Survey into predictive key performance indicator analysis from data mining perspective
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Le résumé fourni par la source
Predictive analytics is seen as one of the emerging technology in this digital age of big data. Computational processing power and speed has grown exponentially in the last few years that has made predictive analytic practical for application in different organization. Manufacturing industries has huge amount of data in different shapes and forms, and keep regular track of their performance by monitoring key performance indicators defined under business strategy. Prioritizing and predicting these key performance indicators provide organization cutting edge as compared to competitors by being proactive rather than reactive. As compared to traditional business intelligence tools where focus is on static report or dashboards about past data, predictive analysis focuses on estimating outcomes with the objective of driving better business performance. Moreover, it is also being adopted for decision-making tools. Different data mining techniques are applied in the field of performance management system as per individual or project need. Many researches has developed different ideas to understand and evaluate complex intervened key performance indicator relationships in performance measurement system. The aim of the paper is to present comprehensive version of predictive key performance indicator analysis from its background to state of the art, describing various data mining standards, methodologies as well as industrial and research application. The paper also studies various surveys regarding predictive analytic for business application to identify different best practices in this field.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Survey into predictive key performance indicator analysis from data mining perspective
- Date Crossref
- 01/09/2020
- Éditeur
- IEEE
- Type
- proceedings-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Volkswagen Group (Germany) pays non établi dans la noticeEntreprise
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Otto-von-Guericke-Universität Magdeburg pays non établi dans la noticeUniversité ou école supérieure
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Technische Hochschule Ingolstadt pays non établi dans la noticeUniversité ou école supérieure
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Productivity Controlling & Production system Volkswagen AG pays non établi dans la noticeInstitution
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Productivity Controlling Volkswagen AG pays non établi dans la noticeInstitution
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Faculty of Computer Science pays non établi dans la noticeUniversité ou école supérieure
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Faculty of Engineering & Management pays non établi dans la noticeUniversité ou école supérieure
Volkswagen Group (Germany), Otto-von-Guericke-Universität Magdeburg et Technische Hochschule Ingolstadt, avec 4 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.