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About 10,000 papers for “Predictive analytics”

Predictive Analytics

Maximilian Stichter · Controlling · 2013

Month by month, Controlling - Zeitschrift für erfolgsorientierte Unternehmenssteuerung publishes peer-reviewed, applied research contributions for business management, accounting and reporting. Key elements of succesful corporate controlling are presented in an analytic, well-structured manner.

Cited by 1

Predictive Analytics

A. S. Anurag, M. Johnpaul · Advances in computational intelligence and robotics book series · 2024

Supply chain management (SCM) is a critical management function in any organization. After the pandemic in 2019, the global supply chain faced drastic disruptions, compelling policymakers worldwide to seek ways to manage them. Integrating artificial intelligence (AI) technologies into supply chain processes has revolutionized it. Predictive analytics is a vital tool that helps supply chain managers anticipate future trends and make informed decisions. Based on statistical principles and machine learning algorithms, predictive analytics encompasses techniques such as linear regression, decision trees, cluster analysis, time series models, and logistic regression. Unlike traditional supply chains, which often lack visibility, supply chains integrated with predictive analytics offer enhanced transparency. Mastering these intelligent technologies becomes imperative for organizations seeking to thrive in today's dynamic market landscape. This chapter provides readers with a comprehensive grasp of predictive analytics and its indispensable role in SCM processes.

Cited by 0

Predictive Analytics

P. S. Venkateswaran, Sriramkumar Mm · Advances in marketing, customer relationship management, and e-services book series · 2025

Predictive analytics, leveraging machine learning and big data, has emerged as a transformative tool in business and consumer behavior forecasting. This study explores the application of predictive models to anticipate market trends, optimize decision-making, and enhance customer engagement. Integrating machine learning algorithms with big data enables businesses to analyze vast amounts of structured and unstructured data, uncovering patterns that drive actionable insights. Results highlight the synergistic relationships among capabilities, such as real-time data availability and data source utilization, while emphasizing the foundational role of robust technical infrastructure. Managerial implications underscore the need for a balanced development of capabilities and strategic integration of predictive systems into decision-making processes. This study contributes to the understanding of how organizations can effectively deploy predictive analytics to anticipate trends, optimize decision-making, and achieve competitive advantage.

Cited by 5

Predictive Analytics

Lim, Daryl · eYLS (Yale Law School) · 2019

“Predictive Analytics” blends the latest research in behavioral economics with artificial intelligence to address one of the most important legal questions at the heart of intellectual property law and antitrust law – how do courts and agencies make judgments about innovation and competition policies? How can they better predict the consequences of intervention or non-intervention?\nThe premise of this Article is that we should not continue to build doctrine at the IP-antitrust on theoretical neoclassical assumptions alone but also on the reality of markets using all that AI has to offer us. Behavioral economics and AI do not replace traditional antitrust analysis. Rather, they are complements and imbue antitrust law with continuing durability.\nPredicting competitive effects is difficult and we need tools to predict outcomes as precisely and reliably as possible. Until now, antitrust law has only been able to operate before a veil of assumptions and rhetoric. Stakeholders have only been able to think about whether and how to intervene in the exercise of IP rights, particularly patent rights, in the broadest terms since even the smallest perturbations in a complicated set of variables can set off ripples that lead to dramatically divergent outcomes. Facts have always mattered in antitrust law, and a more expansive toolkit can only increase our likelihood of getting it right.\nBehavioral economics sheds light on anticompetitive conduct that neoclassical antitrust may regard as irrational and therefore improbable. Once we recognize that it is rational and probable, we need to quantify and value the effects of the conduct. To do this, we need to employ more of the analogical reasoning intrinsic in antitrust law. For that, predictive analytics is very good in helping stakeholders with pattern recognition and simulation runs. This brings us closer to being able to ascribe value which human judgment can be brought to bear. In these, AI provides stakeholders with augmented capabilities to confront the computational challenges these tasks require.

