Goodyear Project - Mile Marker 0

This report offers recommendations and implementation methods of different modes of entry for Goodyear to enter into the last mile delivery market. Goodyear came to us with the prompt, pick a customer segment, build a strategy, frame how it works and pitch the idea, and this paper attempts to answer this prompt

Transporte de mercancías al interior de las ciudades utilizando infraestructura ferroviaria: Una revisión

RESUMEN : Este documento propone una revisión bibliográfica de la literatura sobre sistema de transporte ferroviario urbano como método alternativo para el transporte de mercancías al interior de las ciudades. En especial, se presenta una descripción básica del proceso de distribución urbana de mercancías (DUM), los problemas asociados a la distribución urbana mercancías, la forma como se clasifican. Asimismo, se presenta estudios recientes relacionados con distribución urbana de mercancías, estudios recientes sobre el uso de sistemas ferroviario para distribución urbana de mercancías, su clasificación, finalmente se presentan los objetivos considerados en los estudios y sus métodos de solución

Spatial-Temporal Event Analysis as a Prospective Approach for Signalling Emerging Food Fraud-Related Anomalies in Supply Chains

One of the pillars on which food traceability systems are based is the unique identification and recording of products and batches along the supply chain. Patterns of these identification codes in time and place may provide useful information on emerging food frauds. The scanning of codes on food packaging by users results in interesting spatial-temporal datasets. The analysis of these data using artificial intelligence could advance current food fraud detection approaches. Spatial-temporal patterns of the scanned codes could reveal emerging anomalies in supply chains as a result of food fraud in the chain. These patterns have not been studied yet, but in other areas, such as biology, medicine, credit card fraud, etc., parallel approaches have been developed, and are discussed in this paper. This paper projects these approaches for transfer and implementation in food supply chains in view of future applications for early warning of emerging food frauds

Deep Learning for Edge Computing Applications: A State-of-the-Art Survey

With the booming development of Internet-of-Things (IoT) and communication technologies such as 5G, our future world is envisioned as an interconnected entity where billions of devices will provide uninterrupted service to our daily lives and the industry. Meanwhile, these devices will generate massive amounts of valuable data at the network edge, calling for not only instant data processing but also intelligent data analysis in order to fully unleash the potential of the edge big data. Both the traditional cloud computing and on-device computing cannot sufficiently address this problem due to the high latency and the limited computation capacity, respectively. Fortunately, the emerging edge computing sheds a light on the issue by pushing the data processing from the remote network core to the local network edge, remarkably reducing the latency and improving the efficiency. Besides, the recent breakthroughs in deep learning have greatly facilitated the data processing capacity, enabling a thrilling development of novel applications, such as video surveillance and autonomous driving. The convergence of edge computing and deep learning is believed to bring new possibilities to both interdisciplinary researches and industrial applications. In this article, we provide a comprehensive survey of the latest efforts on the deep-learning-enabled edge computing applications and particularly offer insights on how to leverage the deep learning advances to facilitate edge applications from four domains, i.e., smart multimedia, smart transportation, smart city, and smart industry. We also highlight the key research challenges and promising research directions therein. We believe this survey will inspire more researches and contributions in this promising field

Amazon and the US food retailing industry in 2018

This work project presents a case study, which describes Amazon’s effort to establish a footprint on United States’ food retailing landscape. The landscape can be described as being highly competitive and companies leverage on new technologies and private label products to increase revenues and foster customer loyalty. Following the presentation of the case, a case analysis has been conducted that focuses on the US food retailing industry’s structure, Amazon’s competitive advantages and business model and the company’s potential disruptive innovations. So far, such innovations that touch various parts of Amazon’s Omnichannel offering have taken place. The aforementioned innovations mainly thrive to improve the customer experience and convenience as well as the speed of process. In general, they allow Amazon to pursue a dual advantages approach regarding differentiation and cost leadership

Everyday Automation

This Open Access book brings the experiences of automation as part of quotidian life into focus. It asks how, where and when automated technologies and systems are emerging in everyday life across different global regions? What are their likely impacts in the present and future? How do engineers, policy makers, industry stakeholders and designers envisage artificial intelligence (AI) and automated decision-making (ADM) as solutions to individual and societal problems? How do these future visions compare with the everyday realities, power relations and social inequalities in which AI and ADM are experienced? What do people know about automation and what are their experiences of engaging with ‘actually existing’ AI and ADM technologies? An international team of leading scholars bring together research developed across anthropology, sociology, media and communication studies and ethnology, which shows how by rehumanising automation, we can gain deeper understandings of its societal impacts

THE QUANTIFIED PANDEMIC: Digitised surveillance, containment and care in response to the COVID-19 crisis

In this chapter, I present a sociocultural analysis of how automated decision-making (ADM) tools and related software were deployed or anticipated in response to the COVID-19 crisis during the first year of the pandemic. These technologies included apps used to monitor people in quarantine and self-isolation, contact tracing apps, surveillance drones, digitised temperature checking devices, apps for delivering COVID test results, software for identifying ‘at risk’ patients and for selecting recipients of vaccines, and digital vaccine ‘passport’ apps, as well as automated symptom checker apps, platforms and chatbots designed to help people determine whether they were infected with the novel coronavirus or needed to seek medical attention. Building on scholarship in critical public health, technocultures and critical data studies, I identify and discuss the social and political contexts and effects of these technologies. I demonstrate that despite techno-utopian promissory narratives routinely promoting their advantages, while some of these technologies have assisted with COVID-19 surveillance, control and medical care, many have failed. Furthermore, the deployment of these technologies has in many cases exacerbated existing socioeconomic disadvantage and stigmatisation, excluded some social groups and populations from economic support or healthcare and flouted human rights relating to privacy and freedom of movement

Everyday Automation

This Open Access book brings the experiences of automation as part of quotidian life into focus. It asks how, where and when automated technologies and systems are emerging in everyday life across different global regions? What are their likely impacts in the present and future? How do engineers, policy makers, industry stakeholders and designers envisage artificial intelligence (AI) and automated decision-making (ADM) as solutions to individual and societal problems? How do these future visions compare with the everyday realities, power relations and social inequalities in which AI and ADM are experienced? What do people know about automation and what are their experiences of engaging with ‘actually existing’ AI and ADM technologies? An international team of leading scholars bring together research developed across anthropology, sociology, media and communication studies and ethnology, which shows how by rehumanising automation, we can gain deeper understandings of its societal impacts