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The Sherwin Research Group aims to improve decision-making, risk mitigation, reliability, and systems thinking by solving problems faced in industry settings. Dr. Sherwin's work is motivated by 18 years of industry experience, where he has observed and directly experienced the inefficiencies associated with strategic and operational decision-making. Today, data is abundant but not always efficiently utilized 

or converted into usable information. Decisions are often made qualitatively, reactively, and without consideration for the overall system. To solve these problems, Dr. Sherwin and his team utilize data analytics, mathematical modeling, machine learning, and decision frameworks to simplify complex situations and solve industry problems. Our goal is to improve supply chain networks and the systems within those networks to benefit the global economy and, ultimately, the public good. Among other publications, Dr. Sherwin has published work in Omega, IISE Transactions, and the International Journal of Production Economics.

Our Team

Olivia Greene

Undergraduate Researcher

Supply Chain Management

Ben Sadler

Undergraduate Researcher

Computer Science


Sarah Martin

Undergraduate Researcher

Supply Chain Management

Jacob Hanzlik

SOBA Research Fellow

MBA Candidate


Puraav Karnavat

Research Intern

Current Projects



Obsolescence Prediction Tools in Critical Supply Chain Management

  • The objective of this research is develop tools to inform strategic and tactical decision-making by identifying key factors that assure supplier and product continuity and developing machine learning models to predict the probability and timing of product and supplier obsolescence

  • Funded by: Naval Sea Systems Command (NAVSEA) Naval Engineering Education Consortium (NEEC)

  • Research Partner: University of Tennessee Knoxville


Optimal Production Scheduling in a Continuous Flow Food Manufacturing Environment with Sequential Operations and Parallel Machines

  • This research aims to develop methods to optimize production scheduling in a continuous-flow food manufacturing environment with sequential operations and parallel machines.


Supply Chain Analytics Case Study

  • This work includes designing a case study, based on a medical device manufacturing supply chain, for use in supply chain management courses with the purpose of enhancing student learning by connecting the various decisions within a supply chain (forecasting, production planning, inventory management, and technology) through data analysis and tactical and strategic decision-making.


Effect of Political Disruption on a Critical Supply Chain

  • Cobalt is an important element used in computers and electric vehicles. Over 50% of cobalt is found in areas currently experiencing political unrest. This project aims to analyze the current situation and pose potential solutions. 


A Standard Framework for Supply Chain Risk Management

  • Several frameworks, maturity models, and standards exist across various industries and disciplines. In addition, international organizations have adopted many of these standards to assess performance and compliance and provide certifications to organizations that meet standard criteria. However, there does not appear to be a universally accepted standard for the governance of supply chain management. In this research, we propose a standard framework for managing risk across an organization's supply chains.

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An Empirical Evaluation of Supplier Performance Assessment and Prediction in a Critical Supply Chain

  • This research aims to present a decision framework that can be utilized during the supplier selection process. The proposed framework demonstrates a mathematical model, trained on empirical data, to predict the reliability of a given supplier being considered during the procurement process. Using such a model alleviates an organization's reliance on institutional knowledge, which is becoming increasingly scarce in critical supply chains.

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