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PROCESS IMPROVEMENT: AN EXPLORATORY DATA ANALYSIS APPROACH WITHIN AN INTERVAL‐BASED OPTIMIZATION FRAMEWORK
Author(s) -
SARAIVA PEDRO M.,
STEPHANOPOULOS GEORGE
Publication year - 1998
Publication title -
production and operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.279
H-Index - 110
eISSN - 1937-5956
pISSN - 1059-1478
DOI - 10.1111/j.1937-5956.1998.tb00436.x
Subject(s) - computer science , problem statement , process (computing) , operations research , pointwise , set (abstract data type) , benchmarking , space (punctuation) , industrial engineering , statement (logic) , management science , economics , mathematics , mathematical analysis , management , law , political science , engineering , programming language , operating system
This article revisits an old problem; “systematically explore the information contained in a set of operating data records and find from it how to improve operational performance by taking the appropriate decisions in the space of operating conditions,” thus leading to continuous process improvement. A series of industrial case studies within the framework of the internships in the Leaders for Manufacturing (LFM) program at Massachusetts Institute of Technology led us to a reexamination of the traditional formulations for the above problem. The resulting methodology is characterized by the following features: (1) problem statement and solutions are expressed in terms of hyperrectangles in the decision space, replacing conventional pointwise results; (2) data‐driven, nonparametric learning methodologies were advanced to produce the requisite mapping between performance and decisions; (3) operating performance is in essence multifaceted, leading to a multiobjective problem, which is treated as such. The proposed methodology has been applied to a number of industrial examples and in this paper we provide a brief overview only of those that can be discussed in the open literature.