Department of Industrial Engineering

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Browsing Department of Industrial Engineering by browse.metadata.advisor "Ackerman, Simon"

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    Simulating the South African forestry supply chain
    (Stellenbosch : Stellenbosch University, 2022-04) Laubscher, Jennifer Mignonne; Bekker, James F.; Ackerman, Simon
    ENGLISH SUMMARY: The purpose of this study was to provide base simulation models that demonstrated some capabilities of discrete-event stochastic simulation when applied to the South African pulp- and saw timber supply chains from nursery- to mill-gate. The simulation models were designed to provide support for strategic decision-making by allowing scenario analysis through experimentation and bi-objective optimisation. A literature study was performed on various topics relating to the South African forestry industry, supply chain management and simulation modelling. This literature study included a discussion on various simulation software packages, from which one, namely Siemens Tecnomatix Plant Simulation was selected to be utilised in this study. To simplify the simulation modelling process two case studies were developed through stakeholder and subject-matter expert consultation. The case studies were converted into process ow diagrams, which could further be expanded into concept models. Thereafter, the simulation input data requirements were identi ed and the concept models could be translated into computerised versions in the chosen simulation software. The simulation models were built following a systematic and iterative approach. Final veri cation was done through structured model walk-throughs, error elimination and by performing entity tracing tests. Validation was done in consultation with project stakeholders and subject-matter experts, as well as through validation experimentation. After the simulation models were veri ed, validated and proven to be credible representations of the real-world pulpwood- and saw timber supply chains from nursery- to mill-gate, their experimental capabilities were demonstrated. The models were found to have a large combinatorial nature with regards to the number of experiments that can be performed, and exhibited the capability to perform \what-if" analysis and bi-objective optimisation.