The role of Manganese and minerals towards sustainable development in South Africa is a topic that has not been widely researched, despite the country’s dominant endowment of these mineral resources (SAMI, 2009). An alternative approach to evaluate beneficiation opportunities in the Manganese mining value chain as a resource, by investigating dynamic parameters that describe the pattern of sustainable development in the industry’s value chain, was addressed in this research. A systems thinking approach was investigated as a tool to review and solve sustainable development problems in the Manganese resources value chain.
The research focused on the application of system dynamics modelling within the systems thinking framework. This was intended to establish a pattern of relationship and causality between the input parameters of the Manganese mining value chain and the key drivers of sustainable development. A system dynamics model was developed based on the primary published works of Forrester (1969, 1971), Schumpeter (1962), Meadows et al (1972) which were most recently reviewed by Meadows et al (2007) in their work on “Thinking in Systems” and Saeed (2010) in his work on “Economic Development, Creative Destruction and Urban Dynamics”. A specific focus on Manganese mining in the Northern Cape’s Kalahari basin was chosen to illustrate the impact of mineral resource beneficiation and the different value chain decisions over a 10-year period, based on the dynamic sensitivities of the selected input parameters.
A systems dynamic model was developed, inspired by the works of Meadows et al (2007) in systems thinking to describe the dynamic behaviour of the Manganese mining value chain and its impact on the economic activity of the Northern Cape region of John Taolo Gaetsewe (JTG), over the simulation period. Three value chain scenarios from “upstream mining” through “primary beneficiation” to “secondary beneficiation” of Manganese minerals were simulated on a system dynamics software platform and based upon the same Manganese Ore and input cost parameters. The patterns of feedback on each value chain scenario performance were evaluated based on a sensitivity analysis against power cost, rail cost and market price, as key dynamic parameters in the value chain. An improvement of the system dynamics model was developed, integrating the performance of the ”Secondary beneficiation” stage of the Manganese value chain to the system dynamics model describing the impact of infrastructure development on the economic activity of the Northern Cape, notwithstanding other industry contributions. Further system dynamics modelling of the secondary beneficiation as an integrated part of the economic system that includes human development and housing development, was conducted to further establish the impact of the secondary beneficiation scenario on infrastructure development and overall economic activity of the JTG region. The socio-economic development model was based upon the principle of relative attractiveness (Forrester, 1969) and the assertion by Perkins et al (2005) of the causal relationship between infrastructure development and economic development.
Based upon the analysis of the research result of the dynamic simulation of the secondary beneficiation scenario, a framework for developing, evaluating and selecting beneficiation opportunities in South Africa‘s Manganese industry was established. The framework describes the key policy and investment decisions along the value chain in the Manganese industry, and identifies key drivers of performance at each stage of the value chain investment. The framework also highlights the potential areas of impact.
The research introduces the ability to integrate the feedback loop system when simulating the potential performance of a Manganese resources value chain stage. The feedback mechanism that system dynamics modelling provides in the Vensim tool makes the tool relevant for the simulation of policy intervention in the Manganese mineral beneficiation scenario analysis and the same could be applied in other mineral commodities. The system dynamics model has demonstrated, by using the balance feedback variable, the impact of power (Eskom, 2012) and logistics capacity (Transnet, 2012), constraints on the ability of the Manganese resource value chain to meet the targeted depletion rates, irrespective of market demand. The second important contribution of this research is the ability to integrate the impact of single and multiple variables, and observe the impact of each variable using the integrated Monte Carlo function in the Vensim DSS (Vensim, 2010). By establishing a pattern of performance in various output elements in the model and their sensitivity to input variations, the investors can make bold and informed decisions at various stages of the Manganese resources value chain.
In conclusion, the research recommends that the proposed framework be limited to use as a starting point to establish the necessary interventions. However it emphasises the need to conduct at great length, the normal business case and necessary feasibility studies that are required for any capital investment. The research highlights the dynamic nature of environmental conditions as a limitation to the application of the framework, and suggests that the framework be used carefully as a pre-condition to conventional business case studies when making investment or policy decisions.