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Launched in 2005, the Food Systems Leadership Institute (FSLI) is a 2-year leadership development program primarily focused on academic leaders. As of spring 2020, FSLI has enrolled 15 Cohorts, training a total of 347 Fellows. In 2020, a review of the graduated cohorts was undertaken to understand both the range of institutions served by FSLI and the career trajectory of the 319 graduated Fellows who participated in Cohorts 1-14. A total of 78 different organizations have enrolled FSLI participants, including 79% of the 1862 Land Grant Universities, 68% of the 1890 Land Grant Universities, and 12% of the 1994 Institutions, in addition to fewer participants from non-Land Grant public universities, government institutions, industry, and institutions located outside of the U.S. FSLI has served participants from 84% of the US and Territories. The review showed that 46% of Fellows in Cohorts 1-14 advanced into higher positions of academic administration and they filled 169 new hierarchical positions, including college-level, university-level and system-level administration positions in higher education. Similar trajectories were found in industry-organizations, although in smaller numbers. In all, 470 administrative and leadership positions have been filled in these organizations by the 319 members of the cohorts reviewed. While career progression is a limited measure of leadership success, this brief review supports the hypothesis that participation in the FSLI program contributes to the careers of the enrolled participants.

With the growth of unstructured data, opportunities to generate insights into supply chain risks in low cost countries (LCCs) are emerging. Sourcing risk has primarily focused on short-term mitigation. This paper aims to offer an approach that uses newsfeed data to assess regional supply base risk in LCC’s for the apparel sector, which managers can use to plan for future risk on a long-term planning horizon.

This paper demonstrates that the bulk of supplier risk assessments focus on short-term responses to disruptions in developed countries, revealing a gap in assessments of long-term risks for supply base expansion in LCCs. This paper develops an approach for predicting and planning for long-term supply base risk in LCC’s to address this shortfall. A machine-based learning algorithm is developed that uses the analysis of competing hypotheses heuristic to convert data from multiple news feeds into numerical risk scores and visual maps of supply chain risk. This paper demonstrates the approach by converting large amounts of unstructured data into two measures, risk impact and risk probability, leading to visualization of country-level supply base risks for a global apparel company.

This paper produced probability and impact scores for 23 distinct supply base risks across 10 countries in the apparel sector. The results suggest that the most significant long-term risks of supply disruption for apparel in LCC’s are human resource regulatory risks, workplace issues, inflation costs, safety violations and social welfare violations. The results suggest that apparel brands seeking suppliers in the regions of Cambodia, India, Bangladesh, Brazil and Vietnam should be aware of the significant risks in these regions that may require mitigative action.

This approach establishes a novel approach for objectively projecting future global sourcing risk, and yields visually mapped outcomes that can be applied in forecasting and planning for future risks when considering sourcing locations in LCC’s.