Skip to content

Similarity Metric for Sourcing Support

Dr. Daniel A. Finke 
Research Associate
Pennsylvania State University
Friday, January 12, 2018   2:30-3:30pm
JDT 410



Although specifications exist for technical data packages (TDP) used in government sourcing, the quality, format, and quantity of information included significantly varies across the population of all TDPs available for bid and proposal. According to the Defense Logistics Agency Internet Bid Board System, there are thousands of components currently out for bid.  The volume of requests limits manufacturer’s ability to evaluate each and every one to determine if it is a suitable match for their capabilities.  The evaluation process includes analysis of the TDP and manual comparison to their specific manufacturing capabilities; manufacturing processes, tooling, material, tolerances, and production capacity.  TDP analysis looks at the quality and format of the information to determine if the information is sufficient to manufacture the component.  Oftentimes the component is a good fit for the manufacturing company, but the requested production quantity is so low that it does not make economic sense to bid.

There exists a need for a method to grade the quality of a given technical data package so that it can be compared objectively to others. This TDP figure of merit can be used by sourcing organizations to improve the quality of the TDP which will improve the quality and quantity, and reduce the cost of the resulting bids.  It can also be used by manufacturing organizations to establish internal risk levels that translate into costs to the government.  The quality or figure of merit value for a given TDP should factor into the bidder’s risk, but there are other factors that should be considered including similarity to current/past products, production quantities, and delivery schedule

This presentation will discuss the development of a Figure of Merit (FoM) used to quantify the quality of a given Technical Data Package (TDP) by combining Subject Matter Expert assessments and machine learning algorithms.   In addition, a Similarity Metric that enables sourcing agencies to group low requested quantity TDPs into a single package to make it more attractive for vendors to bid will be discussed.  The similarity metric can also be utilized by the vendors to quickly identify other TDPs available for bid that are similar to a TDP they are interested in.  Lastly, a Bidder’s Risk Metric will be presented that leverages the FoM and Similarity Metric to provide a mechanism for potential bidders to assess the risk involved with respect to their particular manufacturing capabilities for a given TDP.


Dr. Daniel Finke is a Research Associate in the Production Engineering Department at the Applied Research Laboratory, The Pennsylvania State University. Much of Dr. Finke’s experience in applied research and development is within the US Navy shipbuilding domain collaborating on projects in Advanced Manufacturing Enterprise with a focus on production and capacity planning and manufacturing system modeling and analysis.

Dr. Finke received his PhD in Industrial Engineering (2010) and MS in Industrial Engineering and Operations Research (2002) from the Pennsylvania State University and a BS in Industrial Engineering from New Mexico State University (2000).  His current research interests include simulation-based decision support, planning and scheduling, heuristic algorithm development and implementation, agent-based simulation and modeling, and process improvement

The flagship campus of the University of Tennessee System and partner in the Tennessee Transfer Pathway.

Report an accessibility barrier