Global Logistics, Global Labor

2011 ◽  
pp. 211-230 ◽  
Author(s):  
Edna Bonacich ◽  
Gary G. Hamilton
Keyword(s):  
Global Logistics

RISK ASSESSMENT OF ARCTIC NAVIGATION BY USING IMPROVED FUZZY-AHP APPROACH

2021 ◽  
Vol 157 (A4) ◽  
Author(s):  
B Sahin ◽  
S Kum
Keyword(s):  
Fuzzy Ahp ◽  
Weather Conditions ◽  
Risk Identification ◽  
The Arctic ◽  
Risk Levels ◽  
Global Logistics ◽  
Geographical Position ◽  
Arctic Navigation ◽  
Hull Damage ◽  
Hierarchy Process

In this study, navigational risk factors of the Arctic Ocean are defined and numerical weights of each risk are obtained by using Improved Fuzzy Analytical Hierarchy Process (IF-AHP) method after conducting expert consultations. The Northern Sea Route shortens the maritime distance approximately 7000 nautical miles comparing to the conventional Suez Canal route. Therefore, it takes a significant role of being economic and time advantage for global logistics. Its geographical position, presence of ice, heavy weather conditions, strong currents and winds are some risks for Arctic transportation. There always have the possibility of unpredictable catastrophes such as a collision, grounding, hull damage and etc. in this region. Reflections of such unwanted incidents might be very costly for economic, political, environmental and safety concerns. Due to there are limited academic studies regarding to analytical and systematical risk identification and determination of risk levels, this study contributes to complete this academic gap.

scholarly journals Has Carbon Disclosure Become More Transparent in the Global Logistics Industry? An Investigation of Corporate Carbon Disclosure Strategies between 2010 and 2015

Logistics ◽  
2018 ◽  
Vol 2 (3) ◽  
pp. 13 ◽  
Author(s):  
David M. Herold
Keyword(s):  
Management Practices ◽  
Logistics Industry ◽  
Carbon Management ◽  
Related Information ◽  
External Pressures ◽  
Global Logistics ◽  
Disclosure Responses ◽  
Stakeholder Pressures ◽  
Over Time

Global logistics companies are increasingly disclosing carbon related information due to institutional and stakeholder pressures. Existing research, however, is limited to categorizing these pressures and their influences on corporate carbon disclosure strategies. In particular, literature to date has not distinguished between different carbon disclosure strategies and how they may have changed over time. In response, this paper: (1) proposes a framework that depicts four different carbon disclosure responses and strategies based on internal and external pressures; and (2) subsequently analyzes and compares corporate carbon disclosure strategies between 2010 and 2015. Using a sample of 39 leading global logistics companies, carbon disclosure strategies are categorized based on the analysis of 25 applied carbon management practices from Bloomberg ESG to see if carbon management practices and the associated strategies have changed. The findings show overall shifts to more transparent corporate carbon disclosure strategies between 2010 and 2015 with an increase of applied carbon management practices in both internal and external actions.

Customer centricity and guanxi prevalence as social capital: a study of international business relationships

2018 ◽  
Vol 33 (8) ◽  
pp. 1209-1220 ◽  
Author(s):  
Angeline Close Scheinbaum ◽  
Stephen W. Wang
Keyword(s):  
Structural Equation ◽  
Equation Modeling ◽  
Global Business ◽  
Global Marketing ◽  
Logistics Industry ◽  
Content Type ◽  
Global Logistics ◽  
High Level ◽  
Customer Centricity

