Factors affecting incentive dependency of residents to participate in e-waste recycling: a case study on adoption of e-waste reverse supply chain in Iran

2015 ◽  
Vol 19 (1) ◽  
pp. 325-338 ◽  
Author(s):  
Amin Jafari ◽  
Jafar Heydari ◽  
Abbas Keramati
Keyword(s):  
Supply Chain ◽  
Waste Recycling ◽  
Reverse Supply Chain ◽  
Factors Affecting

scholarly journals The Application of Blockchain-Based Life Cycle Assessment on an Industrial Supply Chain

2021 ◽  
Author(s):  
Xuda Lin ◽  
Xing Li ◽  
Sameer Kulkarni ◽  
Fu Zhao
Keyword(s):  
Life Cycle Assessment ◽  
Supply Chain ◽  
Life Cycle ◽  
Inventory Data ◽  
Specific Data ◽  
Factors Affecting ◽  
Block Chain ◽  
New Framework ◽  
Significant Factors

Life Cycle Assessment (LCA) is a widely recognized tool used to evaluate environmental impacts of a product or process, based on the environmental inventory database and bills of material. Data quality is one of the most significant factors affecting the analysis results. However, currently most datasets in inventory databases are generic i.e., they may represent material and energy flow of a process at market average, instead of a specific process used by a manufacturer. As a result, stockholders are unable to track their supply chain to find out the actual environmental impact from each supplier and to compare the environmental performance of alternative options. In this paper, we developed a new framework i.e., blockchain based LCA (BC-LCA), where block-chain technology is adapted to secure and transmit inventory data from upstream suppliers to downstream manufacturers. With BC-LCA, more specific data can be acquired along the supply chain in a real-time manner. Moreover, the availability, accuracy, privacy, and automatic update of inventory data can be improved. A case study is provided based on an industrial supply chain, to demonstrate the utilization of BC-LCA.

scholarly journals Integrated reverse supply chain model for food waste based on industry 4.0 revolutions

2021 ◽  
Vol 13 (4) ◽  
pp. 70-83
Author(s):  
Sharareh Mohajeri ◽  
Fatemeh Harsej ◽  
Mahboubeh Sadeghpour ◽  
Jahanfar Khaleghi Nia
Keyword(s):  
Supply Chain ◽  
Food Waste ◽  
Industry 4.0 ◽  
Waste Recycling ◽  
Appropriate Technology ◽  
Household Waste ◽  
Chain Model ◽  
Reverse Supply Chain ◽  
Whale Optimization ◽  
Multi Phase

The present research offeres a model to the advantage of operations for the food reverse supply chain by perfor-mancing Industry 4.0 Revolutions model of expanding a fuzzy multi-phase model for the food waste gathering reverse supply chain. This study introduces, a household waste recycling machine, which symbolizes the Industry 4.0 Revolutions. Also, electric-type vehicles have been considered for collection and delivery in accordance with the Industry 4.0 Revolutions. The rate of technology has been described in recycling stations. Several methods with different technologies to recycle food waste have been selected and assessed based on the Industry 4.0 Revolutions indicators. The food wastes are sent to recycling stations, that is places maintained, operated or used to store, buy or sell wastes before they recycled with appropriate technology. The understudy model is multi-objective, maximizing the benefit of recycling and customer response and minimizing the adverse effects of environmental pollution and transportation costs. In this research, the whale optimization algorithm is applied. The present work proposes an end-to-end solution for Reverse Supply Chain Management for food waste based on the Industry 4.0 Revolutions.

Using Big Data Analytics to Forecast Trade Volumes in Global Supply Chain Management

2019 ◽  
pp. 70-99
Author(s):  
Murat Ozemre ◽  
Ozgur Kabadurmus
Keyword(s):  
Big Data ◽  
International Trade ◽  
Supply Chain ◽  
Data Analytics ◽  
Open Data ◽  
Big Data Analytics ◽  
Global Supply Chain ◽  
Factors Affecting ◽  
Export Volume

As the supply chains become more global, the operations (such as procurement, production, warehousing, sales, and forecasting) must be managed with consideration of the global factors. International trade is one of these factors affecting the global supply chain operations. Estimating the future trade volumes of certain products for specific markets can help companies to adjust their own global supply chain operations and strategies. However, in today's competitive and complex global supply chain environments, making accurate forecasts has become significantly difficult. In this chapter, the authors present a novel big data analytics methodology to accurately forecast international trade volumes between countries for specific products. The methodology uses various open data sources and employs random forest and artificial neural networks. To demonstrate the effectiveness of their proposed methodology, the authors present a case study of forecasting the export volume of refrigerators and freezers from Turkey to United Kingdom. The results showed that the proposed methodology provides effective forecasts.

Reverse supply chain issues in Indian electronics industry: a case study

2018 ◽  
Vol 8 (3) ◽  
pp. 115-129 ◽  
Author(s):  
Saurabh Agrawal ◽  
Rajesh K. Singh ◽  
Qasim Murtaza
Keyword(s):  
Supply Chain ◽  
Electronics Industry ◽  
Reverse Supply Chain ◽  
Indian Electronics Industry

scholarly journals A comprehensive reverse supply chain model using an interactive fuzzy approach – A case study on the Vietnamese electronics industry

2019 ◽  
Vol 76 ◽  
pp. 87-108 ◽  
Author(s):  
Linh Thi Truc Doan ◽  
Yousef Amer ◽  
Sang-Heon Lee ◽  
Phan Nguyen Ky Phuc ◽  
Luu Quoc Dat
Keyword(s):  
Supply Chain ◽  
Electronics Industry ◽  
Chain Model ◽  
Fuzzy Approach ◽  
Reverse Supply Chain ◽  
Supply Chain Model
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