1. James Bartholomew, Jenna Benkula, Molly Conley, James Grossman, Mary Hourihan, Becca Jewell, Patti Long

2.  Scope, Goal, & Overview of Project  Assumptions & Constraints of Model  Model Demonstration  Possible Solutions  Recommendations & Conclusion

3.  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

4.  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

5.  Draft  Full  Draft Turned Full  Pre-Proposal  Pre-Proposal turned Full Proposal  Electronic  Hardcopy ▪ SPA1 ▪ SPA2 ▪ SPA3

6.  Scope, Goal, & Overview of Project  Assumptions & Constraints of Model  Model Demonstration  Possible Solutions  Recommendations & Conclusion

7.  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

8.  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

9.  Scope, Goal, & Overview of Project  Assumptions & Constraints of Model  Model Demonstration  Possible Solutions  Recommendations & Conclusion

10.  An overview of the entire process  Flow  Entities  Sub-models  Sequencing  Assigning Values/Properties  End Values

11. BASE # to Post # Pre- proposals# Late# Denied # Already Awarded# Drafts $ to Post & Already Awarded$ Late$ Denied 910.86718.767236.56719.3342.4317.33$243,211.23 $ 64,791,325.39 $ 4,425,172.71 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.

12. Total of above coded as “A” in status (awarded)448 Total of above coded as “R” in status (in review) 111 Total of above coded as “S” in status (submitted)338 Total of above coded as “I” in status (inactive)79 Total of above coded as “D” in status (declined)123 Total of above coded as “P” in status (presumed rejected)1 Total of above coded as “C” in status (cancelled in house)6 Total of above coded as “W” in status (withdrawn by PI)5 TOTAL1111

13. 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% BASE # to Post# Late# Denied 910.867236.56719.33 Total coded as “A” in status (awarded)448 Total coded as “D” in status (declined)123

14.  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

15.  Inputs are not consistent.  Inability to model SPAs assisting other SPAs due to lack of Business Rule. BASE SPA 1 Utilization SPA 2 Utilization SPA 3 Utilization AA Utilization Polly Utilization 48.114%71.032%76.243%41.485%0.1% Each of these were run for 30 years, but these values are averages.

16.  Scope, Goal, & Overview of Project  Assumptions & Constraints of Model  Model Demonstration  Possible Solutions  Recommendations & Conclusion

17.  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

18.  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

19.  4 Day Rule: OSP will not accept proposals that are due in less than 4 days BASEBASE with 4 Day Rule Number Awarded 911No statistically significant difference Number Late23736

20.  All proposals go through due date distribution BASEBASE with E-ESF Number Awarded 911854 Number Late237294

21.  Institute 4 day rule to try to improve results  Assuming no change in due date distribution BASEBASE with E- ESF Adding 4 Day Rule Number Awarded 911854599 Number Late 2372941

22.  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

23.  Modeled using 30-40% increase in proposal input BASEBASE with 30-40% increase Number Awarded 911598 Number Late237846

24.  Institute 4 day rule to try to improve results BASEBASE with 30-40% increase Add 4 Day Rule Number Awarded 911598928 Number Late 237846598

25.  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

26. BASEBASE with 50% decrease in process times due to better WF Number Awarded 9111110 Number Late23736  Modeled using 50% decrease in all process times due to better Work Flow

27.  Modeled using 50% decrease in all process times due to better Work Flow & 30-40% increase in proposal input BASEBASE with 50% decrease Base with 50% decrease & 30- 40% increase Number Awarded 91111101452 Number Late 23736>1

28.  Scope, Goal, & Overview of Project  Assumptions & Constraints of Model  Model Demonstration  Possible Solutions  Recommendations & Conclusion

29. Short term:  Implement e-ESF Long term:  Consider using a system to manage workflow better  Hire someone to research proposal opportunities

30.  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

31.  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

32.  Suggest PI’s utilize Sarah more  Hiring someone to search for proposals and notify PIs  Implement work flow system:  Cayuse?  Savvion?

33.  Scope, Goal, & Overview of Project  Assumptions & Constraints of Model  Model Demonstration  Possible Solutions  Recommendations & Conclusion