Doctoral Degrees (Industrial Engineering)

Permanent URI for this collection

https://scholar.sun.ac.za/handle/10019.1/775

Browse

Browsing Doctoral Degrees (Industrial Engineering) by Author "Bvuchete, Munyaradzi"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    A demand driven supply chain management maturity model for public healthcare sector
    (Stellenbosch : Stellenbosch University, 2020-03) Bvuchete, Munyaradzi; Grobbelaar, Sara; Van Eeden, Joubert; Stellenbosch University. Faculty of Industrial Engineering. Dept. of Industrial Engineering.
    ENGLISH ABSTRACT: To cope with low forecast accuracy and high demand variability in complex public healthcare supply chains, many supply chain nodes are trying to move from a pure Push strategy, to a Pull strategy, driven by actual customer demand. However, there are a few methodologies through which an analyst can impartially measure and support implementation of Demand Driven Supply Chain Management (DDSCM) practices in public healthcare Supply Chain Networks (SCNs). Therefore, the research aim of this study was to develop a network-maturity mapping tool that support the design, implementation and assessment of DDSCM performance for unique supply chain nodes in the public healthcare SCN as well as to provide guidance on how the unique supply chain nodes in the SCN can progress towards advanced DDSCM maturity stages. This study used a systems engineering research design and provides a systematic literature review to establish the main dimensions1 and their associated capabilities2 in the DDSCM network maturitymapping tool such as visibility, technology, collaboration, human resources, organisational alignment, performance management and distribution management. The tool was validated through twenty-three (23) subject matter experts to assert its completeness, credibility and usefulness in public healthcare SCN. Moreover, to test for applicability and validity of the tool, it was applied in twelve (12) SCN case studies, which included pharmaceutical manufacturing companies, Central Medicines Stores (pharmaceutical distributors), primary healthcare facilities and hospitals. All these supply chain nodes in the public healthcare SCN acknowledged that the network maturity-mapping tool was clear, structured logically and allows for comprehensive assessment. Key findings from the case studies show that, for those DDSCM capabilities that require higher effort to implement/sustain, it is common for the DDSCM capabilities to be less mature. Moreover, case study outcomes suggest that, the higher the impact of the DDSCM capabilities, the more mature the capabilities. Findings also reveal that most mature supply chain nodes are at the manufacturing end of the SCN. In contrary, healthcare facilities are the least mature. The study contributes to the limited literature on DDSCM in the public healthcare sector in developing countries. Moreover, the main contribution is the network maturity mapping tool to assess both where the public healthcare SCN is today on the maturity scale and how it can progress to more advanced maturity levels of DDSCM. This allows for a systematic and methodological planning of interventions for improvement. Lastly, to make DDSCM sustainable, supply chain nodes have to adopt a continuous improvement commitment that focuses on enhancing the supply chain processes, supporting the people, and fixing the data issues. This implies that supply chain nodes need to develop strategically aligned capabilities, not only at the supply chain node itself, but also among the other supply chain nodes that are part of the value-adding networks. By adopting this DDSCM orientation, supply chain nodes will attain tremendous benefits, which include improved delivery performance, reduced inventories and supply chain costs. Another benefit is that downstream supply chain nodes will be able to scale up and implement the Visibility Analytics Network (VAN) model developed by the National Department of Health (NDoH) to improve the availability of medicines at primary healthcare facilities, since informed planners will have total visibility of inventory levels and consumption patterns of primary healthcare facilities. Consequently, orders will be generated based on this quality data.