Improving Operations Performance through Artificial Intelligence, Digital, and Advanced Analytics Applications

2018 ◽  
Author(s):  
Anders Brun
Keyword(s):  
Artificial Intelligence ◽  
Advanced Analytics ◽  
Operations Performance

Familienunternehmen und Personalmaßnahmen in Krisenphasen

2021 ◽  
Author(s):  
Daniel Ludwig
Keyword(s):  
Artificial Intelligence ◽  
Research Methods ◽  
Quantitative Research ◽  
Family Businesses ◽  
Research Assistant ◽  
Quantitative Research Methods ◽  
Personnel Practices ◽  
Advanced Analytics

This work examines family and non-family businesses and their use of personnel practices in times of crisis. The detailed questions that it addresses are, firstly, whether these types of businesses, in connection with crisis indicators, exert an influence on the use of personnel practices. Secondly, the study clarifies whether there are differences between family and non-family businesses and to what extent this is influenced by varying crisis indicators. The author previously worked as a research assistant, during which time, in addition to the topics covered in this work, he was primarily concerned with quantitative research methods. Since completing his dissertation, he has been working in the field of advanced analytics and artificial intelligence.

The Nature of Intelligent Analytics

2021 ◽  
pp. 1-21
Author(s):  
Zhaohao Sun ◽  
Andrew Stranieri
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Research And Development ◽  
Business Intelligence ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Business Analytics ◽  
Novel Approach ◽  
Advanced Analytics

Intelligent analytics is an emerging paradigm in the age of big data, analytics, and artificial intelligence (AI). This chapter explores the nature of intelligent analytics. More specifically, this chapter identifies the foundations, cores, and applications of intelligent big data analytics based on the investigation into the state-of-the-art scholars' publications and market analysis of advanced analytics. Then it presents a workflow-based approach to big data analytics and technological foundations for intelligent big data analytics through examining intelligent big data analytics as an integration of AI and big data analytics. The chapter also presents a novel approach to extend intelligent big data analytics to intelligent analytics. The proposed approach in this chapter might facilitate research and development of intelligent analytics, big data analytics, business analytics, business intelligence, AI, and data science.

Emerging Technologies in Supply Management

2020 ◽  
Author(s):  
Andrea M. Prud’homme ◽  
John V. Gray ◽  
Andrew C. Barley
Keyword(s):  
Artificial Intelligence ◽  
Emerging Technologies ◽  
Supply Management ◽  
Process Automation ◽  
Robotic Process Automation ◽  
Management Processes ◽  
Virtual And Augmented Reality ◽  
Advanced Analytics ◽  
Speed And Accuracy ◽  
Positive Results

This chapter looks at emerging technologies and their use in supply management processes as a means to improve effectiveness through improved speed and accuracy, at a reduced cost. Many technologies are finding their way into supply management, with differing levels of penetration and application and with mixed results. It may be challenging for supply management professionals to understand how, when, and where these technologies are likely to yield positive results. This chapter reviews several technologies, including artificial intelligence/machine learning, big data/advanced analytics, blockchain, cloud computing, conversational things (e.g., chatbots), immersive technologies (e.g., virtual and augmented reality), and robotic process automation. Findings indicate that the primary advantages are achieved by improving current processes and workflows, rather than that these technologies are currently disrupting or will fundamentally change supply management. Another important finding is the importance of “clean data” inputs, something that artificial intelligence can help with and that is foundational for successful robotic process automation.

Profiting From Digital Transformation?

