Advancing our knowledge on how supply chain actors engage with geographic regions
By Tiago Reis, PhD student at Université Catholique de Louvain and ESR fellow in the COUPLED project
COUPLED is a four-year, European Training Network granted by the EC under Horizon 2020.
The products we consume on a daily basis are supported by a very complex supply chain that operates all over the world. Raw materials are sourced from different places across the globe, from iron ore in Australia to coffee in Colombia, but the relationship between these supply chain actors and the geography of their sourcing strategies has never been systematically measured. Knowing when, why and how companies change, or don’t change, their sourcing decisions is important to understand how they create sustainability solutions or problems, like agricultural intensification or deforestation, and contribute to rural development.
Therefore, in our article that was published in One Earth journal on July 24, we propose the concept of geographic stickiness in the trade of global commodities. We define geographic stickiness as the stability of the commercial relationships between regions and supply chain actors. In the article we develop this concept, and a methodology for measuring it, using the supply chain of soy in Brazil as a case study. We look at the whole export supply chain, from municipalities of production and trade, through traders, and to consuming countries, to understand how ‘sticky’ they are to a certain geographic region. We use the data from trase.earth (preview in Figure A).
The methodology we propose is innovative because we can measure the strength and intensity of these relationships for the first time. We use metrics from temporal network analyses, such as topological overlap, and others, that look at the similarity of supply chain network configurations over different snapshots in time (Figure B is a graphical summary of the article). In other words, we take a photograph of all commercial relationships in one given year, for example, 2003. We take another photograph of all commercial relationshops in another year, which can be 2004 or any other, then we compare the two photographs and measure the topological differences or the changes in the commercial relationships between regions and supply chain actors. We can look at the scores of the entire supply chain or disaggregate at the individual level, for instance, the commercial relationships of a specific municipality, trader or importing country. A value of 1 means that the configuration of the commercial linkages of this particular individual did not change at all. If we get 0, it means it reconfigured completely. All values between indicate different levels of configuration.
To make an analogy for the meaning of this study, let’s look at gravity. Although we have always intuitively understood gravity by knowing and feeling its effects on us, it was only after Isaac Newton’s elaborations that we became able to measure and calculate gravity and its relationship to other physical entities. With Newton’s conceptualization, we have since become able to use this knowledge for our purposes such as engineering buildings and machines, including the aeroplane.
Similarly, we have always known that supply chain actors have stable or volatile relationships with producing regions over time. These companies source the inputs and goods that they need from certain regions to supply final products to their customers, for example, cocoa from Ghana to make Belgian chocolate. However, we have never conceptualized and measured the strength and intensity of these relationships. By doing so, we can now further investigate and establish connections and associations with other processes, phenomena, and indicators to improve our knowledge of how supply chains and landscapes interact. For example, how much deforestation can be caused by a company that is geographically volatile in its sourcing pattern, that is, a company that buys raw materials from different places every year? In addition, how much deforestation can be avoided by companies that are more stable geographically? What are the impacts on local livelihoods, agricultural intensification, local labor and income opportunities caused by sticky or non-sticky companies? By being in a better position to inquire about these questions, we can propose better solutions to the problems that these interactions may create.
When we apply our methodology to the soy export supply chain in Brazil, we find that the soy traders with the largest market shares (those who buy the greatest proportions of Brazil’s soy production) also exhibit the highest stickiness scores. This means that they have been sourcing from more or less the same regions over time. This group of traders also show higher deforestation risks. This finding seems obvious, I know. The novelty here is the ability we now have to quantify this relationship.
Furthermore, those companies that are more committed to certain places (high stickiness) seem to be more concerned about deforestation in their supply chains since, for example, they are mostly signatories of zero-deforestation commitments, which are sustainability commitments made by companies not to buy products from recently-deforested lands. Knowing the stickiness of companies’ sourcing can help improve such commitments.
We hypothesize that stickier companies are more likely to induce changes in production if they require, for example, that their suppliers adopt sustainability good practices and do not deforest to grow croplands. In contrast, non-sticky companies are more likely to simply stop sourcing from unsustainable regions instead of driving transformation. Stickiness, in this case, may represent engagement, trust, and long-term and sustainable relationships between a company and the places of production, which could make it more likely that zero-deforestation demands are transmitted to the ground. Knowledge of the stickiness of trading relationships can also inform the negotiation of trade agreements, such as the EU-Mercosur. What would be the feasibility and potential effectiveness of banning the imports of particular products from certain places? That depends on stickiness.
So the next question to be addressed is, why does this happen? In other words, why do the same companies that are geographically sticky also have the highest deforestations risks, despite having committed to removing deforestation from their supply chains? We still do not know, but we hypothesize about several factors that we are exploring in the next chapter, to come out soon.
Read the full article HERE.
