Neural networks based vendor-managed forecasting: a case study
by Atul B. Borade, Satish V. Bansod
International Journal of Integrated Supply Management (IJISM), Vol. 6, No. 2, 2011

Abstract: Vendor-managed inventory (VMI) is a collaborative supply chain management practice adopted by many organisations. For making inventory-related decisions an accurate forecast is needed. Traditional forecasting models provide close but not accurate forecasts. In the recent years, decision support tools, like neural networks, are used for making an accurate forecast. This paper presents a case study of a small enterprise where a vendor-managed inventory pact was in force between enterprise and a retailer. In the study, various neural networks were used for demand forecasting. The results of neural network based forecasts are found and compared on various fronts. Multi-criteria decision-making tools are adopted for comparing and verifying the results. Study shows that even small enterprise could adopt the simple VMI system by using properly trained neural network and obtain substantial saving in inventory and costs.

Online publication date: Fri, 17-Jun-2011

The full text of this article 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 Integrated Supply Management (IJISM):
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