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11.4.3 Measuring Systems Engineering Productivity
Author(s) -
Wang Gan,
Shernoff Alex,
Saleski Lori,
Deal John C.
Publication year - 2010
Publication title -
incose international symposium
Language(s) - English
Resource type - Journals
ISSN - 2334-5837
DOI - 10.1002/j.2334-5837.2010.tb01151.x
Subject(s) - productivity , productivity model , measure (data warehouse) , partial productivity , variety (cybernetics) , software deployment , computer science , work (physics) , production (economics) , industrial engineering , systems engineering , engineering management , engineering , software engineering , economics , total factor productivity , microeconomics , database , mechanical engineering , artificial intelligence , macroeconomics
Productivity measurement has long been employed to guide economic activities. Engineering organizations, as labor‐intensive economic entities, have used a variety of labor productivity metrics to capture the underlying productivity and efficiency (a reciprocal notion), in their ever‐enduring attempts to produce more outputs with fewer human resources. Depending on the functional disciplines, some widely embraced and de facto standard measures range from hours per drawing in hardware engineering, to lines of code per hour or day practiced in software engineering. Systems engineering, a relatively young profession still struggling to find its identity at times, has not yet reached an agreement on how it should capture its productivity and efficiency. However, such a measure is critical in managing resources, improving processes and work output, and establishing its utility as a bona‐fide field of engineering in production of goods and services. This paper presents a proposed systems engineering productivity and efficiency (P&E) measure for system development projects, where there is usually the greatest amount of systems engineering content. This measure is defined by using parameters described in COSYSMO, a parametric estimating model for systems engineering. We provide the definition, recommendations for deployment, and relay our experience in applying this measure to organizational productivity and efficiency improvement.