Team Office of Sponsored Programs

James Bartholomew, Jenna Benkula, Molly Conley, James Grossman, Mary Hourihan, Becca Jewell, Patti Long Team Office of Sponsored Programs

Road Map • Scope, Goal, & Overview of Project • Assumptions & Constraints of Model • Model Demonstration • Possible Solutions • Recommendations & Conclusion

Scope • Evaluate the front-end proposal submission process by: • Identifying System Constraints • Identifying methods of exploiting constraints to increase throughput and improve customer service Desired outcomes • More proposals awarded • More money coming into the University • Improve customer relations

Overview of Project • Research the OSP grant and contract process • Gather and build a base of data • Build Arena Simulation of processes • Verify the process with Dr. Metlen • Validate the process with OSP • Simulate alternative scenarios • Present findings to OSP

Types of Entities • Draft • Full • Draft Turned Full • Pre-Proposal • Pre-Proposal turned Full Proposal • Electronic • Hardcopy • SPA1 • SPA2 • SPA3

Assumptions and Constraints • 50% of AA’s time pre-process • No pre-proposal as draft • Only one AA on at a time • No SPA ever helps another SPA • Constraint: no specific business rule • Data issues • Incorrect Assumptions • Communication

More Assumptions • Decision modules • 14% are already awarded • 25% have due dates • 80% of proposals to post are awarded • Decision modules within SPA’s process • Duration times • SPAs, AAs, and Polly process times • Business Rules • SPAs only work one proposal • Earliest due date first • Four day stamp for proposals without due date • Arrival rates • Based on last years numbers • Addition of entities randomly distributed

Arena Model • An overview of the entire process • Flow • Entities • Sub-models • Sequencing • Assigning Values/Properties • End Values

Base Model Values Each of these models were run for one year 30 times to show the possible variation, thus these values are averages of a normal distribution.

OSP’s Results

Validated Outputs OSP Results: 448(A) + 123(D) = 571 448/571 = 78.459% Model Results: 910.867(A) + 236.567(L) + 19.333(D) = 1166.767 910.867/1166.767 = 78.067%

Money • OSP 2008: $65,000,000 • Base Model: $232,397,418.76 • Partial Funding • Funding Over Time • Possibility of Not Being Funded • Data Integration • Average per proposal from raw data: $244,807.61 • Average per proposal from model: $255,138.79

Wait Time and Utilization • Inputs are not consistent. • Inability to model SPAs assisting other SPAs due to lack of Business Rule. Each of these were run for 30 years, but these values are averages.

Possible Solutions • Developed 14 scenarios • Scenarios were based on: • Our knowledge of the process • Current OSP process improvement initiatives • PI suggestions • Scenarios sought to: • Increase number of proposals awarded • Increase money coming into the university • Improve PI relations

Scenarios Investigated • Implementing four day rule • Electronic ESF form • Hiring someone to look for proposals and encourage PIs to submit to them • Resolving Workflow issues: Cayuse or Savvion • Various combinations of these

Base with 4 Day Rule • 4 Day Rule: OSP will not accept proposals that are due in less than 4 days

Base with Electronic ESF • All proposals go through due date distribution

Base with E-ESF and 4 Day Rule • Institute 4 day rule to try to improve results • Assuming no change in due date distribution

Base with Increase in Inputs • Increase in money coming from Washington • Hire another person to: • Research proposal opportunities • Identify appropriate PI • Identify opportunities for cross functional endeavors • Results: • Increase in proposal inputs • Demand planning for SPAs • Increase number of PI’s who respond to RFPs • Give OSP a positive face in the University community

Base with Increase in Inputs • Modeled using 30-40% increase in proposal input

Base with Increase in Inputs & 4 Day Rule • Institute 4 day rule to try to improve results

Base with Workflow • Implement a workflow system • Benefits: • It will decrease process significantly for all participants • Spot real time process errors • Increase PI satisfaction • Increased transparency of the process • Could also help with post award process • Real time data collector

Base with Decrease in Process Times • Modeled using 50% decrease in all process times due to better Work Flow

Base with Decrease in Process Times & Increase in Inputs • Modeled using 50% decrease in all process times due to better Work Flow & 30-40% increase in proposal input

Recommendations from our model: Short term: • Implement e-ESF Long term: • Consider using a system to manage workflow better • Hire someone to research proposal opportunities

Workflow Feasability • Cayuse424: $37,000/year • Decreases all process times by AT LEAST 50% • Detects real time errors • Customizable for non grants.gov proposals • Tracks data & data entry • Also aids in post award process • Savvion: $200,000 outlay; $80,000 for person to run/year, $3000 for licensing • Similar to Cayuse with more broad based implications across the University

Other suggestions we didn’t model: • Define job of OSP: find grants? write grants? • Apply for grant to do training seminars • Sit down with new PIs before they write their first proposal • Have feedback mechanism for PIs & other users of OSP • Look at FA – incentive to do research • Measure things that are important • Start HAC after process • Demand forecasting

Overall Reccomendations • Suggest PI’s utilize Sarah more • Hiring someone to search for proposals and notify PIs • Implement work flow system: • Cayuse? • Savvion?

Questions?

Team Office of Sponsored Programs