Green supply chain inventory model for deteriorating items with variable demand under inflation
Smita Rani,
Rashid Ali and
Anchal Agarwal
International Journal of Business Forecasting and Marketing Intelligence, 2017, vol. 3, issue 1, 50-77
Abstract:
Green supply chain increasingly gained interest in the last decade. What initially started as an effort to save environment has developed into a great business concept with increased profitability. Though various studies have been conducted in green supply chain, most of them have neglected the impact of deterioration and inflation. In this study, we develop an inventory model in green supply chain for deteriorating items considering recycling and reverse logistics taking inflation into account. It is assumed that remanufactured products will go to secondary market and have higher demand. Demand is assumed to be quadratic for remanufactured products while linear demand is followed for new products. Holding cost is assumed to be time dependent and deterioration is assumed to follow two-parameter Weibull distribution. The objective is to minimise total cost. Numerical and sensitivity analysis are carried out at the end of this paper.
Keywords: green supply chains; GSC; deterioration; inflation; variable demand; reverse logistics; recycling; remanufacturing; Weibull distribution; inventory modelling; inventory management; deteriorating; green SCM; supply chain management; GSCM. (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://www.inderscience.com/link.php?id=82548 (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:ijbfmi:v:3:y:2017:i:1:p:50-77
Access Statistics for this article
More articles in International Journal of Business Forecasting and Marketing Intelligence from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().