This research has led to a novel methodology for assessment and quantification of supply risks in the supply chain. The research has built on advanced Knowledge Discovery techniques and has resulted to a software implementation to be able to do so. The methodology developed and presented here resembles the well-known consumer credit scoring methods as it leads to a similar metric, or score, for assessing a supplier’s reliability and risk of conducting business with that supplier. However, the focus is on a wide range of operational metrics rather than just financial, which credit scoring techniques typically focus on. The core of the methodology comprises the application of Knowledge Discovery techniques to extract the likelihood of possible risks from within a range of available datasets. In combination with cross-impact analysis, those datasets are examined for establish the inter-relationships and mutual connections among several factors that are likely contribute to risks associated with particular suppliers. This approach is called conjugation analysis. The resulting parameters become the inputs into a logistic regression which leads to a risk scoring model the outcome of the process is the standardized risk score which is analogous to the well-known consumer risk scoring model, better known as FICO score. The proposed methodology has been applied to an Air Conditioning manufacturing company. Two models have been developed. The first identifies the supply risks based on the data about purchase orders and selected risk factors. With this model the likelihoods of delivery failures, quality failures and cost failures are obtained. The second model built on the first one but also used the actual data about the performance of supplier to identify risks of conducting business with particular suppliers. Its target was to provide quantitative measures of an individual supplier’s risk level. The supplier risk scoring model is tested on the data acquired from the company for its performance analysis. The supplier risk scoring model achieved 86.2% accuracy, while the area under curve (AUC) was 0.863. The AUC curve is much higher than required model’s validity threshold value of 0.5. It represents developed model’s validity and reliability for future data. The numerical studies conducted with real-life datasets have demonstrated the effectiveness of the proposed methodology and system as well as its future potential for industrial adoption.

Working with international suppliers in global supply chains, manufacturing firms now are faced with substantial supplier risks which could be triggered by disruptions in either their suppliers or the supplier’s market. Reactive actions to the risks, however, have usually been shown to be inefficient and sometimes ineffective. In this dissertation, therefore, I develop a theoretical framework linking some key relationship-specific capabilities to supplier risk. My contention is that the capabilities, when developed, can help proactively mitigate the risk. Thus, the model in this study is grounded in the resource-based and the relational views. In this study, the survey method has been employed to collect data from 66 manufacturing firms in the United State who are sourcing from international suppliers. Procedural and statistical methods have been employed to guard against typical empirical issues including non-response bias, common method bias, and problems in validity and reliability of measurement instruments. Structural equation modeling with partial least squares was employed to test the model with bootstrapping to estimate t-values for the paths. The analysis results showed support for the model. A conclusion from the study is that visibility is the critical relationship-specific capability that needs to develop for buying firms to mitigate supplier risk proactively. This is because it may not be substitutable by other mechanisms like goodwill trust, and other capabilities, including absorptive capacity and IT integration, will only operate via visibility to influence risk performance. Moreover, visibility is a significant capability that helps mitigate risk regardless of the relationship duration between the buyer and the supplier and of the market conditions under which the supplier is working. This study thus adds to the risk literature with discussions of supplier risks. Nuances have also been added to the resource-based and relational views by developing the theoretical relationships among the identified capabilities and by examining the contextual conditions under which the relationships are working to mitigate supplier risk. Managers from both sides of a dyadic relationship may benefit from the study by utilizing the tools and the study results to monitor and mitigate supplier risk.

This thesis deals with the provision of credit supply, especially the risk associated with their delivery. The key part of this work is a model that evaluates the level of supply risk using fuzzy logic. Model evaluation of the supply risk is introduced to selected customers selected companies.

M. Tech. (Department of Logistics, Faculty of Management Sciences), Vaal University of Technology. / The purpose of this study was to investigate the relationship between supplier selection practices, supplier risk management, supplier commitment and procurement performance in the public health industry in Gauteng province. Many studies have been conducted on the specific subject of procurement performance within the public health care sector. However, there is very little evidence that any such studies have been carried out that have precisely been narrowed down to the specific subject of the dimensional relationships and linkages between Supplier selection, supplier risk management, supplier commitment and procurement performance in the public health industry in South Africa. This study, therefore, was conducted to fill this gap. To measure the study constructs, the survey material was designed in the form of a structured questionnaire. Participants were asked to complete four test instruments namely: supplier selection questionnaire, supplier risk management questionnaire, supplier commitment and procurement performance questionnaire. A total number of 200 questionnaires was distributed to the identified sample of public health industries of which 187 responded and finally, 150 questionnaires were usable and used for data analysis. The collected quantitative data were analysed using the SMART-partial least squares (SMART-PLS 3) structural equation modelling procedure. The actual data analysis techniques applied included descriptive statistics and inferential statistics using structural equation modelling. Also, the latter used a SMART-PLS 3 to test the psychometric properties of measurement scales and the testing of the six hypotheses using the path analyses technique. The results of the study showed positive and significant relationships amongst all variables except for one. Specifically, supplier selection and supplier risk management exerted a moderate and significant influence on supplier commitment. Moreover, supplier commitment had a strong positive and significant relationship with procurement performance while supplier risk management had a weak and insignificant relationship with the same factor. More results provided from the analysis confirmed the existence of a very strong and significant relationship between supplier selection and procurement performance. Besides, the study takes note of its contributions to highlighting its merits. From a theoretical perspective, it provides an in-depth examination of some driving factors to supplier selection, supplier risk management, supplier commitment and procurement performance within Public health entities. Given that a study of this nature has not been performed before amongst South African public health care sectors, the results are an essential addition to the existing body of literature within the area of procurement performance within public health industries in developing countries such as South Africa. The study concludes by suggesting recommendations for limiting the impact of the identified challenges on procurement performance.