Time to market prediction using type-2 fuzzy sets

P Baguley, T Page, V Koliza, P Maropoulos

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

Purpose - Time to market is the essential aim of any new product introduction process. Performance measures are simple quantities that indicate the state of manufacturing organisations and are used as the basis of decision-making at this crucial early stage of the process. Fuzzy set theory is a method for using qualitative data and subjective opinion. Fuzzy sets have been used extensively in manufacturing for applications including control, decision-making, and estimation. Type-2 fuzzy sets are a novel extension of type-1 fuzzy sets. Aims to examine this subject. Design/methodology/approach - This research explores the increased use of type-2 fuzzy sets in manufacturing. In particular, type-2 fuzzy sets are used to model "the words that mean different things to different people". Findings - A model that can leverage design process knowledge and predict time to market from performance measures is a potentially valuable tool for decision making and continuous improvement. A number of data sources, such as process maps, from previous research into time to market in a high technology products company, are used to structure and build a type-2 fuzzy logic model for the prediction of time to market. Originality/value - This paper presents a demonstration of how the type-2 fuzzy logic model works and provides directions for further research into the design process for time to market. © Emerald Group Publishing Limited.
Original languageEnglish
Pages (from-to)513-520
Number of pages8
JournalJournal of Manufacturing Technology Management
Volume17
Issue number4
DOIs
Publication statusPublished - 2006

Keywords

  • Decision making
  • Fuzzy sets
  • Formal logic
  • Marketing
  • Forecasting
  • Process control
  • Time series analysis
  • Product development

Fingerprint Dive into the research topics of 'Time to market prediction using type-2 fuzzy sets'. Together they form a unique fingerprint.

  • Cite this