Predictive business operations management

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

Результат исследований: Материалы для журналаСтатья

11 Цитирования (Scopus)

Выдержка

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.

Язык оригиналаАнглийский
Страницы (с-по)1-14
Число страниц14
ЖурналLecture Notes in Computer Science
Том3433
СостояниеОпубликовано - 2005
Опубликовано для внешнего пользованияДа

Отпечаток

Operations Management
Forecasting
Time series
Industry
Metric
Time Series Forecasting
Performance Indicators
Time Series Analysis
Visibility
Forecast
Exceed
Extremes
Planning
Series
Business
Costs
Range of data

ASJC Scopus subject areas

  • Computer Science (miscellaneous)

Цитировать

Castellanos, M., Salazar, N., Casati, F., Dayal, U., & Shan, M. C. (2005). Predictive business operations management. Lecture Notes in Computer Science, 3433, 1-14.

Predictive business operations management. / Castellanos, Malu; Salazar, Norman; Casati, Fabio; Dayal, Umesh; Shan, Ming Chien.

В: Lecture Notes in Computer Science, Том 3433, 2005, стр. 1-14.

Результат исследований: Материалы для журналаСтатья

Castellanos, M, Salazar, N, Casati, F, Dayal, U & Shan, MC 2005, 'Predictive business operations management', Lecture Notes in Computer Science, том. 3433, стр. 1-14.
Castellanos M, Salazar N, Casati F, Dayal U, Shan MC. Predictive business operations management. Lecture Notes in Computer Science. 2005;3433:1-14.
Castellanos, Malu ; Salazar, Norman ; Casati, Fabio ; Dayal, Umesh ; Shan, Ming Chien. / Predictive business operations management. В: Lecture Notes in Computer Science. 2005 ; Том 3433. стр. 1-14.
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