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
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: firstname.lastname@example.org).
C. B. Cardeira is with the Department of Mechanical Engineering (IDMEC), Instituto Superior Técnico (IST), University of Lisbon, 1049-001 Lisboa, Portugal (e-mail: email@example.com).
P. Paranhos is with IDEPA – Ind. Passamanarias, Lda., Av. 1. de Maio, 71, 3700-227 S. João Madeira, Portugal (e-mail: firstname.lastname@example.org).
S. Ribeiro is with SisTrade – Software Consulting, R. Manuel Pinto de Azevedo, 64B, 4100-320 Porto, Portugal (e-mail: email@example.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: firstname.lastname@example.org).
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.