Title: Predictive business operations management

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

Addresses: Hewlett-Packard, 1501 Page Mill Road, MS 1142, Palo Alto, CA 94304, USA. ' Hewlett-Packard, 1501 Page Mill Road, MS 1142, Palo Alto, CA 94304, USA. ' Hewlett-Packard, 1501 Page Mill Road, MS 1142, Palo Alto, CA 94304, USA. ' Hewlett-Packard, 1501 Page Mill Road, MS 1142, Palo Alto, CA 94304, USA. ' Hewlett-Packard, 1501 Page Mill Road, MS 1142, Palo Alto, CA 94304, USA

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.

Keywords: business process intelligence; time series; metrics prediction; metrics forecasting; business operations; performance indicators; business planning; business process management.

DOI: 10.1504/IJCSE.2006.014772

International Journal of Computational Science and Engineering, 2006 Vol.2 No.5/6, pp.292 - 301

Available online: 08 Aug 2007 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article