Survey into predictive key performance indicator analysis from data mining perspective

• Published in: Proceedings (IEEE International Conference on Emerging Technologies and Factory Automation) Vol. 1; pp. 476 - 483
• Main Authors: Thakur, Akshay, Beck, Robert, Mostaghim, Sanaz, Grosmann, Daniel
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2020
• Subjects: best practices; Data mining; Decision making; Key performance indicator; key performance indicators; KPI relationship; KPI selection; Manufacturing industries; predictive analysis; Shape; Standards organizations; survey; Text analysis
• ISSN: 1946-0759
• DOI: 10.1109/ETFA46521.2020.9212111

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.