scholarly journals Achieving Resilience and Business Sustainability during COVID-19: The Role of Lean Supply Chain Practices and Digitalization

2021 ◽  
Vol 13 (22) ◽  
pp. 12369
Author(s):  
Matteo Trabucco ◽  
Pietro De Giovanni
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Supply Chain ◽  
Customer Service ◽  
Mobile Apps ◽  
Production Costs ◽  
Sustainable Business ◽  
Business Sustainability ◽  
Logistic Regression Models

This paper investigates how firms can enjoy a sustainable business even during the COVID-19 pandemic. The adoption of lean coordination mechanisms over the supply chain (SC) and lean approaches in omnichannel strategies can guarantee the business sustainability and resilience. Furthermore, we investigate whether business sustainability, along with digitalization through mobile apps, Artificial Intelligence systems, and Big Data and Machine Learning enable firms’ resilience. We first explore the background on the subject, identify the research gap, and develop some research hypotheses to be tested. Then, we present the data collection process and the sample, which finally consists of firms from different sectors, including retailing, electronics, pharmaceutics, and agriculture. Several logistic regression models are developed and estimated to generate findings and managerial insights. Our results show that a lean omnichannel approach is an effective practice to preserve production costs, SC visibility, inventory available over the SC, and sales. Furthermore, lean coordination with contracts can make a business sustainable by preserving quality, ROI, production costs, customer service, and inventory availability. Finally, firms can be highly sustainable through resilience when they engage in sustainable ROI, SC visibility, and sales; in contrast, the adoption of mobile apps worsens firms’ resilience, which is not influenced by Artificial Intelligence and Big Data and Machine Learning.

scholarly journals Reimagining the Future of Healthcare Industry through Internet of Medical Things (IoMT), Artificial Intelligence (AI), Machine Learning (ML), Big Data, Mobile Apps and Advanced Sensors

2019 ◽  
Vol 9 (1) ◽  
pp. 3014-3019
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Science Fiction ◽  
Medical Devices ◽  
Mobile Apps ◽  
The Internet ◽  
Healthcare Industry ◽  
Remote Locations ◽  
Internet Of Medical Things

From Bluetooth enabled hearing aids to robotic caretakers, wearable and smart devices industries are immensely contributing to the development of the healthcare industry with the help of Internet of Things (IoT). Latest technologies like Artificial Intelligence, 3D Printing, Big data, Machine Learning, Advanced Sensors, Mobile Applications and other technologies will continue to generate lot of opportunities for Medtech organizations. Some of the latest healthcare innovations practiced at present might have been seen or read by some of us only in science fiction movies or science fiction stories a long ago. Presently, IoT and Artificial Intelligence is creating a revolution in healthcare industry when it comes to diagnosis and treatment of varied diseases. From smartphones to robots, artificial intelligence is already making its presence felt in healthcare industry and as such it is progressively recognizing the transformative nature of IoT technologies which drives innovation in the development of connected medical devices. Gradual increase in the number of connected medical devices with the advent of technology advancements helps to capture and transmit medical related data wherever and whenever required to the concerned people and thus, it gave birth to the Internet of Medical Things (IoMT), where the Internet of Things (IoT) and healthcare meet. The IoMT helps to constantly monitor and alter (if required) the behvaiour of the patient and his/her health status in real time and also supports healthcare organizations to effectively streamline clinical processes, patient information and related work flows to enhance its operational productivity. The IoMT has made and continues to make the delivery of P4 Medicine (Predictive, Preventive, Personalized and Participatory) even for remote locations with the help of connected sensors and devices helping in real-time patient care. IoMT helps doctors and caregivers to provide patient care and support by constantly monitoring data related to patients through mobile apps and connected medical devices even when patient(s) or doctor(s) are located at remote locations. This research paper discusses about six use cases explaining how IoMT is applied in healthcare industry.

Impact of Supply Chain Digitalisation on a Connected Global Market

2020 ◽  
pp. 186-204
Author(s):  
Farooq Habib ◽  
Murtaza Farooq Khan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Final Section ◽  
Digital Technologies ◽  
Global Market ◽  
The Third ◽  
The Impact

This chapter focuses on the impact of supply chain digitalisation on a connected global market. The first section focuses on the dynamic consumer requirements and preferences. The second section appraised the segmentation and mapping of digital technologies. The third section examines the contemporary application of digital technologies including: big data, blockchains, artificial intelligence, machine learning, and data analytics. The final section analysises the rules and regulations the form the basis of a contemporary framework for the governance of digital technologies.

scholarly journals Blockchain Disrupting Fintech and the Banking System

Proceedings ◽  
2021 ◽  
Vol 74 (1) ◽  
pp. 24
Author(s):  
Eduard Alexandru Stoica ◽  
Daria Maria Sitea
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Financial Institutions ◽  
New World ◽  
Great Part ◽  
Banking System ◽  
Daily Activities ◽  
Blockchain Technology ◽  
Almost All

