Tecnología para Tiendas Inteligentes

Trabajo de Fin de Grado en Doble Grado en Ingeniería Informática y Matemáticas, Facultad de Informática UCM, Departamento de Ingeniería del Software e Inteligencia Artificial, Curso 2020/2021Smart stores technologies exemplify how Artificial Intelligence and Internet of Things can effectively join forces to shape the future of retailing. With an increasing number of companies proposing and implementing their own smart store concepts, such as Amazon Go or Tao Cafe, a new field is clearly emerging. Since the technologies used to build their infrastructure offer significant competitive advantages, companies are not publicly sharing their own designs. For this reason, this work presents a new smart store model named Mercury, which aims to take the edge off of the lack of public and accessible information and research documents in this field. We do not only introduce a comprehensive smart store model, but also work-through a feasible detailed implementation so that anyone can build their own system upon it.Las tecnologías utilizadas en las tiendas inteligentes ejemplifican cómo la Inteligencia Artificial y el Internet de las Cosas pueden unir, de manera efectiva, fuerzas para transformar el futuro de la venta al por menor. Con un creciente número de empresas proponiendo e implementando sus propios conceptos de tiendas inteligentes, como Amazon Go o Tao Cafe, un nuevo campo está claramente emergiendo. Debido a que las tecnologías utilizadas para construir sus infraestructuras ofrecen una importante ventaja competitiva, las empresas no están compartiendo públicamente sus diseños. Por esta razón, este trabajo presenta un nuevo modelo de tienda inteligente llamado Mercury, que tiene como objetivo mitigar la falta de información pública y accesible en este campo. No solo introduciremos un modelo general y completo de tienda inteligente, sino que también proponemos una implementación detallada y concreta para que cualquier persona pueda construir su propia tienda inteligente siguiendo nuestro modelo.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu

Deep Learning for Efficient Retail Shelf Stock Monitoring and Analysis

This thesis explores the automation of stock management in retail stores, with a specific focus on stores specializing in the sale of fruits and vegetables. Traditionally, these stores have relied on manual stock management methods, involving periodic inspections to maintain product availability. In response, this study proposes the application of Deep Learning techniques, particularly object counting models, to automate stock management. The automation process comprises two key steps. Initially, a camera positioned above a shelf of fruits and vegetables captures an image, which is processed to identify boxes containing fruits and vegetables, along with their respective categories. Afterward, a Deep Learning counting model is employed to provide an estimation of the number of objects present within each box. These estimations can then be continuously monitored or subjected to analysis to optimize store operations. The research encompasses four distinct data scenarios: supervised learning, semi-supervised learning, few-shot learning, and zero-shot learning. Within each scenario, existing object counting methods are evaluated using object detection and density estimation methodologies. The primary goals of this research are to establish an experimental setup for assessing object counting models across different learning frameworks, evaluate their performance in various scenarios, and analyze the practical strengths and limitations of these techniques in retail store environments. Key findings from the study highlight the superior performance of YOLO models, especially YOLOv5, in supervised learning scenarios, striking a balance between speed and model size. In semi-supervised learning, the application of the Efficient-Teacher approach to YOLO models enhances performance with limited labeled data. Zero-shot learning, specifically the CLIP-Count method offering a balance between speed and acceptable error rates, is recommended for data-scarce environments with sufficient computational resources. While few-shot learning, represented by the SAFECount approach, remains as the last option due to its relatively higher error, and it is suggested for situations with limited data and computational resources. Furthermore, our study reveals that improving the counting model's performance can be achieved through the removal of certain complex-shaped categories that present counting difficulties, such as grapes and hot peppers. Additionally, merging categories of fruits and vegetables with similar appearances emerges as a viable strategy for optimization. Overall, this thesis offers practical insights into automating stock tracking in retail stores. It emphasizes the importance of selecting the right learning framework and model based on specific operational needs and constraints such as data availability, providing valuable guidance to improve stock management efficiency in diverse data scenarios

