and challenges, retail firms can harness Big Data as a critical source of competitive advantage. Finally, the paper identifies future research directions to further advance the application of Big Data in the retail sector, aiming to address existing gaps and unlock its full potential.

An Evaluation Research of Suning Tesco Merchants Carrefour

In 2019, Suning Tesco announced the acquisition of 80%of the shares of Carrefour China for RMB 4.3 billion. The mergers and acquisitions marked important measures to further deepen the layout of retail business and expand offline channels. Carrefour China has faced operating difficulties for a long time, while Suning hopes to enhance its competitiveness in the retail industry through mergers and acquisitions to accelerate online and offline integration. Suning completed the acquisition through its subsidiary Suning Commercial Trade, and promised to further invest in Carrefour China, promote its transformation and upgrading, and improve store operation efficiency and supply chain management. In the integration of mergers and acquisitions, Suning plans to use its advantages in big data, logistics and technology to enhance the competitiveness of Carrefour in the Chinese market, and position the Carrefour China as a "offline experience, online shopping" retail platform. For Suning, the acquisition not only enhances its advantages in the traditional retail field, but also promotes the in -depth integration of e -commerce and physical retail; Carrefour China can use Suning's localization experience and e -commerce platform to improve its operations. Status, enhance market share. In general, this M&A has brought synergy effects to both parties and promoted the innovation and transformation of the retail industry.

Effective Recruitment and Employee Development Tools in the Context of Labor Shortages in the Service Sector

The labor shortage in the food service, retail, and hospitality industries is becoming an increasingly pressing issue, complicating business growth. High employee turnover significantly raises costs associated with recruitment, training, and adaptation, which is particularly challenging for small and medium–sized enterprises. This article examines the causes of the labor shortage, including demographic decline, seasonality, competition for talent, and evolving employee expectations.Special attention is given to effective methods of recruitment and employee retention. The study explores non–material motivation tools, such as individual development plans, career planning, and workBlife balance support. Streamlining the hiring process through automation, HR branding, and the cultivation of corporate culture are highlighted as key strategies for reducing turnover.Furthermore, the importance of a systematic approach to employee training and development is emphasized, contributing to increased engagement and service quality. The article concludes that a comprehensive approach to personnel management is essential for enhancing companies’ competitiveness in the context of labor shortages.

Understanding customer opinions on IoT applications implemented in the retail industry worldwide and its implications for businesses in Vietnam

PurposeThis study aims to deeply understand customer experiences toward Internet of Things (IoT) applications in retail by developing machine learning models for aspect-based sentiment analysis (SA). It includes creating a related terms dictionary and proposing implications for retail businesses in Vietnam based on these analyses. The ultimate goal is to gain insights into customer opinions and assist administrators in formulating effective digital transformation and business strategies within the Vietnamese market.Design/methodology/approachInitially, this research uses qualitative methods to identify different aspects of customer experience at stores equipped with IoT applications. Then, quantitative methods were applied through classification machine learning models which were trained on the annotated data set to classify comments into aspects and sentiments. Finally, the classification results were analyzed and visualized to draw implications about customer opinions of these stores.FindingsThis study collected 77,042 customers’ comment from potential and actual customers who have ever shopped at retail stores with IoT applications deployed worldwide, identified ten new aspects of customer experience in this field and built a dictionary of related terms. Furthermore, this study contributed two efficient ensemble models with an accuracy of 81% and 89% for analyzing aspects and customer sentiments, respectively. This study also proposes implications for managers regarding the use of IoT technology in retail stores to improve shopping experiences for customers.Originality/valueThis study’s findings help managers develop appropriate digital transformation and business strategies for integrating IoT technology into retail stores, especially for retail businesses in the Vietnamese market based on the analysis results and proposed model.

Empirical Study of Social Media Marketing's Impact on Brand Image and Loyalty in Retail, Muscat

This study aims to explore SMM's influence on brand image and loyalty in the retail industry of Muscat. The study employs the quantitative method as it ensures valid and reliable results. The data for the analysis were collected from 150 employees in the retail sector through structured survey questionnaires. The data is analyzed using the SPSS tool through statistical approaches such as ANOVA, Correlation, and Regression. The findings of this research will illuminate the significance of SMM and identify factors that encourage SMM in the retail sector. The outcomes provide valuable insights into SMM initiatives that aid in increasing Muscat's brand reputation and image in the retail industry. This study's implications will benefit retail organizations in attaining a competitive advantage in the market.

Machine Learning Techniques in Predicting Sales a Case Study of Jumia

The retail industry has experienced significant growth with the advent of e-commerce platforms like Jumia. Predicting sales accurately is critical for inventory management, marketing strategies, and overall operational efficiency. This paper explores the application of machine learning techniques to predict sales on Jumia, leveraging historical sales data and other relevant features to build and evaluate predictive models. Our results demonstrate that advanced machine learning models, particularly the gradient boosting machine, significantly outperform the baseline linear regression model. The gradient boosting machine achieved the lowest Mean Absolute Error (MAE) and Mean Squared Error (MSE), highlighting its superior prediction accuracy. Feature importance analysis revealed that pricing, promotional activities, and seasonal factors are key drivers of sales. These findings indicate that machine learning can effectively capture sales patterns, providing valuable insights for decision-makers.

Research on Financial Risks in the Food Retail Industry under the Background of New Retail Transformation—A Case Study of Company G

Research on Financial Risks in the Food Retail Industry under the Background of New Retail Transformation—A Case Study of Company G

Links among service quality, customer satisfaction and loyalty in Vietnam retail industry during the COVID-19 pandemic

Links among service quality, customer satisfaction and loyalty in Vietnam retail industry during the COVID-19 pandemic

A study of the Impact of AI and New Media on Trade Development in the Retail Sector and Coping Strategies

The rapid advancement of global information technology is transforming industries at an unprecedented pace, with emerging technologies becoming integral to our daily lives and reshaping traditional business models. The retail industry, in particular, has witnessed a significant evolution over the past three decades. What once relied solely on face-to-face interactions has now shifted through the e-commerce revolution and into a highly sophisticated model powered by artificial intelligence (AI) and big data. These technological breakthroughs have redefined the retail landscape, enabling businesses to harness data-driven marketing strategies and leverage the growing influence of social media for promotion. As a result, geographical barriers are no longer a limitation, and retailers can reach a global audience with ease. However, this transformation also brings new challenges, such as increased competition and the need for product differentiation in a crowded marketplace. The evolution of retail not only reflects the broader impact of global technological advancements but also highlights the opportunities and complexities of operating in the digital age. By exploring these cutting-edge technologies, this paper can better understand the forces shaping modern retail and gain valuable insights into this rapidly changing era.