Browsing by Author "Matshabaphala, Ntebaleng Sharon"
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- ItemImplementation of clustering techniques for segmentation of Mozambican cassava suppliers(Stellenbosch : Stellenbosch University, 2021-03) Matshabaphala, Ntebaleng Sharon; Grobler, Jacomine; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.ENGLISH ABSTRACT: Although an organisation generally accumulates many suppliers in the course of doing business,some of these suppliers are of little or no importance to the organisation beyond fulfilling a simple order transaction, while other suppliers play a strategic role in the success of an organisation. The decision to invest in supplier relationships is a major step for an organisation, especially because the value gained from interacting in a supply network rests on the principle of prioritising the right suppliers. The segmentation of suppliers plays a significant role in supplier relationship management. Not only does it offer an effective method of assessing suppliers, but it also provides a resource-efficient decision methodology that specifies appropriate relation-ships and governance structures for each segment. In this thesis, three techniques are applied for clustering cassava suppliers in Mozambique.Over 3 000 smallholder farmers supply cassava to a for-profit social enterprise called Dadtco Philafrica. Dadtco Philafrica needs an effective supplier segmentation method to gain insight into how it should direct its resources to where they will have the greatest impact. Thek-means algorithm, agglomerative hierarchical clustering (AHC), and self-organising maps(SOM) with Ward clustering were applied to a real-world case study. Extensive algorithm pa-rameter tuning was conducted in order to ascertain good parameter values for each clustering technique. Performance of the algorithms was evaluated and compared using intra-cluster and inter-cluster distances, and the best performing algorithm, in the context of the case study,was selected. The SOM with Ward clustering outperformed thek-means and AHC, and its results were used to conduct a detailed cluster analysis. The insights gained from the cluster analysis were used to provide recommendations and to suggest suitable intervention strategies to manage each segment of suppliers. The encouraging results of these algorithms showed that clustering techniques can be utilised effectively in segmenting suppliers. The proposed method offers users the basis of a suppliers egmentation system that is more efficient. A user can simply rerun the algorithm using the latest data, to check for suppliers who have moved to a different cluster and to determine cluster allocation of new suppliers. This method relies primarily on historical data to segment suppliers; therefore, it provides an organisation with data-based insight regarding its supplybase.