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http://theses.ncl.ac.uk/jspui/handle/10443/4374
Title: | Multi-objective optimisation of dynamic short-term credit portfolio selection :the adoption of third party logistics credit for financing working capital contrained small and medium sized enterprises in supply chains |
Authors: | Jiao, Feng |
Issue Date: | 2018 |
Publisher: | Newcastle University |
Abstract: | Many companies, especially small and medium sized enterprises, are faced with liquidity problems. The shortage of working capital in their businesses has prevented supply chains from achieving effectiveness and efficiency in management. Although they can access short-term loans from banks and suppliers, the willingness of these credit lenders to lend short-term capital is often restricted by the fact that they cannot monitor whether or how their customers will use the loans according to the agreements. In many cases, this fact makes it difficult for capitalconstrained companies to obtain sufficient working capital from existing funding sources. A business practice called Integrated Logistics and Financial Service has been developed, which can improve banks’ monitoring of how their loans will eventually be used via the alliance of third party logistics companies and banks. The emergence of credit offered by third party logistics companies (termed as 3PLC) provides more choices for working capital constrained companies. Following on traditional bank overdrafts and trade credit, the new 3PLC became the third type of credit available to short-term working capital constrained companies. A new issue arising from this situation is how a working capital constrained company can determine a credit portfolio from multiple working capital sources. Current studies of credit portfolio management are still silent in considering 3PLC. Moreover, limited studies have integrated credit portfolio management into material flow management in supply chains. In light of the aforementioned discussions, this thesis aims to optimise dynamic credit portfolio management in supply chains to achieve the different business objectives of working capital constrained companies. To achieve the above aims, this thesis firstly applies an analytic hierarchy process and linear programming model to optimise a single objective. It applies the analytic hierarchy process to evaluate the concerns of working capital-constrained companies in selecting credit. These concerns are identified through a thorough literature review focusing on the considerations of small and medium sized enterprises’ in borrowing short-term credit. The analytic hierarchy process has been applied to determine the priority of the identified concerns and the preferences of borrowers for bank overdrafts, trade credit and 3PLC. A linear programming model has been developed based on the results obtained from the analytic hierarchy process model. It determines the maximum borrowing amount for a given period from multiple credit sources. To reflect the complexity of working capital constrained companies borrowing credit, thisthesis has extended the model from single objective optimisation to multiple objectives optimisation. Consequently, a goal-programming model has been developed. This model provides the solution of optimizing two business objectives including overall cost and backorder penalty cost minimization. Numerical examples have been conducted to test and analyse all the mathematical models. This thesis contributes the following aspects: 1) the new 3PLC together with bank overdraft and trade credit have been considered into credit portfolio management; 2) borrower’s concerns and credit preferences relating to the three types of credit have been identified and evaluated; 3) mathematical models have been developed for credit portfolio selection over multiple periods. |
Description: | PhD Thesis |
URI: | http://theses.ncl.ac.uk/jspui/handle/10443/4374 |
Appears in Collections: | Newcastle University Business School |
Files in This Item:
File | Description | Size | Format | |
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Jiao F 2018.pdf | Thesis | 3.23 MB | Adobe PDF | View/Open |
dspacelicence.pdf | Licence | 43.82 kB | Adobe PDF | View/Open |
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