The Framework of Parametric and Non-Parametric Operational Data Analytics (ODA)

46 Pages Posted: 10 Apr 2023 Last revised: 23 Jun 2023

See all articles by Qi Feng

Qi Feng

Mitchell E. Daniels, Jr School of Business, Purdue University

J. George Shanthikumar

Purdue University - Krannert School of Management

Date Written: March 20, 2023

Abstract

This paper introduces the general philosophy of the Operational Data Analytics (ODA) framework for data-based decision modeling. The fundamental development of this framework lies in establishing the direct mapping from data to decision by identifying the appropriate class of operational statistics. The efficient decision making relies on a careful balance between data integration and decision validation. Through a canonical decision-making problem under uncertainty, we show that the existing approaches (including statistical estimation and then optimization, retrospective optimization, sample average approximation, regularization, robust optimization, and robust satisficing) can all be unified through the lens of the ODA formulation. To make the key concepts accessible, we demonstrate, using a simple running example, how some of the existing approaches may become equivalent under the ODA framework, and how the ODA solution can improve the decision efficiency, especially in the small sample regime.

Keywords: Operational Data Analytics, Operational Statistics, Data-Integrated Decision, Small Samples

Suggested Citation

Feng, Qi and Shanthikumar, J. George, The Framework of Parametric and Non-Parametric Operational Data Analytics (ODA) (March 20, 2023). Available at SSRN: https://ssrn.com/abstract=4400555 or http://dx.doi.org/10.2139/ssrn.4400555

Qi Feng (Contact Author)

Mitchell E. Daniels, Jr School of Business, Purdue University ( email )

403 Mitch Daniels Blvd.
West Lafayette, IN 47907
United States

J. George Shanthikumar

Purdue University - Krannert School of Management ( email )

1310 Krannert Building
West Lafayette, IN 47907-1310
United States

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
221
Abstract Views
574
Rank
246,202
PlumX Metrics