A method for comprehending and adapting complex supply chains in agriculture

Abstract

Success stories for applying supply chain methods to enhance agricultural industries are limited, despite their great potential. One key reason is that agricultural chains are subjected to the considerable managerial, social and biophysical complexity, which often leads to the inappropriate use of different methods. We capture supply chain complexity by formulating a matrix of biophysical by management factors. This is used to comprehend supply chain complexity and show how participants in agricultural chains can implement adaptation strategies that add value to their industry. Through various case studies we illustrate how adaptation strategies adopted by chain participants relate to different quadrants of the complexity matrix. An analysis of the literature based on this matrix also shows the suitability of different types of technical methods when used within adaptation strategies of each matrix quadrant. The complexity matrix aids the identification of the right strategies to use for the right problem through engagement with the right people.

Enhancing adaptation outcomes for transformation: climate change in the Australian wine industry

“…In some situations the full extent of the changes to the supply chain may not be realised until after the transformation has occurred. Viticulture is already a complex industry in terms of governance, where value chains involve multiple privately owned businesses, independent harvesters and processers and corporate wineries, so there is no simple or uniform supply chain configuration (Archer, Higgins, & Thorburn, 2009;Soosay et al, 2012). Furthermore, as with many agricultural enterprises, decision-making in the wine industry is often influenced by factors beyond profit, such as family, lifestyle and succession planning.…”

“…Furthermore, as with many agricultural enterprises, decision-making in the wine industry is often influenced by factors beyond profit, such as family, lifestyle and succession planning. Consequently, business objectives and considerations of risk may be quite different from classic business supply chain models (Archer et al, 2009). The influence of the supply chain on transformation decisions is not well understood.…”

Handbook of Operations Research in Agriculture and the Agri-Food Industry

“…The technology and tools for increasing the efficiency of ASCs have been researched in the past; however, their implementation has been very limited due to their mathematical formulation, which contrasts with the intuition of traditional decision-makers, their limitations on capturing the whole system dynamics, and the added complexity inherent of integrated models (Ahumada and Villalobos 2009a;Higgins et al 2009;. Therefore, it is our intention to review some of the most relevant research that has been done in agricultural supply chains and illustrate some ways in which operations research can be used to aid in the management of the supply chain.…”

“…For strategic decisions, such as where to place DCs or new growing regions for F&V, there are opportunities for mathematical optimisation to increase resilience to EWE. Archer et al (2009) formulates a complexity matrix for supply chains, where opportunities to increase resilience (typically strategic) face both high biophysical and managerial complexity. Archer et al (2009) goes on to describe the use of agent-based methods to capture the dynamic relationships under high uncertainty (such as EWE and market instability) and described applications in forestry, grains, sugar and canola.…”

“…Archer et al (2009) formulates a complexity matrix for supply chains, where opportunities to increase resilience (typically strategic) face both high biophysical and managerial complexity. Archer et al (2009) goes on to describe the use of agent-based methods to capture the dynamic relationships under high uncertainty (such as EWE and market instability) and described applications in forestry, grains, sugar and canola. In the case of F&V, agents would potentially represent chain actors (farmers, distributors, processors, retailers) along with the relationship between these actors and those currently outside the network (e.g.…”