Abstract | ||
---|---|---|
Having visibility into the current state of business operations doesn’t seem to suffice anymore. The current competitive market
forces companies to capitalize on any opportunity to become as efficient as possible. The ability to forecast metrics and
performance indicators is crucial to do effective business planning, the benefits of which are obvious – more efficient operations
and cost savings, among others. But achieving these benefits using traditional forecasting and reporting tools and techniques
is very difficult. It typically requires forecasting experts who manually derive time series from collected data, analyze
the characteristics of such series and apply appropriate techniques to create forecasting models. However, in an environment
like the one for business operations management where there are thousands of time series, manual analysis is impractical,
if not impossible. Fortunately, in such an environment, extreme accuracy is not required; it is usually enough to know whether
a given metric is predicted to exceed a certain threshold or not, is within some specified range or not, or belongs to which
one of a small number of specified classes. This gives the opportunity to automate the forecasting process at the expense
of some accuracy. In this paper, we present our approach to incorporating time series forecasting functionality into our business
operations management platform and show the benefits of doing this.
|
Year | DOI | Venue |
---|---|---|
2006 | 10.1007/978-3-540-31970-2_1 | computational science and engineering |
Keywords | DocType | Volume |
predictive business operations management,business operation,extreme accuracy,normal range,business operations management,abnormal situation,prediction process,better management,effective business planning,performance indicator,time series,operations management,time series forecasting | Journal | 2 |
Issue | ISSN | ISBN |
5/6 | 0302-9743 | 3-540-25361-0 |
Citations | PageRank | References |
18 | 1.06 | 2 |
Authors | ||
5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Malú Castellanos | 1 | 351 | 74.33 |
Norman Salazar | 2 | 24 | 1.52 |
Fabio Casati | 3 | 4526 | 464.03 |
Umeshwar Dayal | 4 | 8452 | 2538.92 |
Ming-chien Shan | 5 | 1707 | 368.22 |