Classifying Attitudes of Thai Business Organizations Toward the Perceived Benefit of Customer Predictive Analytics

2018 ◽  
Author(s):  
Santisook Limpeeticharoenchot ◽  
Nagul Cooharojananone ◽  
Thira Chavarnakul ◽  
Kanokwan Atchariyachanvanich
Keyword(s):  
Predictive Analytics ◽  
Business Organizations ◽  
Perceived Benefit ◽  
Thai Business

Predictive Analytics in Operations Management

2016 ◽  
pp. 68-92
Author(s):  
Harsh Jain ◽  
Amrit Pal ◽  
Manish Kumar
Keyword(s):  
Operations Management ◽  
Predictive Analytics ◽  
Operation Management ◽  
Business Organizations ◽  
Process Data ◽  
Huge Amount ◽  
Large Size ◽  
Heterogeneous Processing ◽  
Computing Framework ◽  
Day To Day Operations

Operations management is a field of management which emphasizes on managing the day to day operations of business organizations. These organizations possess a huge amount of data which needs to be analysed for proper functioning of business. This large amount of data keeps some useful information hidden inside it, which needs to be uncovered. This information can be retrieved using predictive analytics techniques, which predict the patterns hidden inside the data. This data is heterogeneous, processing of such huge amount of data creates challenges for the existing technologies. MapReduce is very efficient in processing this huge amount of data. In the field of operation management, data needs to be processed efficiently, so it is highly required to process data using parallel computing framework due to its large size. This chapter covers different techniques of predictive analytics based on MapReduce framework which helps in implementing the techniques on a parallel framework.

Predictive Analytics in Operations Management

2017 ◽  
pp. 1205-1223
Author(s):  
Harsh Jain ◽  
Amrit Pal ◽  
Manish Kumar
Keyword(s):  
Operations Management ◽  
Predictive Analytics ◽  
Operation Management ◽  
Business Organizations ◽  
Process Data ◽  
Huge Amount ◽  
Large Size ◽  
Heterogeneous Processing ◽  
Computing Framework ◽  
Day To Day Operations

Operations management is a field of management which emphasizes on managing the day to day operations of business organizations. These organizations possess a huge amount of data which needs to be analysed for proper functioning of business. This large amount of data keeps some useful information hidden inside it, which needs to be uncovered. This information can be retrieved using predictive analytics techniques, which predict the patterns hidden inside the data. This data is heterogeneous, processing of such huge amount of data creates challenges for the existing technologies. MapReduce is very efficient in processing this huge amount of data. In the field of operation management, data needs to be processed efficiently, so it is highly required to process data using parallel computing framework due to its large size. This chapter covers different techniques of predictive analytics based on MapReduce framework which helps in implementing the techniques on a parallel framework.

scholarly journals Managing the Training Process in Elite Sports: From Descriptive to Prescriptive Data Analytics

2021 ◽  
pp. 1-5
Author(s):  
Kobe C. Houtmeyers ◽  
Arne Jaspers ◽  
Pedro Figueiredo
Keyword(s):  
Data Analytics ◽  
Predictive Analytics ◽  
Business Analytics ◽  
Business Organizations ◽  
Elite Sport ◽  
Training Process ◽  
Use Of Data ◽  
Prescriptive Analytics ◽  
And Performance ◽  
Conceptual Data

Elite sport practitioners increasingly use data to support training process decisions related to athletes’ health and performance. A careful application of data analytics is essential to gain valuable insights and recommendations that can guide decision making. In business organizations, data analytics are developed based on conceptual data analytics frameworks. The translation of such a framework to elite sport may benefit the use of data to support training process decisions. Purpose: The authors aim to present and discuss a conceptual data analytics framework, based on a taxonomy used in business analytics literature to help develop data analytics within elite sport organizations. Conclusions: The presented framework consists of 4 analytical steps structured by value and difficulty/complexity. While descriptive (step 1) and diagnostic analytics (step 2) focus on understanding the past training process, predictive (step 3) and prescriptive analytics (step 4) provide more guidance in planning the future. Although descriptive, diagnostic, and predictive analytics generate insights to inform decisions, prescriptive analytics can be used to drive decisions. However, the application of this type of advanced analytics is still challenging in elite sport. Thus, the current use of data in elite sport is more focused on informing decisions rather than driving them. The presented conceptual framework may help practitioners develop their analytical reasoning by providing new insights and guidance and may stimulate future collaborations between practitioners, researchers, and analytics experts.

Perceived benefit and burden of psychosocial research with children undergoing treatment for cancer, NF-1, or HIV

2010 ◽  
Author(s):  
Lori Wiener ◽  
Abigail Bosk ◽  
Brigitte Widemann ◽  
Rohan Hazra ◽  
Alan Wayne ◽  
...  
Keyword(s):  
Research With Children ◽  
Perceived Benefit

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

Creating adaptive predictions for packaging-critical quality parameters using advanced analytics and machine learning

TAPPI Journal ◽  
2019 ◽  
Vol 18 (11) ◽  
pp. 679-689
Author(s):  
CYDNEY RECHTIN ◽  
CHITTA RANJAN ◽  
ANTHONY LEWIS ◽  
BETH ANN ZARKO
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Predictive Analytics ◽  
Paper Industry ◽  
Quality Parameters ◽  
Fast Change ◽  
The Face ◽  
Model Tuning ◽  
Reducing Costs ◽  
Time Critical

Packaging manufacturers are challenged to achieve consistent strength targets and maximize production while reducing costs through smarter fiber utilization, chemical optimization, energy reduction, and more. With innovative instrumentation readily accessible, mills are collecting vast amounts of data that provide them with ever increasing visibility into their processes. Turning this visibility into actionable insight is key to successfully exceeding customer expectations and reducing costs. Predictive analytics supported by machine learning can provide real-time quality measures that remain robust and accurate in the face of changing machine conditions. These adaptive quality “soft sensors” allow for more informed, on-the-fly process changes; fast change detection; and process control optimization without requiring periodic model tuning. The use of predictive modeling in the paper industry has increased in recent years; however, little attention has been given to packaging finished quality. The use of machine learning to maintain prediction relevancy under everchanging machine conditions is novel. In this paper, we demonstrate the process of establishing real-time, adaptive quality predictions in an industry focused on reel-to-reel quality control, and we discuss the value created through the availability and use of real-time critical quality.

Predictive Analytics erfolgreich implementieren

Controlling ◽  
2020 ◽  
Vol 32 (1) ◽  
pp. 58-64
Author(s):  
Daniel Schlatter ◽  
Christopher Stoll ◽  
Klaus Möller
Keyword(s):  
Predictive Analytics

Trotz deutlicher technologischer Fortschritte wird Predictive Analytics in der Praxis noch immer nur selten für die finanzielle Prognose eingesetzt. Notwendig für eine erfolgreiche Anwendung ist ein ganzheitlicher Ansatz bei der Implementierung, der über die rein technisch „richtige“ Anwendung hinausgeht. Aus der Analyse verschiedener Implementierungsprojekte wurden daher die Erfolgsfaktoren für Predictive Analytics Projekte abgeleitet und in einem ganzheitlichen Konzept zusammengefasst. Damit können Verbesserungen in den Bereichen Prognosegenauigkeit, Ressourceneinsatz und Steuerungswirkung realisiert werden.

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