טכניון מכון טכנולוגי לישראל
הטכניון מכון טכנולוגי לישראל - בית הספר ללימודי מוסמכים  
Ph.D Thesis
Ph.D StudentGideon Samid
SubjectEstimating the Effort to Achieve Research and Development
Objectives
DepartmentDepartment of Chemical Engineering
Supervisor Professor Emeritus Kehat Ephraim


Abstract

The challenge of estimating the effort to achieve R&D objectives is the challenge to appraise the unknown.  As daunting as this challenge may be, its import is sufficient motivation to take it on.


Four quantitative methodologies forming an R&D Suit have been developed.  Chief among them is BiPSA:  Binary Polling Scenario Analysis:  a tool to round up, identify, and extract the scarce sources of relevant knowledge applicable to the task at hand.  BiPSA is based on allowing a small team to write an R&D scenario, and inviting a larger circle of knowledgeable sources to voice a binary opinion:  Is that scenario more likely to happen, or more likely not to happen?  That opinion is accompanied by a measure of confidence in the answer.  When its time is up, a scenario either materialized, or remained an unrealized plan.  This reality check serves as feedback used to constantly evaluate the respondents.  And this re-evaluation improves the integration process that combines all the answers to a summary response for the current scenario.  That integration is based on a novel scheme for processing ordinal-binary input.  It appears that this scheme has broader applications for handling ordinal data.


 In summary, BiPSA offers a validity-rated estimate for an R&D mission, or part thereof.  Such estimate may be combined with other sources which typically show mutual inconsistency.  The array of sometimes congruent and sometimes conflicting estimates is handled via the Validity Resolution Methodology, (VRM), and its results are taken as input by the Knowledge Realization Measurement (KRM) methodology which was developed for the purpose of computing a quantitative measure for the amount of knowledge left to be realized in the analyzed R&D project.  KRM is based on the view that the common denominator to all R&D enterprises, regardless of stage, discipline, budget, or duration is the aspect of knowledge realization.  And the more knowledge that is being realized in the course of the project, the more R&D-like the project will be.  By measuring the knowledge left to be realized, one measures the true metrics of R&D progress.  And this measurement is critical for rational management, and balanced funding. 


The insight gained through these three methodologies is then reflected in a surprise-ready resourcing plan for the project.  That is the concern of the fourth methodology in the suit:  BAROU:  Balanced Reduction of Uncertainty.  It makes use of the quantitative data derived by the three former tools, and plans for a capability to tolerate a gradual, or a sudden resource reduction, or alternatively, a gradual or a sudden increase in the importance and urgency of the project, which might require a faster pace.


 Alongside these quantitative tools, a few qualitative conclusions were realized.  Most surprising among them is the notion that the credibility of the estimate of the effort to achieve an R&D objective should be the driving force for managing the project as a whole.  This is because to increase credibility means to remove the project unknown, and  to alleviate risk.  The field is new, the work is embryonic, but it looks like the embryo of a formidable player.  Research and development propel our economic engines.  Accurate estimates thereof will improve its efficiency, and hence contribute to the general welfare.