Prescriptive Analytics System for Improving Research Power

2013 IEEE 16th International Conference on Computational Science and Engineering ◽

10.1109/cse.2013.169 ◽

2013 ◽

Cited By ~ 11

Author(s):

Sa-Kwang Song ◽

Donald J. Kim ◽

Myunggwon Hwang ◽

Jangwon Kim ◽

Do-Heon Jeong ◽

...

Keyword(s):

Prescriptive Analytics

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Descriptive, predictive, and prescriptive analytics: Data, methods, and algorithms

Economic Analysis Theory and Practice ◽

10.24891/ea.18.3.447 ◽

2019 ◽

Vol 18 (3) ◽

pp. 447-461

Author(s):

V.G. Kogdenko ◽

Keyword(s):

Prescriptive Analytics

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Predictive and Prescriptive Analytics toward Passenger-centric Ground Delay Programs

SSRN Electronic Journal ◽

10.2139/ssrn.3734008 ◽

2020 ◽

Author(s):

Alexandre Jacquillat

Keyword(s):

Prescriptive Analytics ◽

Ground Delay

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Procurement Analytics: Enabling Data Architectures in the Age of Big Data

The Oxford Handbook of Supply Chain Management ◽

10.1093/oxfordhb/9780190066727.013.5 ◽

2020 ◽

Author(s):

Michael Goul ◽

T. S. Raghu ◽

Ziru Li

Keyword(s):

Big Data ◽

Academic Research ◽

Research Literature ◽

Structured Interviews ◽

Data Space ◽

Prescriptive Analytics ◽

Data Architecture ◽

The Future ◽

High Level ◽

Area Data

As procurement organizations increasingly move from a cost-and-efficiency emphasis to a profit-and-growth emphasis, flexible data architecture will become an integral part of a procurement analytics strategy. It is therefore imperative for procurement leaders to understand and address digitization trends in supply chains and to develop strategies to create robust data architecture and analytics strategies for the future. This chapter assesses and examines the ways companies can organize their procurement data architectures in the big data space to mitigate current limitations and to lay foundations for the discovery of new insights. It sets out to understand and define the levels of maturity in procurement organizations as they pertain to the capture, curation, exploitation, and management of procurement data. The chapter then develops a framework for articulating the value proposition of moving between maturity levels and examines what the future entails for companies with mature data architectures. In addition to surveying the practitioner and academic research literature on procurement data analytics, the chapter presents detailed and structured interviews with over fifteen procurement experts from companies around the globe. The chapter finds several important and useful strategies that have helped procurement organizations design strategic roadmaps for the development of robust data architectures. It then further identifies four archetype procurement area data architecture contexts. In addition, this chapter details exemplary high-level mature data architecture for each archetype and examines the critical assumptions underlying each one. Data architectures built for the future need a design approach that supports both descriptive and real-time, prescriptive analytics.

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