Optimizing decision making in the apparel supply chain using artificial intelligence (AI)

2013 ◽  
Author(s):  
W. K. Wong ◽  
Z. X. Guo ◽  
S. Y. S. Leung
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Supply Chain

Port Dada Integration

2021 ◽  
pp. 1-22
Author(s):  
José Luís Cacho ◽  
Adalberto Tokarski ◽  
Elizabete Thomas ◽  
Valentina Chkoniya
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Cloud Computing ◽  
Supply Chain ◽  
Data Integration ◽  
Data Science ◽  
Just In Time ◽  
Use Of Data ◽  
Digital Infrastructure ◽  
Digital Transition

The maritime supply chain is growing in complexity. Ports are at the crossroads of many activities, modes, and stakeholders, and are actively becoming digital hubs. Today, digital and physical connectivity go hand in hand. The port could benefit from taping the opportunities arising from digitalization and data integration since it helps to leverage external knowledge, engage stakeholders, create new decision-making anchors, lower the risk of certain investments, boost productivity and cut costs, and accelerate greening and digital transition, generating possibilities for just-in-time operations and optimizations. The chapter aims to apprehend the use of data science in the port sector. The state of the art in Brazil and Portugal are different. Even inside Brazil, there is no homogeneity of ports in the usage of digital infrastructure, cloud computing, or artificial intelligence. The existing inequalities hinder general cooperation between nations but, at the same time, reveal opportunities to approach specific nodes in the international supply chain.

Building supply-chain resilience: an artificial intelligence-based technique and decision-making framework

2021 ◽  
pp. 1-21
Author(s):  
Amine Belhadi ◽  
Sachin Kamble ◽  
Samuel Fosso Wamba ◽  
Maciel M. Queiroz
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Supply Chain ◽  
Supply Chain Resilience ◽  
Decision Making Framework

Blockchain in Supply Chain: Case Study of Lottemart Ciputat

2019 ◽  
Vol 5 (1) ◽  
pp. 38-49 ◽  
Author(s):  
B. K. Handoyo ◽  
M. R. Mashudi ◽  
H. P. Ipung
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Information Asymmetry ◽  
New Technology ◽  
Business Case ◽  
Analysis Method ◽  
Chain Management ◽  
Production Processes

Current supply chain methods are having difficulties in resolving problems arising from the lack of trust in supply chains. The root reason lies in two challenges brought to the traditional mechanism: self-interests of supply chain members and information asymmetry in production processes. Blockchain is a promising technology to address these problems. The key objective of this paper is to present qualitative analysis for blockchain in supply chain as the decision-making framework to implement this new technology. The analysis method used Val IT business case framework, validated by the expert judgements. The further study needs to be elaborated by either the existing organization that use blockchain or assessment by the organization that will use blockchain to improve their supply chain management.

Post COVID-19 Potentials and Innovation: The Future Supply Chain

2020 ◽  
Vol 11 (SPL1) ◽  
pp. 1054-1057
Author(s):  
Bindu Swetha Pasuluri ◽  
Anuradha S G ◽  
Manga J ◽  
Deepak Karanam
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Supply Chain ◽  
Supply Chains ◽  
World Health Organization ◽  
World Wide ◽  
World Health ◽  
Future Supply ◽  
Entire World ◽  
Health Organization

An unanticipated outburst of pneumonia of inexperienced in Wuhan, , China stated in December 2019. World health organization has recognized pathogen and termed it COVID-19. COVID-19 turned out to be a severe urgency in the entire world. The influence of this viral syndrome is now an intensifying concern. Covid-19 has changed our mutual calculus of ambiguity. It is more world-wide in possibility, more deeply , and much more difficult than any catastrophe that countries and organizations have ever faced. The next normal requires challenging ambiguity head-on and building it into decision-making. It is examined that every entity involved in running supply chains would require through major as employee, product, facility protocols, and transport would have to be in place. It is an urgent need of structuring to apply the lessons well-read for our supply chain setup. With higher managers now being aware of the intrinsic hazards in their supply chain, key and suggestions-recommendations will help to guide leader to commit to a newly planned, more consistent supply chain setup. Besides, the employees’ mental health is also a great concern.

Perceived Effectiveness of Artificial Intelligence based Decision Support System in a Clinical Setting: A Systematic Literature Review (Preprint)

2020 ◽  
Author(s):  
Avishek Choudhury
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Literature Review ◽  
Decision Support System ◽  
Support System ◽  
Clinical Setting ◽  
Clinical Decision Making ◽  
Theoretical Perspective ◽  
Care Providers

UNSTRUCTURED Objective: The potential benefits of artificial intelligence based decision support system (AI-DSS) from a theoretical perspective are well documented and perceived by researchers but there is a lack of evidence showing its influence on routine clinical practice and how its perceived by care providers. Since the effectiveness of AI systems depends on data quality, implementation, and interpretation. The purpose of this literature review is to analyze the effectiveness of AI-DSS in clinical setting and understand its influence on clinician’s decision making outcome. Materials and Methods: This review protocol follows the Preferred Reporting Items for Systematic Reviews and Meta- Analyses reporting guidelines. Literature will be identified using a multi-database search strategy developed in consultation with a librarian. The proposed screening process consists of a title and abstract scan, followed by a full-text review by two reviewers to determine the eligibility of articles. Studies outlining application of AI based decision support system in a clinical setting and its impact on clinician’s decision making, will be included. A tabular synthesis of the general study details will be provided, as well as a narrative synthesis of the extracted data, organised into themes. Studies solely reporting AI accuracy an but not implemented in a clinical setting to measure its influence on clinical decision making were excluded from further review. Results: We identified 8 eligible studies that implemented AI-DSS in a clinical setting to facilitate decisions concerning prostate cancer, post traumatic stress disorder, cardiac ailment, back pain, and others. Five (62.50%) out of 8 studies reported positive outcome of AI-DSS. Conclusion: The systematic review indicated that AI-enabled decision support systems, when implemented in a clinical setting and used by clinicians might not ensure enhanced decision making. However, there are very limited studies to confirm the claim that AI based decision support system can uplift clinicians decision making abilities.

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

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