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SCRC Innovation and Social Responsibility Supply Index: A Critical Input to “Drucker Management Top 250 List”

Research by NC State Poole College Professor Robert Handfield, director of the Supply Chain Resource Cooperative based Poole College, and Yung-Yun Huang, a recent graduate of North Carolina State University’s interdisciplinary doctoral program in operations research, supplied two of the 37 data inputs used in the Wall Street Journal’s inaugural “Management Top 250” ranking of companies, published recently.

Produced in partnership with the Drucker Institute, a unit in Claremont Graduate School, the “landmark ranking marks the first time the ideals and teachings of the late business guru Peter Drucker have been used to analyze and compare the performance of major U.S. companies,” the Wall Street Journal reported.

The ranking “is based on an analysis of 37 data inputs provided by 12 third-party sources.  Data for two of the categories used in the ranking – Innovation and Social Responsibility – are based on processes developed by Handfield and Huang. In the innovation category, metrics related to spending management, category management, strategic sourcing and supplier relationship management. In the social responsibility category, metrics related to supply chain policies, practices and results, including audits and lawsuits, with respect to human relations and the environment.

The nature of the Handfield-Huang index is based on empirical studies showing that “sustainable firms have superior financial outcomes” (e.g. Jacobs et al., 2010;).  Stated differently, there is evidence to suggest transparency of supply chains is simply a function of better management practices, improved systems, and more mature governance mechanisms, which in turn enables organizations to track, measure, and manage their global suppliers in a more transparent manner?

This question was pursued by Yung-Yun Huang in her dissertation completed in May 2017 at NC State University, entitled “Machine Learning in Automating Supply Management Maturity Ratings”.  Huang applied a rigorous machine-based learning automation process to assess the supply management maturity ratings of over 600 global companies. This required a comparison unigram and bigram feature settings, three text summarization techniques (full text, paragraph extraction and sentence extraction), and two different support vector machine approaches (one-against-one and one-against-all) on balanced and imbalanced datasets. The exhibited an 89.9% accuracy when compared to manually acquired maturity ratings, completed by prior generations of graduate students. The automation process was adapted as an external evaluation approach (through public online resources) to assess supply chain sustainability maturity, such as labor & human rights and environmental management.

Read the full report at The Wall Street Journal – Additional links: Methodology for the Management Top 250 Company Rankings ( and the Drucker Institute’s website