Process improvement: An exploratory data analysis approach within an interval-based optimization framework

Pedro M. Saraiva, George Stephanopoulos

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)19-37
Number of pages19
JournalProduction and Operations Management
Volume7
Issue number1
DOIs
StatePublished - 1998

Keywords

  • Exploratory data analysis
  • Interval-based optimization
  • Machine learning
  • Process improvement
  • Quality management

ASJC Scopus subject areas

  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Management of Technology and Innovation

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