Predictive Global Sensitivity Analysis: Foundational Concepts, Tools, and Applications
Charles L. Munson et al.
What the paper says
Modern managers must sift through huge data overload to make quick decisions in dynamic environments. Predictive Global Sensitivity Analysis (PGSA) represents a statistical approach to simplifying a complicated mathematical optimization model into a straightforward set of predictive equations by summarizing numerous complexities into a few highly explanatory variables. Managers can use such equations to make swift decisions with colleagues or customers in real time, or the equations can be used as a monitoring tool to verify current decisions as external conditions change. In this monograph, the authors review the published applications of PGSA that have emerged over the past two decades. Differences in the published works illustrate the underlying flexible nature of the method. Modelers get to practice significant judgement all throughout the process, from application selection through model validation. Section 3 provides a step-by-step tutorial of the full PGSA process. The authors describe how each step has been addressed in the literature to date, and they illustrate each step in detail using two new applications of classic problems in operations research. Section 4 introduces a brand-new application of PGSA that predicts which among three centralized purchasing scenarios that a newly introduced product purchased at a local site should adopt.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.00 × 0.4 = 0.00 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.