Modelling job complexity in garment manufacture by inductive learning

Chi Leung Hui, K. C.K. Chan, K. W. Yeung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)


The lack of a good planning system in preventing operational problems occurring in garment manufacture was of concern to garment manufacturers. Neither mathematical nor statistical approaches have proved to be very effective in tackling this problem. The goal of this research is to establish a model of measuring operational problems by the use of a proven inductive learning technique known as automatic pattern analysis and classification system (APACS). To be effective in this particular application domain, real data on garment production were used. The accuracy of the resulting system is nearly 95 per cent compared with real performance, possibly significantly achieving the goal.
Original languageEnglish
Pages (from-to)34-44
Number of pages11
JournalInternational Journal of Clothing Science and Technology
Issue number1
Publication statusPublished - 1 Jan 1997


  • Clothing industry
  • Learning
  • Production management

ASJC Scopus subject areas

  • Business, Management and Accounting (miscellaneous)
  • Materials Science (miscellaneous)
  • General Business,Management and Accounting
  • Polymers and Plastics


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