Browsing by Author "Du Plessis, Marno"
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- ItemA concept demonstrator for self-organising demand-driven inventory management in pharmaceutical supply chains(South African Institute for Industrial Engineering, 2018) Du Plessis, Marno; Van Vuuren, Jan H.; Van Eeden, JoubertENGLISH ABSTRACT: Perennial stock-outs of essential medicines are commonplace in the pharmaceutical supply chains of developing countries. Stock-outs are mainly attributed to a general lack of collective information sharing in pharmaceutical supply chains. In this paper, a computerised agent-based simulation model concept demonstrator is proposed and demonstrated hypothetically as part of a larger drive to establish the value of leveraging information sharing in pharmaceutical supply chains with a view to enhance decision-making. The objective of this paper is to outline the prerequisite research inputs, design requirements and hypothetical implementation of the aforementioned demonstrator. The work reported on in this paper remains a work in progress.
- ItemReinforcement learning for inventory management in information-sharing pharmaceutical supply chains(Stellenbosch : Stellenbosch University, 2020-03) Du Plessis, Marno; Van Vuuren, J. H.; Van Eeden, Joubert; Stellenbosch University. Faculty of Industrial Engineering. Dept. of Industrial Engineering.ENGLISH ABSTRACT: A general lack of information sharing across the various tiers of pharmaceutical supply chains in developing countries continues to compromise the availability of essential medicines. In the South African public health-care context, recent efforts aimed at improving information sharing in the pharmaceutical supply chain have been plagued by several implementation problems. It is conjectured that the true potential impact of information sharing remains unclear in the South African public health-care domain. The objective in this thesis is to elucidate conceptually how information sharing may benefit inventory management in a pharmaceutical supply chain. A number of hypothetical information-sharing scenarios are proposed in this thesis and their relative effectiveness is evaluated within a simulation modelling environment. The first of these scenarios does not involve any information sharing and serves as a benchmark. The scope of information sharing is further increased incrementally over the remaining scenarios. An agentbased pharmaceutical supply chain simulation model is further established in this thesis in order to evaluate the impact of information sharing in the context of the information-sharing scenarios. This simulation model is implemented as a concept demonstrator and takes as input any userspecified supply chain network. The concept demonstrator is capable of modelling the high-level operation of a pharmaceutical supply chain over time, with a particular focus on the flow of inventory. A reinforcement learning approach is adopted towards discovering effective inventory replenishment policies, specifically informed by information sharing, during each of the aforementioned information-sharing scenarios. The effectiveness of these policies is measured in respect of the total number of stock-outs and product expiries observed in the supply chain. A comparative analysis of the information-sharing scenarios is performed in the context of a hypothetical supply chain network experiencing a fluctuating demand pattern. This analysis reveals that stock-outs may be mitigated substantially when allowing health-care facilities that are located in close proximity to one another to share inventory among each other. It is also shown that the types of, and granularity of, information shared are instrumental in determining the relative effectiveness of information sharing.