Paper short abstract:

Supply chain risk management technologies promise through the combination of data sources and new algorithmic methods to aid in anticipating disruptions such as labor strikes. I unpack and problematize data and algorithms used within such systems and how they co-construct labor risks.

Paper long abstract:

Current supply chain risk management technologies promise through the combination of data sources and new algorithmic methods to aid in the anticipation and reaction to potential disruptions. Among the phenomena marked risky are also local protests, labor strikes, and other forms of unrest. These systems promise to curb labor risks to companies by either minimizing impacts of disruptions e.g. by reactively changing suppliers or aiding in the avoidance of reputational damage e.g. when labor disputes point to problematic working conditions. More recently also legal concerns make these technologies more appealing to companies as new regulations in the EU may require greater labor and transparency standards across supply chains. Concerningly, this technology also potentially undermines worker voice and labor action by making their impacts felt less by companies with more control over supply chain operations. In this paper, I unpack data and algorithms potentially used within such systems and related discourses by drawing mostly upon a situational analysis based on public documents, research papers, and other collected data. My analysis ultimately problematizes risk assessment regimes in supply chain management and points to how they also are involved in the production of certain forms of ignorance.