Inventory Models for Manufacturing Process with Reverse Supply Chain

Mahapatra, Rabindra Narayan (2013) Inventory Models for Manufacturing Process with Reverse Supply Chain. PhD thesis.



Technology innovation leading to development of new products and enhancement of features in existing products is happening at a faster pace than ever. This trend has resulted in gross increase in use of new materials and decreased customers‘ interest in relatively older products leading to the deteriorating conditions of the environment due to the reduction of non-renewable resources and steady increase in the land fill of waste. This has forced organizations and communities to consider recovery alternatives such as reuse, repair, recycle, refurbish, remanufacture and cannibalize, rather than discarding of the products after end of life.
Products are retuned back or become redundant because either they do not function properly or functionally they become obsolete. The sources of these returns are Manufacturing returns, Distribution returns and Customer returns. The product recovery options in reverse supply are Repair, Refurbish, Re-manufacture, Cannibalize and Recycle. The main difference between the options is in the reprocessing techniques. Where Repair, refurbishing, and remanufacturing are involved in the up gradation of the used products in quality and/or technology with a difference with respect to the degree of up gradation(repair involves the least, and remanufacturing the largest),the cannibalization and recycling are involved in using parts ,components and materials of the used products.
Although much is being disused on the different recovery options still a lot of research remains to be done for improvement of the currently available techniques. In this context the present work focuses on remanufacturing option of recovery process for return items which is the most advanced and environmentally friendly production processes in use. Therefore the broad objectives of the present work are to deal with the different models of remanufacturing either new or existing for adding new features to it and making it simple and more user oriented, to develop deterministic models using direct manufacturing and remanufacturing for profit optimization, to develop and deal with probabilistic models of inventory with demand fluctuation using direct manufacturing and select and recommend a tool for predicting various critical parameters associated with the Reverse supply chain (RSC).to make these models usable to achieve maximum advantages by reutilization of resources integrating the upstream and downstream chains.
For the effective implementation of remanufacturing in Reverse supply chain, the entire work has been arranged in different chapters to present the distinct aspects of the research. Models are developed with special reference to remanufacturing. These models proposed helped in minimizing the gaps existing in the RSC in the
present scenario. The different models proposed for RSC are discussed on the basis of deterministic and probabilistic approaches. Although a lot of assumptions are intentionally made to make the models deterministic, still these models have its own identity in satisfying the needs of RSC. Two models are being discussed under deterministic approach. These models tries to find out the amount of new product supply to the market, the amount of remanufactured products supply to the market, the amount of products returned from the market and the amount of waste. Pertinent data from industry have been considered to prepare the models. The model variables are tested with adaptive-network-based fuzzy inference system (ANFIS), where the testing of the actual out come and desired outcome is done by using ANFIS. One of the proposed models is picked up to predict the critical parameters associated with RSC using remanufacturing.
Although the models dealing with the deterministic RSC models are simple still it becomes difficult to deal with a situation where there is a fluctuation of demand in the market, which is a common phenomenon. Therefore, it becomes inevitable to use the probabilistic approach for sorting out it. The aim is to deal with probabilistic models of inventory and models are proposed where the uncertainty due to fluctuation of demand and uncertainty in the return rate of used products is taken care of by using the safety stock. The determination of the safety stock is done on the basis of service level approach. The model variables are optimized using mathematical models considering the profit maximization.
The contribution of the present work is directed towards the environmental benefits. The manufacture of durable goods is one of the major contributors to the GNP of all developed countries. It employs large amounts of human resources, raw materials and energy. The raw materials and energy in the production of durable goods have been continually depleted. Many durable products are disposed in landfills at the end of their useful lives as well. The landfill space has been decreasing and the price charged by the landfills is increasing at a faster rate. This becomes an environmental concern. Remanufacturing, as discussed earlier is one of the predominant product recovery option for the return products. With respect to quality it is considered to be as good as new ones but with a lower cost of conversion. Therefore, focusing on remanufacturing option of product recovery not only decreases the depletion rate of virgin raw materials and rate of land fill but also contributes much towards the GDP as well as GNP. The models proposed in this work are simple and can be practically implemented to get benefits from the return items and still satisfying the market demand for sustainable production.

Item Type:Thesis (PhD)
Uncontrolled Keywords:adaptive-network-based fuzzy inference system, Reverse supply chain
Subjects:Engineering and Technology > Mechanical Engineering > Production Engineering
Divisions: Engineering and Technology > Department of Mechanical Engineering
ID Code:5472
Deposited By:Hemanta Biswal
Deposited On:06 Feb 2014 10:44
Last Modified:06 Feb 2014 10:44
Supervisor(s):Biswal, B B

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