Digitalization of Retail Stores using Bluetooth Low Energy Beacons

This thesis explores the domains of retail stores and the Internet of Things, with a focus on Bluetooth Low Energy beacons. It investigates how one can use the technology to improve physical stores, for the benefit of both the store and the customers. It does this by going through literature and information from academia and the relevant industry. Additionally, an interview with an expert in the retail domain is conducted, and a survey consisting of a series of interviews and questionnaire with what can be considered experts in the IT domain. A prototype app called Stass is developed, the app demonstrates some of the usages of the technology and is also used for evaluating the performance of the beacons.Masteroppgave i informasjonsvitenskapINFO39

Recent Advances in Reducing Food Losses in the Supply Chain of Fresh Agricultural Produce

Fruits and vegetables are highly nutritious agricultural produce with tremendous human health benefits. They are also highly perishable and as such are easily susceptible to spoilage, leading to a reduction in quality attributes and induced food loss. Cold chain technologies have over the years been employed to reduce the quality loss of fruits and vegetables from farm to fork. However, a high amount of losses (≈50%) still occur during the packaging, transportation, and storage of these fresh agricultural produce. This study highlights the current state-of-the-art of various advanced tools employed to reducing the quality loss of fruits and vegetables during the packaging, storage, and transportation cold chain operations, including the application of imaging technology, spectroscopy, multi-sensors, electronic nose, radio frequency identification, printed sensors, acoustic impulse response, and mathematical models. It is shown that computer vision, hyperspectral imaging, multispectral imaging, spectroscopy, X-ray imaging, and mathematical models are well established in monitoring and optimizing process parameters that affect food quality attributes during cold chain operations. We also identified the Internet of Things (IoT) and virtual representation models of a particular fresh produce (digital twins) as emerging technologies that can help monitor and control the uncharted quality evolution during its postharvest life. These advances can help diagnose and take measures against potential problems affecting the quality of fresh produce in the supply chains. Plausible future pathways to further develop these emerging technologies and help in the significant reduction of food losses in the supply chain of fresh produce are discussed. Future research should be directed towards integrating IoT and digital twins in order to intensify real-time monitoring of the cold chain environmental conditions, and the eventual optimization of the postharvest supply chains. This study gives promising insight towards the use of advanced technologies in reducing losses in the postharvest supply chain of fruits and vegetables

The impact of product, service and in-store environment perceptions on customer satisfaction and behaviour

Much previous research concerning the effects of the in-store experience on customers’ decision-making has been laboratory-based. There is a need for empirical research in a real store context to determine the impact of product, service and in-store environment perceptions on customer satisfaction and behaviour. This study is based on a literature review (Project 1) and a large scale empirical study (Projects 2/3) combining two sources of secondary data from the largest retailer in the UK, Tesco, and their loyalty ‘Clubcard’ provider, Dunnhumby. Data includes customer responses to an online self-completion survey of the customers’ shopping experience combined with customer demographic and behavioural data from a loyalty card programme for the same individual. The total sample comprised n=30,696 Tesco shoppers. The online survey measured aspects of the in-store experience. These items were subjected to factor analysis to identify the influences on the in-store experience with four factors emerging: assortment, retail atmosphere, personalised customer service and checkout customer service. These factors were then matched for each individual with behavioural and demographic data collected via the Tesco Clubcard loyalty program. Regression and sensitivity analyses were then conducted to determine the relative impact of the in-store customer experience dimensions on customer behaviour. Findings include that perceptions of customer service have a strong positive impact on customers’ overall shopping satisfaction and spending behaviour. Perceptions of the in-store environment and product quality/ availability positively influence customer satisfaction but negatively influence the amount of money spent during their shopping trip. Furthermore, personalised customer service has a strong positive impact on spend and overall shopping satisfaction, which also positively influences the number of store visits the week after. However, an increase in shopping satisfaction coming from positive perceptions of the in-store environment and product quality/ availability factors helps to reduce their negative impact on spend week after. A key contribution of this study is to suggest a priority order for investment; retailers should prioritise personalised customer service and checkout customer service, followed by the in-store environment together with product quality and availability. These findings are very important in the context of the many initiatives the majority of retail operators undertake. Many retailers focus on cost-optimisation plans like implementing self-service check outs or easy to operate and clinical in-store environment. This research clearly and solidly shows which approach should be followed and what really matters for customers. That is why the findings are important for both retailers and academics, contributing to and expanding knowledge and practice on the impact of the in-store environment on the customer experience