Using Theme and Domain Networking Approaches to Understand Complex Agri-Industrial Systems

Abstract

Complex systems involve a number of interconnected entities, which collectively exhibit emergence and behaviour that cannot be explained by merely studying the individual entities. Agri-industrial systems, such as sugarcane production, are generally complex due to the presence of many autonomous stakeholders operating under diverse conditions, and may therefore contain varying perspectives and interests. The identification of problems and opportunities in such systems requires an approach that will, as far as possible, consider the entire system and how individual entities interact. Network analyses have the capacity to describe a complex system, depicting these interactions. In addition, graph theory approaches can help to identify key points in the system where there are opportunities for improvement. This paper presents a methodology to assist researchers to make sense of complex matters in an agri-industrial context. In the South African sugar industry, it can be argued that systemic inefficiencies in the supply chain reduce optimum performance. Research conducted in two large milling areas is used to develop and demonstrate the use of network approaches to analyse supply chains and identify opportunities for improvement. The research developed two types of map: system domain networks and theme networks, which are found to be appropriate for drawing a first set of conclusions concerning a relatively unfamiliar complex system. Although the paper focuses on sugarcane, there is significant scope to apply these techniques across a broader spectrum of agri-industrial sectors.