Prescriptive Analytics in Internet of Things with Concentration on Deep Learning

Iman Raeesi Vanani () and Setareh Majidian ()
Additional contact information
Iman Raeesi Vanani: Associate Professor, Allameh Tabataba’i University
Setareh Majidian: Alumni of University of Tehran

A chapter in Introduction to Internet of Things in Management Science and Operations Research, 2021, pp 31-54 from Springer

Abstract: Abstract Broad utilization of internet of things in different industries and manufacturing segments, from healthcare to transportation and supply chain, smart house, city and smart medicine, generated a huge amount of data through such smart devices. Prescriptive analytical power of firms enables them to gain a competitive advantage from generated data. Prescriptive analytics can be enabled by deep learning to overcome the complexity of pattern recognition and data analysis of censored data. Deep learning with learning (un)structured data has great capability in identifying hidden knowledge. Therefore, deep learning plays an enabler role for prescriptive analytics. The goal of prescriptive analytics is to create business value with enhancing firms’ performance by offering optimal solutions. In this chapter, absorptive capacity theory is deployed and explains that to the extent that firms can empower prescriptive analytics with deep learning technology and optimization algorithms to identify and leverage external knowledge; firms can gain advantage of exploitative innovation capability and enhance their performance significantly.

Keywords: Prescriptive analytics; Deep learning; IoT (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-030-74644-5_2

Ordering information: This item can be ordered from
http://www.springer.com/9783030746445

DOI: 10.1007/978-3-030-74644-5_2

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().