The full text of this article

 

An analytic model to investigate the demand propagation in EOI supply networks
by Roberto Montanari; Eleonora Bottani
International Journal of Simulation and Process Modelling (IJSPM), Vol. 12, No. 2, 2017

 

Abstract: This paper builds upon the study by Montanari et al. (2015). These authors presented a probabilistic approach, named M.DPA.eoq, to predict the demand seen by an upper-tier echelon (e.g., a distribution centre) of a supply network, serving several lower-tier echelons (e.g., retail stores) operating according to an economic order quantity (EOQ) policy. In this paper, we investigate the case of the economic order interval (EOI) policy and thus formulate the Montanari demand probabilistic approach in the EOI scenario (M.DPA.eoi) framework. Although its analytic formulation is not so simple, the M.DPA.eoi is quite easy to understand and can be implemented without difficulties in general-purpose software, such as Microsoft Excel™. Therefore, it is expected to be directly exploited by supply network managers, to estimate the distribution of the demand the upper-tier echelon will face in a defined network structure. The model is tested on four scenarios, with different network structures and different behaviours of the lower-tier echelons.

Online publication date: Sun, 09-Apr-2017

 

is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

 
Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

 
Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Simulation and Process Modelling (IJSPM):
Login with your Inderscience username and password:

 

    Username:        Password:         

Forgotten your password?


 
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

 
If you still need assistance, please email subs@inderscience.com