The power of prediction: predictive analytics, workplace complements, and business performance

Author(s):  
Erik Brynjolfsson ◽  
Wang Jin ◽  
Kristina McElheran
2018 ◽  
Vol Volume-2 (Issue-2) ◽  
pp. 1046-1050
Author(s):  
Supriya V. Pawar ◽  
Gireesh Kumar ◽  
Eashan Deshmukh ◽  

Author(s):  
Himani Singal ◽  
Shruti Kohli

There is a remarkable association between an organization's analytics intricacy and its competitive enactment. The biggest problem to adopting analytics is the lack of knowledge of using it to improve business performance. A website is believed and considered as ‘face' of the company. In present era, there are more than 200 million people who buy goods online across the globe. Business Analytics helps companies to find the most profitable customer and allows them to justify their marketing effort, especially when the competition is very high. Predictive analytics helps organizations to predict churn, default in loan payment, brand switch, insurance loss and even the outcome in a football match. There is ample evidence from the corporate world that the ability to make better decisions (by management executives) improves with analytical skills. This chapter will provide an in-depth knowledge of business analytic techniques and their applications in improving business processes and decision-making.


2018 ◽  
Vol Volume-2 (Issue-2) ◽  
pp. 380-382
Author(s):  
Supriya V. Pawar ◽  
Gireesh Kumar ◽  
Eashan Deshmukh ◽  

Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel

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.


2019 ◽  
Vol 9 (1) ◽  
pp. 53-66 ◽  
Author(s):  
Dandan Irawan

Basically a natural partnership will achieve its goal if mutual requirements, mutual reinforcement, and mutual benefit can be maintained and made a strong fundamental commitment among partners. Nevertheless the development seems very slow. The cause is the presence of specific and different conditions and structure factors compared to other countries. Along with that, we still encounter various forms of gaps, such as inequality among regions, among income groups, between sectors, among economic actors, and so forth. The next problem is that in business entities including cooperatives and micro and small enterprises in running their business activities requires business partnerships with medium and large enterprises in order to improve business performance and business scale. While on the other hand our economic conditions and structures are not yet fully conducive to fostering partnerships based on purely business considerations or competitive market motivations but the business partnership of the foundation is strong enough in our country's constitution. Partnerships will work if partners are equally benefiting. Our concept of partnership is like that, although in the short term, there is a party or a party benefiting more from the other side.


TAPPI Journal ◽  
2019 ◽  
Vol 18 (11) ◽  
pp. 679-689
Author(s):  
CYDNEY RECHTIN ◽  
CHITTA RANJAN ◽  
ANTHONY LEWIS ◽  
BETH ANN ZARKO

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.


Sign in / Sign up

Export Citation Format

Share Document