The WSJ Drucker Management 250 List: NC State’s Input into the Ratings

Every year about this time, the Wall Street Journal reveals its list of the Management Top 250 firms, which lists America’s Best-Run Companies.  The announcement, which appeared in today’s WSJ, identifies some of the top companies, with Amazon ranked at #1, followed by Microsoft, Apple, Alphabet, and Cisco.

NC State’s Supply Chain Resource Cooperative has played an important role in the development of these ratings, since the early days of its development.  Development of the model began in 2014, and included several prototype phases, with increasingly larger samples of companies included. Lawrence Crosby, then the chief data scientist at the Drucker Institute’s KH Moon Center for a Functioning Society, designed the model and, with the Drucker Institute’s Rick Wartzman and Zach First, oversaw its development.

One hundred sixty-nine indicators were analyzed before settling on 37 that went into the original model.  Professor Rob Handfield met with Lawrence Crosby during that period, and discussions focused primarily on how supply chain innovation and social responsibility metrics would figure into the list of top companies.  Based on several iterations of discussions, reviews of metrics, and benchmarking, the team settled on a system for assessing two important indications of supply chain performance.

All indicators were judged against the following criteria:

• They needed to be rigorously developed based on sound statistical methods.

• They needed to capture the essence of a specific Drucker principle.

• They needed to have a sufficiently high correlation with the other indicators of the same dimension—providing assurance that each one was actually measuring the same aspect of corporate effectiveness. For example, each indicator in the area of Customer Satisfaction had to correlate highly with other indicators in that category.

In this manner, two important indicators were developed for the Management Ratings, as shown below:

Supply Chain Resource Cooperative: Innovation Rating Metrics related to spend management, category management, strategic sourcing and supplier relationship management. Through a process developed by North Carolina State University professor Robert Handfield, a review is conducted of publicly available information, as well as interviews and surveys of corporate procurement specialists.

 

Supply Chain Resource Cooperative: Social Responsibility Rating Supply-chain policies, practices and results, including audits and lawsuits, with respect to human relations and the environment. Through a process developed by North Carolina State University professor Robert Handfield, a review is conducted of publicly available information, as well as interviews and surveys of corporate procurement specialists.

As noted above, the ratings were produced through a process developed not by me, but my former PhD student, Yung-Yun Huang.  Her dissertation completed in 2017 utilized Machine-Based Learning algorithms to scan third party news feeds, assess corporate commitment to sustainable supply chain principles, and evaluated the entire set of Fortune 500 companies on their progress towards truly sustainable supply chains.

Our list of top companies is similar, but not exactly the same as the WSJ Top Managed List.  For example, our list of top companies in the “Innovation” category includes the following:

1 Apple
2 Microsoft
3 P&G
4 Verizon
5 PepsiCo
6 3M
7 Colgate-Palmolive
8 Eli Lily
9 Abbott Labs
10 TJ Maxx

On the other hand, the top companies for the “Social Responsibility” rating, which taps into the top companies that have sustainable supply chains, include the following.  This is a somewhat different list, but reflects the importance of sustainability as a core component of supply chain excellence.  This is a theme that we have continued to focus on in the research of the SCRC.

1 Apple
2 Hewlett Packard
3 TJ Maxx
4 Manulife Financial
5 Praxair
6 Hess
7 Micron Technology
8 Ball Corporation
9 Sanmina Corporation
10 3M

We are proud to be a part of this on-going initiative, and I personally want to thank Yung-Yun for the great work she has done in developing this Machine-based Learning algorithm that is contributing to this work.