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General Information
    • ISSN: 2010-3689
    • Frequency: Bimonthly (2011-2014); Monthly (Since 2015)
    • DOI: 10.18178/IJIET
    • Editor-in-Chief: Prof. Dr. Steve Thatcher
    • Executive Editor: Ms. Nancy Y. Liu
    • Abstracting/ Indexing: EI (INSPEC, IET), Electronic Journals Library, Google Scholar, Crossref and ProQuest
    • E-mail: ijiet@ejournal.net
Editor-in-chief
Prof. Dr. Steve Thatcher
University of South Australia, Australia
It is my honor to be the editor-in-chief of IJIET. The journal publishes good papers which focous on the advanced researches in the field of information and education technology. Hopefully, IJIET will become a recognized journal among the scholars in the filed of information and education technology.
IJIET 2015 Vol.5(4): 303-310 ISSN: 2010-3689
DOI: 10.7763/IJIET.2015.V5.521

Selection of the Most Suitable Statistical Process Control Approach for Short Production Runs: A Decision-Model

Pedro A. Marques, Carlos B. Cardeira, Paula Paranhos, Sousa Ribeiro, and Helena Gouveia
Abstract—Nowadays, customers are increasingly claiming not only for better quality products at the lowest possible cost, but also demanding customized solutions to satisfy their specific, sometimes unique, needs and wants. Due to this, manufacturing companies are seeking to adopt higher agile production models, such as mass customization strategies. In the quality control field, statistical process control (SPC) methods have been widely used to monitor process performance and detect abnormal situations in its behavior; however, traditional SPC approaches are usually not appropriate for small lot or batch sizes, for the start-up of a process, and for situations where a high variety of mixed products exist. Such situations are within the scope of the so called short production runs. Several SPC schemes have been proposed for short-run environments; all of them have their own advantages, shortcomings, and more suitable for certain production scenarios. This paper provides an up-to-date literature review on the topic, identifies classes of SPC short-run methods, and presents a decision-model that guides production managers in the choice of the most appropriate SPC short-run approach. The model was validated in a textile production company, and is being incorporated into a software package.

Index Terms—Decision-model, short-run, statistical process control (SPC).

P. A. Marques is with the Department of Strategy and Special Projects, ISQ – Instituto de Soldadura e Qualidade, Av. Prof. Dr. Cavaco Silva, 33, Taguspark, 2740-120 Porto Salvo, Portugal (e-mail: pamarques@isq.pt).
C. B. Cardeira is with the Department of Mechanical Engineering (IDMEC), Instituto Superior Técnico (IST), University of Lisbon, 1049-001 Lisboa, Portugal (e-mail: carlos.cardeira@tecnico.ulisboa.pt).
P. Paranhos is with IDEPA – Ind. Passamanarias, Lda., Av. 1. de Maio, 71, 3700-227 S. João Madeira, Portugal (e-mail: paula.paranhos@idepa.com).
S. Ribeiro is with SisTrade – Software Consulting, R. Manuel Pinto de Azevedo, 64B, 4100-320 Porto, Portugal (e-mail: sousa.ribeiro@sistrade.com).
H. Gouveia is with the Lab I&D, ISQ – Instituto de Soldadura e Qualidade, Av. Prof. Dr. Cavaco Silva, 33, Taguspark, 2740-120 Porto Salvo, Portugal (e-mail: hngouveia@isq.pt).

[PDF]

Cite: Pedro A. Marques, Carlos B. Cardeira, Paula Paranhos, Sousa Ribeiro, and Helena Gouveia, "Selection of the Most Suitable Statistical Process Control Approach for Short Production Runs: A Decision-Model," International Journal of Information and Education Technology vol. 5, no. 4, pp. 303-310, 2015.

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