Cited by 0Open access

Predictive Analytics

André Groß · LIBDOC - Westsächsische Hochschule Zwickau · 2018

Fehler, die in einer Produktion entstehen, müssen behoben und zukünftig vermieden werden. Dazu sind Prozesse des Qualitätsmanagements von größter Bedeutung. Diese sind allerdings stehts reaktiv und kosten Zeit. Um die Bearbeitungszeit der Prozesse zu verkürzen und die Entstehung weiterer Fehler zu vermeiden, ist der Einsatz neuer Methoden notwendig. Predictive Analytics ermöglicht die Vorhersage von Ereignissen auf Basis bereits vergangener Vorfälle. Durch dieses Verfahren besteht die Möglichkeit zu einem präventiven Handeln und der gleichzeitigen Unterstützung von Qualitätsprozessen. Die vorliegende Arbeit stellt den Anwendungsversuch von Predictive Analytics im BMW Werk Leipzig vor. Hauptaugenmerk liegt dabei auf der Vorhersage von ausgewählten Fehlerbildern.

Cited by 0

Predictive Analytics

SMD Azash, P. Venkata Subbaiah, Lucia Vilčeková · 2024

Predictive analytics are rapidly developing into a differentiating factor for high-performing enterprises and are being adopted by organizations in a variety of industries. Its goal is to forecast the likelihood of a future event occurring in several facets of a company's operations, including customer churn, loan default, and staff performance. In this current chapter, an attempt is made to study the use of predictive analytics tools for predicting employee turnover and to classify the employee group into stayer and leaver so that effective planning of human resources can be made. To achieve the abovementioned objective data is gathered from 300 employees by administration of a structured questionnaire across selected IT companies in India using a simple random sampling method.

Cited by 0Open access

predictive analytics

2022

This seminal text demystifies the terminology of working in the fashion industry today, providing definitions of processes, techniques, features, and even some historical terms that you need to know. The dictionary now includes coverage of sustainability, smart materials, new technologies and processes. This book has been reorganized in a purely alphabetical order for easy reference. Lavishly illustrated with over 800 illustrations capturing the styles and details of fashion, this reference work is a must have for students, designers, fashion merchandisers, historians, and fashion enthusiasts.

Cited by 0

Predictive Analytics

Sema A. Kalaian, Rafa Kasim · IGI Global eBooks · 2017

Predictive analytics and modeling are analytical tools for knowledge discovery through examining and capturing the complex relationships and patterns among the variables in the existing data in efforts to predict the future organizational performances. Their uses become more common place due largely to collecting massive amount of data, which is referred to as “big data,” and the increased need to transform large amounts of data into intelligent information (knowledge) such as trends, patterns, and relationships. The intelligent information can then be used to make smart and informed data-based decisions and predictions using various methods of predictive analytics. The main purpose of this chapter is to present a conceptual and practical overview of some of the basic and advanced analytical tools of predictive analytics. The chapter provides a detailed coverage of some of the predictive analytics tools such as Simple and Multiple-Regression, Polynomial Regression, Logistic Regression, Discriminant Analysis, and Multilevel Modeling.

Cited by 2

Predictive Analytics

Kristen J. Harris, Hannabeth Franchino-Olsen · IGI Global eBooks · 2022

This chapter presents Prevention Now's design and use of predictive analytics as a method to identify risk factors for human trafficking and to intervene the upstream of exploitation. The method relies on a social-ecological framework for organizing risk factors and to guide analytical prevention efforts. Prevention Now is a non-profit anti-trafficking organization that is prioritizing a data-driven and multidisciplinary approach to trafficking prevention. This approach has three central aims: to develop trans-disciplinary partnerships, to build geographically-specific analytical models, and to translate model findings into targeted prevention. This chapter presents efforts undertaken by Prevention Now towards building collaborative partnerships across systems in a given geography, county, state, to inform data collection and prevention efforts. The chapter centers around Prevention Now's predictive analytics project involving four US states and its ability to leverage data collection and analysis in efforts to better understand the drivers of trafficking.

Cited by 0