2021 ◽  
Vol 12 (5) ◽  
pp. 68-79
Author(s):  
Ulrich Lichtenthaler
Keyword(s):  
Artificial Intelligence ◽  
Digital Transformation ◽  
Superior Performance ◽  
Business Operations ◽  
Combining Data ◽  
New Knowledge ◽  
Business Opportunities ◽  
Recent Trends ◽  
Advanced Analytics

Many companies have recently started digital transformation initiatives, and they now increasingly focus on artificial intelligence (AI). By means of smart algorithms and advanced analytics, firms attempt to leverage some of the results of their ongoing digital transformation initiatives, for example with regard to data about their established business operations. A conceptual framework underscores the need for combining data management and AI initiatives in order to ensure a firm's digital readiness and to realize digital business opportunities subsequently. An overview of recent trends further illustrates how different companies respond to these managerial challenges. This paper contributes to the literature on digitalization, AI, and ‘integrated intelligence' by highlighting the role of AI for leveraging data from digital transformation initiatives. Specifically, the use of AI applications helps companies to turn data into valuable knowledge and intelligence. In addition, this paper provides new knowledge about achieving superior performance in the digital economy.

Beyond artificial intelligence: why companies need to go the extra step

2020 ◽  
Vol 41 (1) ◽  
pp. 19-26 ◽  
Author(s):  
Ulrich Lichtenthaler
Keyword(s):  
Artificial Intelligence ◽  
Design Methodology ◽  
Corporate Strategy ◽  
Cost Savings ◽  
Human Intelligence ◽  
Content Type ◽  
Positive Effects ◽  
Extra Step ◽  
Conceptual Paper ◽  
Advanced Analytics

Purpose The purpose of this paper is to underscore the need for developing a meta-intelligence in companies based on a conceptual framework for combining artificial and human intelligence. Design/methodology/approach This is a conceptual paper, which draws on the insights from extant theoretical and empirical research. Findings In light of a growing trend towards artificial intelligence (AI), most companies face substantial difficulties, which often derive from an excessive emphasis on merely replacing human intelligence by means of AI. To capture the complementary benefits of artificial and human intelligence beyond mere cost savings, firms do not only need to enhance their advanced analytics while continuing to develop their human intelligence. Rather, they additionally need a meta-intelligence for transforming their intelligence architecture in line with corporate strategy. Consequently, the firms need intelligence3 – comprising AI, human intelligence and the meta-intelligence. Originality/value The new concept of a meta-intelligence for renewing and recombining artificial and human intelligence helps to reconcile diverse findings in prior research. Without this meta-intelligence, most AI initiatives will be isolated endeavors, which may have positive effects but likely will not live up to the expectations.

scholarly journals Health Care Equity in the Use of Advanced Analytics and Artificial Intelligence Technologies in Primary Care

2021 ◽  
Author(s):  
Cheryl R. Clark ◽  
Consuelo Hopkins Wilkins ◽  
Jorge A. Rodriguez ◽  
Anita M. Preininger ◽  
Joyce Harris ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Primary Care ◽  
Health Care ◽  
Sociodemographic Factors ◽  
Health Care Equity ◽  
Practice Of Medicine ◽  
Race Ethnicity ◽  
Advanced Analytics ◽  
Healthcare Equity ◽  
Inform Decision Making

AbstractThe integration of advanced analytics and artificial intelligence (AI) technologies into the practice of medicine holds much promise. Yet, the opportunity to leverage these tools carries with it an equal responsibility to ensure that principles of equity are incorporated into their implementation and use. Without such efforts, tools will potentially reflect the myriad of ways in which data, algorithmic, and analytic biases can be produced, with the potential to widen inequities by race, ethnicity, gender, and other sociodemographic factors implicated in disparate health outcomes. We propose a set of strategic assertions to examine before, during, and after adoption of these technologies in order to facilitate healthcare equity across all patient population groups. The purpose is to enable generalists to promote engagement with technology companies and co-create, promote, or support innovation and insights that can potentially inform decision-making and health care equity.

Automated Drilling Data Quality Control Using Application of AI Technologies

2021 ◽  
Author(s):  
Francesco Battocchio ◽  
Jaijith Sreekantan ◽  
Arghad Arnaout ◽  
Abed Benaichouche ◽  
Juma Sulaiman Al Shamsi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Quality Control ◽  
Data Quality ◽  
Information Loss ◽  
Sensor Data ◽  
Data Quality Control ◽  
Drilling Data ◽  
Advanced Analytics ◽  
Quality Issues

