Development and Evaluation of a New Packaging System for Fresh Produce: a Case Study on Fresh Cherries Under Global Supply Chain Conditions

2014 ◽  
Vol 8 (3) ◽  
pp. 655-669 ◽  
Author(s):  
Georgios Koutsimanis ◽  
Janice Harte ◽  
Eva Almenar
Keyword(s):  
Supply Chain ◽  
Fresh Produce ◽  
Global Supply Chain ◽  
Packaging System ◽  
Chain Conditions

Industry 4.0: The Case-Study of a Global Supply Chain Company

2021 ◽  
pp. 483-498
Author(s):  
Cezar Honorato ◽  
Francisco Cristovão Lourenço de Melo
Keyword(s):  
Supply Chain ◽  
Industry 4.0 ◽  
Global Supply Chain

scholarly journals One to Many: A Framework for Scaling Risk Mitigation Assurance

2018 ◽  
pp. 280-290
Author(s):  
Joel Bigley
Keyword(s):  
Supply Chain ◽  
Qualitative Case Study ◽  
Risk Mitigation ◽  
Physical Property ◽  
Global Supply Chain ◽  
Global Organization ◽  
A Priority ◽  
Single Facility ◽  
Overall Risk

The lack of vulnerability control leads to organizational harm in global businesses when the exploitation of a weakness occurs. How can a global supply chain protect itself from vulnerabilities that can damage their brand and their physical property? In this qualitative case study the author shows how a multi-site corporation can use a method to learn what the vulnerabilities are in a single facility and use a framework to methodically scale the controls needed to mitigate vulnerabilities across the supply chain. This framework allows leadership to keep cost in mind so that the highest vulnerabilities are mitigated as a priority. A team of security experts was used to capture data in framework used for analysis. This framework was then used strategically to reduce the overall risk of threat vectors in the global organization.

scholarly journals Selection of warehouse location for a global supply chain: A case study

2018 ◽  
Vol 30 (4) ◽  
pp. 343-356 ◽  
Author(s):  
Rajesh Kr Singh ◽  
Nikhil Chaudhary ◽  
Nikhil Saxena
Keyword(s):  
Supply Chain ◽  
Global Supply Chain ◽  
Warehouse Location ◽  
Selection Of

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.

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

2022 ◽  
pp. 921-944
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.

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