Nowadays society is profoundly changed by technology, velocity and productivity. While individuals are not yet prepared for holographic connection with banks or financial institutions, other innovative technologies have been adopted. Lately, a new world has been launched, personalized and adapted to reality. It has emerged and started to govern almost all daily activities due to the five key elements that are foundations of the technology: machine to machine (M2M), internet of things (IoT), big data, machine learning and artificial intelligence (AI). Competitive innovations are now on the market, helping with the connection between investors and borrowers—notably crowdfunding and peer-to-peer lending. Blockchain technology is now enjoying great popularity. Thus, a great part of the focus of this research paper is on Elrond. The outcomes highlight the relevance of technology in digital finance.

scholarly journals Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa

2021 ◽  
Vol 18 (15) ◽  
pp. 7890
Author(s):  
Bruce Mellado ◽  
Jianhong Wu ◽  
Jude Dzevela Kong ◽  
Nicola Luigi Bragazzi ◽  
Ali Asgary ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Developed Countries ◽  
Machine Learning Techniques ◽  
Multiple Data ◽  
Learning Techniques ◽  
Unequal Access ◽  
Roll Out

COVID-19 is imposing massive health, social and economic costs. While many developed countries have started vaccinating, most African nations are waiting for vaccine stocks to be allocated and are using clinical public health (CPH) strategies to control the pandemic. The emergence of variants of concern (VOC), unequal access to the vaccine supply and locally specific logistical and vaccine delivery parameters, add complexity to national CPH strategies and amplify the urgent need for effective CPH policies. Big data and artificial intelligence machine learning techniques and collaborations can be instrumental in an accurate, timely, locally nuanced analysis of multiple data sources to inform CPH decision-making, vaccination strategies and their staged roll-out. The Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC) has been established to develop and employ machine learning techniques to design CPH strategies in Africa, which requires ongoing collaboration, testing and development to maximize the equity and effectiveness of COVID-19-related CPH interventions.

Innovative Approach to Build a Scalable Hybrid Model for Prescriptive Maintenance

2021 ◽  
Author(s):  
Richard Büssow ◽  
Bruno Hain ◽  
Ismael Al Nuaimi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Hybrid Model ◽  
Domain Knowledge ◽  
Spare Parts ◽  
Good Prediction ◽  
First Principle ◽  
Plant Data ◽  
Rotating Equipment

Abstract Objective and Scope Analysis of operational plant data needs experts in order to interpret detected anomalies which are defined as unusual operation points. The next step on the digital transformation journey is to provide actionable insights into the data. Prescriptive Maintenance defines in advance which kind of detailed maintenance and spare parts will be required. This paper details requirements to improve these predictions for rotating equipment and show potential to integrate the outcome into an operational workflow. Methods, Procedures, Process First principle or physics-based modelling provides additional insights into the data, since the results are directly interpretable. However, such approaches are typically assumed to be expensive to build and not scalable. Identification of and focus on the relevant equipment to be modeled in a hybrid model using a combination of first principle physics and machine learning is a successful strategy. The model is trained using a machine learning approach with historic or current real plant data, to predict conditions which have not occurred before. The better the Artificial Intelligence is trained, the better the prediction will be. Results, Observations, Conclusions The general aim when operating a plant is the actual usage of operational data for process and maintenance optimization by advanced analytics. Typically a data-driven central oversight function supports operations and maintenance staff. A major lesson-learned is that the results of a rather simple statistical approach to detect anomalies fall behind the expectations and are too labor intensive. It is a widely spread misinterpretation that being able to deal with big data is sufficient to come up with good prediction quality for Prescriptive Maintenance. What big data companies are normally missing is domain knowledge, especially on plant critical rotating equipment. Without having domain knowledge the relevant input into the model will have shortcomings and hence the same will apply to its predictions. This paper gives an example of a refinery where the described hybrid model has been used. Novel and Additive Information First principle models are typically expensive to build and not scalable. This hybrid model approach, combining first principle physics based models with artificial intelligence and integration into an operational workflow shows a new way forward.

Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

Data Science: Big Data, Machine Learning, and Artificial Intelligence

2018 ◽  
Vol 15 (3) ◽  
pp. 497-498 ◽  
Author(s):  
Ruth C. Carlos ◽  
Charles E. Kahn ◽  
Safwan Halabi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Data Science

Machinic Empathy and Mental Health: The Relational Ethics of Machine Empathy and Artificial Intelligence in Her

2019 ◽  
pp. 158-175
Author(s):  
Frances Shaw
Keyword(s):  
Mental Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Health Research ◽  
Mental Health Treatment ◽  
Mental Health Research ◽  
Relational Ethics ◽  
Health Treatment

This paper situates a discussion of Her within contemporary developments in empathic machine learning for mental health treatment and therapy. Her simultaneously hooks into and critiques a particular imaginary about what artificial intelligence can do when combined with big data. Shaw threads the representation of empathy and artificial intelligence in the film into discussions of contemporary mental health research, in particular possibilities for the automation of treatment, whether through machine learning or guided interventions. Her provides some useful ways to think through utopian, dystopian, and ambivalent readings of such applications of technology in a broader sense, raising questions about sincerity and loss of human connectivity, relational ethics and automated empathy.

Sign in / Sign up

Export Citation Format

Share Document