TY - GEN

T1 - Comparing different approaches for design of experiments (DoE)

AU - Tanco, Martín

AU - Viles, Elisabeth

AU - Pozueta, Lourdes

PY - 2009

Y1 - 2009

N2 - Design of Experiments (DoE) is a methodology for systematically applying statistics to experimentation. Since experimentation is a frequent activity at industries, most engineers (and scientists) end up using statistics to analyse their experiments, regardless of their background. OFAT (one-factor-at-a-time) is an old-fashioned strategy, usually taught at universities and still widely practiced by companies. The statistical approaches to DoE (Classical, Shainin and Taguchi) are far superior to OFAT. The aforementioned approaches have their proponents and opponents, and the debate between them is known to become heated at times. Therefore, the aim of this paper is to present each approach along with its limitations.

AB - Design of Experiments (DoE) is a methodology for systematically applying statistics to experimentation. Since experimentation is a frequent activity at industries, most engineers (and scientists) end up using statistics to analyse their experiments, regardless of their background. OFAT (one-factor-at-a-time) is an old-fashioned strategy, usually taught at universities and still widely practiced by companies. The statistical approaches to DoE (Classical, Shainin and Taguchi) are far superior to OFAT. The aforementioned approaches have their proponents and opponents, and the debate between them is known to become heated at times. Therefore, the aim of this paper is to present each approach along with its limitations.

KW - Classical

KW - Design of Experiments

KW - Shainin

KW - Statistical approach

KW - Taguchi

UR - http://www.scopus.com/inward/record.url?scp=78651542499&partnerID=8YFLogxK

U2 - 10.1007/978-90-481-2311-7_52

DO - 10.1007/978-90-481-2311-7_52

M3 - Conference contribution

AN - SCOPUS:78651542499

SN - 9789048123100

T3 - Lecture Notes in Electrical Engineering

SP - 611

EP - 621

BT - Advances in Electrical Engineering and Computational Science

ER -