Predictive business operations management

Malu Castellanos, Norman Salazar, Fabio Casati, Umeshwar Dayal, Ming Chien Shan

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

The ability to forecast metrics and performance indicators for business operations is crucial to proactively avoid abnormal situations, and to do effective business planning. However, expertise is typically required to drive each step of the prediction process. This is impractical when there are thousands of metrics to monitor. Fortunately, for business operations management, extreme accuracy is not required. It is usually enough to know when a metric is likely to go beyond the normal range of values. This gives opportunity for automation. In this paper, we present an engine that completely automates the prediction of metrics to support a better management of business operations.

Original languageEnglish
Pages (from-to)292-301
Number of pages10
JournalInternational Journal of Computational Science and Engineering
Volume2
Issue number5-6
Publication statusPublished - 2006
Externally publishedYes

Keywords

  • Business operations
  • Business process intelligence
  • Metrics
  • Prediction
  • Time series

ASJC Scopus subject areas

  • Computational Mathematics
  • Modelling and Simulation
  • Computational Theory and Mathematics
  • Hardware and Architecture
  • Software

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