On establishing initial inventory levels in the rent-to-own industry: an empirical analysis

Michael H. Anderson and Soheil Sibdari

International Journal of Revenue Management, 2013, vol. 7, issue 1, 37-55

Abstract: A theoretic model is developed to calculate the optimal inventory investment for a firm in the rent-to-own industry. This investment question is shown to be equivalent to a queueing problem and queueing technology is employed to fashion a solution. The initial inventory level is a critical decision which should be made to maximise expected profit. The decision is complicated by the fact that the arrival process of new customers, the total number of times that an item will be rented, and the length of time an item is kept by a customer are all random variables. Despite this, the model is able to provide closed-form solutions for various parameters, e.g., the probability that a potential customer will be lost due to product unavailability. A compelling feature of the current study is our access to a large and detailed transactional database. This is used to gain insight into actual contract usage and to numerically estimate the input parameters to our model. The resultant optimal inventory level is then compared against the actual levels providing a clear goodness-of-fit test for our overall approach.

Keywords: empirical analysis; inventory management; Poisson demand; revenue management; rent-to-own; RTO; inventory levels; modelling; inventory investment; queueing technology. (search for similar items in EconPapers)
Date: 2013
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.inderscience.com/link.php?id=53361 (text/html)
Access to full text is restricted to subscribers.

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:ids:ijrevm:v:7:y:2013:i:1:p:37-55

Access Statistics for this article

More articles in International Journal of Revenue Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().