Purpose This research blends perspectives of the Eastern phenomenon of guanxi with the more Western perspectives of relationship marketing and customer centricity. Extending scholarship on guanxi in marketing (e.g. Park and Luo, 2001; Sheu and Hu, 2009; Luo et al., 2008; Fowler and Reisenwitz, 2014), the objective is to highlight the indirect role of customer centricity (i.e. how visible or central it is for the business partner to communicate with/have information sharing with), for firms in regions with a prevalence of guanxi. Design/methodology/approach The empirical model is tested in context of global marketing in the business-to-business (B2B) logistics industry (n = 508). A total of 508 global logistics employees and managers with experience in global business participated in the survey in Taiwan. Structural equation modeling was used for data analysis with multi-group analyses. Findings Customer centricity intensifies positive outcomes of guanxi prevalence. Specifically, a high level of customer centricity strengthens established associations among guanxi prevalence, trust, relationship commitment and firm performance. Originality/value While most work on guanxi has a focus in China, this research focuses on Taiwan. While building on a wealth of literature, relatively less work has focused on customer centricity.

scholarly journals Analysis of the Impact of the Internet of Things Technology on the Development of the Transport and Logistics Market

2020 ◽  
pp. 74-82
Author(s):  
N. HRYNCHAK
Keyword(s):  
Internet Of Things ◽  
Information And Communication Technologies ◽  
Social Systems ◽  
Radical Change ◽  
Logistics Services ◽  
Global Logistics ◽  
Technology Applications ◽  
Logistics Market ◽  
The Impact ◽  
Internet Of Things Technology

Internet of Things (IoT) is becoming a technology of great importance in the current era of the international economy development, with essential impact on all the social systems. Logistics is a sector undergoing deep innovation-driven change in the latest decades. It was information and communication technologies (ICT) that greatly contributed in the growth of the logistics market. These ICT-specific innovations allow connected logistics services to generate big scopes of data and diverse information that can be stored and analyzed with high accuracy. On account of this, market analysts believe that IoT is capable to trigger radical change the way of supply chains, and predict the average nearly 30 per cent annual increase of the global logistics market after 2020. This raises the importance of analyses of various dimensions of IoT technology impact on the expansion of the transport and logistics services market.          The definition of the notion “Internet of Things” is given, with emphasizing its significance for the modern economy. The IoT contributions in enhancing the market effectiveness of the transport and logistics services market are highlighted. Statistical data on the perspectives of IoT technology applications in transport and logistics services are analyzed, showing that transport logistics is an industry leader by IoT spending; essential advantages created by IoT in the transport and logistics services are highlighted. Cases of effective use of IoT at company level worldwide are given, to confirm that transport and logistics is one of the most promising sectors for implementation of IoT.  

scholarly journals Computational Logistics for Container Terminal Handling Systems with Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Bin Li ◽  
Yuqing He
Keyword(s):  
Deep Learning ◽  
Container Terminal ◽  
Container Terminals ◽  
Hardware Platform ◽  
Logistics Network ◽  
Carbon Efficiency ◽  
The Past ◽  
Global Logistics ◽  
High Degree ◽  
Deep Learning Model

Container terminals are playing an increasingly important role in the global logistics network; however, the programming, planning, scheduling, and decision of the container terminal handling system (CTHS) all are provided with a high degree of nonlinearity, coupling, and complexity. Given that, a combination of computational logistics and deep learning, which is just about container terminal-oriented neural-physical fusion computation (CTO-NPFC), is proposed to discuss and explore the pattern recognition and regression analysis of CTHS. Because the liner berthing time (LBT) is the central index of terminal logistics service and carbon efficiency conditions and it is also the important foundation and guidance to task scheduling and resource allocation in CTHS, a deep learning model core computing architecture (DLM-CCA) for LBT prediction is presented to practice CTO-NPFC. Based on the quayside running data for the past five years at a typical container terminal in China, the deep neural networks model of the DLM-CCA is designed, implemented, executed, and evaluated with TensorFlow 2.3 and the specific feature extraction package of tsfresh. The DLM-CCA shows agile, efficient, flexible, and excellent forecasting performances for LBT with the low consuming costs on a common hardware platform. It interprets and demonstrates the feasibility and credibility of the philosophy, paradigm, architecture, and algorithm of CTO-NPFC preliminarily.

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