Abstract Drilling data quality is notoriously a challenge for any analytics application, due to complexity of the real-time data acquisition system which routinely generates: (i) Time related issues caused by irregular sampling, (ii) Channel related issues in terms of non-uniform names and units, missing or wrong values, and (iii) Depth related issues caused block position resets, and depth compensation (for floating rigs). On the other hand, artificial intelligence drilling applications typically require a consistent stream of high-quality data as an input for their algorithms, as well as for visualization. In this work we present an automated workflow enhanced by data driven techniques that resolves complex quality issues, harmonize sensor drilling data, and report the quality of the dataset to be used for advanced analytics. The approach proposes an automated data quality workflow which formalizes the characteristics, requirements and constraints of sensor data within the context of drilling operations. The workflow leverages machine learning algorithms, statistics, signal processing and rule-based engines for detection of data quality issues including error values, outliers, bias, drifts, noise, and missing values. Further, once data quality issues are classified, they are scored and treated on a context specific basis in order to recover the maximum volume of data while avoiding information loss. This results into a data quality and preparation engine that organizes drilling data for further advanced analytics, and reports the quality of the dataset through key performance indicators. This novel data processing workflow allowed to recover more than 90% of a drilling dataset made of 18 offshore wells, that otherwise could not be used for analytics. This was achieved by resolving specific issues including, resampling timeseries with gaps and different sampling rates, smart imputation of wrong/missing data while preserving consistency of dataset across all channels. Additional improvement would include recovering data values that felt outside a meaningful range because of sensor drifting or depth resets. The present work automates the end-to-end workflow for data quality control of drilling sensor data leveraging advanced Artificial Intelligence (AI) algorithms. It allows to detect and classify patterns of wrong/missing data, and to recover them through a context driven approach that prevents information loss. As a result, the maximum amount of data is recovered for artificial intelligence drilling applications. The workflow also enables optimal time synchronization of different sensors streaming data at different frequencies, within discontinuous time intervals.

Hope for Inoperable Kidney Cancer (Vol. 25, No. 6, Full Issue)

2021 ◽  
Vol 25 (06) ◽  
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Clinical Trial ◽  
Early Detection ◽  
Kidney Cancer ◽  
Vice President ◽  
World Health ◽  
Radiation Oncologist ◽  
Trial Process ◽  
Advanced Analytics

For the month of June 2020, in our Features section, we have an article contribution by Dr Jonathan Teh a Consultant Radiation Oncologist at Asian Alliance Radiation & Oncology (AARO) on how SBRT can provide hope for inoperable kidney cancer. Also in the Features section, we commemorate World Health Day with Viatris. In the Columns section, look at how the microbiome can be a gateway to wellness in an article contribution by Daniel Ramón Vidal, Vice President of R&D Health & Wellness at ADM. Also, explore how artificial intelligence and advanced analytics can help in enhancing clinical trial process. In the Spotlights section, we interviewed Jeong Jae Youn, Country Manager for GE Healthcare Singapore & Emerging ASEAN to dive into mammograms and ultrasound methods used for early detection of signs of breast cancer.

scholarly journals Space Computation Sciences Leveraging Cloud Computing, Artificial Intelligence, and Advanced Analytics to Power Innovation: A Review Report

2018 ◽  
Vol 5 (2) ◽  
pp. 89-90
Author(s):  
Takudzwa Fadziso
Keyword(s):  
Artificial Intelligence ◽  
Data Analytics ◽  
Technological Innovations ◽  
Space Technology ◽  
The Moon ◽  
Space Industry ◽  
New Space ◽  
Advanced Analytics ◽  
Cloud Technologies ◽  
Modern Technologies

In order to realize the goal of exploring and discovering new worlds, technological innovations are important. The space industry has been growing since the first space ship landed on the moon in the Apollo 11 mission. Huge leaps in space technology experienced since then combined with modern technologies such Artificial Intelligence, Data Analytics and Cloud technology can help space explorers to go further in space and explore territories not reached before, and potentially uncover new space phenomena. The objective of this research is to explore how space computation sciences can leverage Artificial Intelligence, Cloud technologies, and Advanced Data Analysis to power innovations.

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