Filtering of a Partially Observed Inventory System

Lakhdar Aggoun ()
Additional contact information
Lakhdar Aggoun: Sultan Qaboos University

Chapter 8 in Hidden Markov Models in Finance, 2007, pp 121-132 from Springer

Abstract: Summary The vast majority of work done on inventory system is based on the critical assumption of fully observed inventory level dynamics and demands. Modern technology, like the internet, offers a tremendous number of opportunities to businesses to collect imperfect but useful information on potential customers which helps them planning efficiently to meet future demands. For instance visits to commercial web sites provides the management of a business of a source of partial information on future demands. On the other hand it is often the case that it is not economically viable to fully observe the dynamics of inventory levels and only partial information is accessible to the management. In this article, using hidden Markov model techniques we estimate the inventory level as well as future demands of partially observed inventory system. The parameters of the model are updated via the EM algorithm.

Keywords: Filtering; Markov chains; change of measure; inventory model (search for similar items in EconPapers)
Date: 2007
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:978-0-387-71163-8_8

Ordering information: This item can be ordered from
http://www.springer.com/9780387711638

DOI: 10.1007/0-387-71163-5_8

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 ().