Abstract
Green Supply Chain Management (GrSCM) has become one of the most crucial innovation in the Supply Chain Management (SCM). This approach involves environmental concerns and issues into the SCM, thus, companies and authorities tend to exploit the GrSCM through logistics process in order to improve their performance. In this paper, we will give a demonstration of the added value of the Urban Traffific Management (UTM) and its integration in the concept of GrSCM, we also aim to study its impact on the performance improvement in Transport Management with a focus on Air quality improvement. This study proposes a new approach and model based on Deep learning for Urban Traffific Control Management to solve the traffific flflow problem in order to reduce the congestion, improve the air quality and enhance the urban supply chain. Our proposed framework for Data collection and processing is mainly based on Internet of Thing (IoT) technologies for an effificient Smart City.
Highlights
Nowadays, the industrial companies tend to enhance their products quality, reduce lead time and costs, taking into account the customer requirements, in line with the changing competitive landscape and pressure on prices, which force them to focus more on sustainability than the environmental issues [1]
This paper offers a new framework and model to assess the impact of the Urban Traffic Control Management (UTCM) on the Green Supply Chain Management (GrSCM)
Our approach for Urban Traffic Control Management is based on Artificial Intelligence and Internet of Thing (IoT), in order to tackle a worldwide issue which is air pollution in cities which means hazards for health and worse quality of life
Summary
The industrial companies tend to enhance their products quality, reduce lead time and costs, taking into account the customer requirements, in line with the changing competitive landscape and pressure on prices, which force them to focus more on sustainability than the environmental issues [1]. The environmental pollution is a serious problem worldwide, knowing that manufacturing and transportation are among the main sources of toxic gas emissions and subsequently affect the scale of air quality. Green Supply Chain Management (GrSCM) was introduced as a preventive approach and solution to improve the performance of the company’s processes and products. 26 TOWARDS A GREEN SUPPLY CHAIN BASED ON SMART URBAN TRAFFIC USING DEEP LEARNING APPROACH in countries under development [4]. Our research work deals with Green Supply Chain and Transport, which form an important research field in corporate governance with a particular attention on Urban Traffic Management. Our aim to propose a smart solution of Urban traffic management based on Artificial Intelligence in order to reduce congestion, improve the air quality for an ecological smart city. We will present the Deep Learning Model used for urban traffic prediction traffic and the project implementation framework in the GrSCM context
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