Product: Management and Development
Product: Management and Development
Original Article

Test planning based on ontological models constructed from product usage profiles

Lucas Barboza Zattar Paganin, Milton Borsato, Juliana Schmidt, Augusto Domingos, Roberto Shigueru Sato

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Design for Reliability (DfR) is such an approach that can be referred as a set of activities that attempts to ensure the reliability of a product throughout all stages of its lifecycle. Recent research works on the subject, as well as common practices by industry, have revealed no cases of implementation of DfR in the initial stages of new product development, such as the definition of test plans based on product usage profiles. Therefore, the main goal of this research is to elaborate a method, based on an ontological model, that allows the determination of the most appropriate test plan, by considering usage characteristics of products. In order to develop this method, the Design Science Research methodological framework was used. Achieved results show that the proposed solution is an efficient and easy-to-use method, which potentially improves product reliability throughout the product lifecycle.


design for reliability, new product development, test planning, ontology, product lifecycle.


AHLERS, D. et al. Challenges for information access in multi-disciplinary product design and engineering settings. In: INTERNATIONAL CONFERENCE ON DIGITAL INFORMATION MANAGEMENT, 10., 2015, USA. Proceedings… USA: IEEE, 2015. p. 109-114.

AKERKAR, R.; SAJJA, P. Knowledge-based systems. Sudbury: Jones & Bartlett Publishers, 2010.

ASHRAFI, M.; DAVOUDPOUR, H. A hierarchical bayesian network to compare maintenance strategies based on cost and reliability. International Journal of Industrial Engineering: Theory Applications and Practice, v. 26, n. 3, p. 1-11, 2019.

BILGIN, G.; DIKMEN, I.; TALATBIRGONUL, M. Ontology evaluation: an example of delay analysis. Procedia Engineering, v. 85, p. 61-68, 2014.

BOUNCKEN, R. B. et al. Coopetition in new product development alliances: advantages and tensions for incremental and radical innovation. British Journal of Management, v. 29, n. 3, p. 391-410, 2018.

BRALLA, J. G. Design for excellence. New York: McGrawHill, 1996.

CORCHO, Ó. et al. ODEval: a tool for evaluating RDF(S), DAML+OIL, and OWL concept taxonomies. In: M Bramer, M.; Devedic, V. Artificial intelligence applications and innovations. Boston: Kluwer Academic Publishers, 2004. p. 369-382.

CROWE, D.; FEINBERG, A. Design for reliability. Boca Raton: Press CRC, 2001.

CUI, A. S.; WU, F. The impact of customer involvement on new product development: contingent and substitutive effects. Journal of Product Innovation Management, v. 34, n. 1, p. 60-80, 2017.

DA XU, L.; XU, E. L.; LI, L. Industry 4.0: state of the art and future trends. International Journal of Production Research, v. 56, n. 8, p. 2941-2962, 2018.

DAML Ontology Library. Available from: <>. Access in: 13 Dec. 2019. DMOZ. Available from: <>. Access in: 13 Dec. 2019.

DRESCH, A.; LACERDA, D. P.; ANTUNES JUNIOR, J. A. V. Design science research: a method for science and technology advancement. New York: Springer, 2015.

FERNANDEZ-BREIS, J. T. et al. Quality evaluation framework for bio-ontologies. In: INTERNATIONAL CONFERENCE ON BIOMEDICAL ONTOLOGY, 2009, Buffalo. Proceedings… Manchester: Nature Proceding,, 2009. p. 127-131.

GANGEMI, A. Ontology design patterns for semantic web content. In: Lecture notes in computer science: including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics. In: INTERNATIONAL SEMANTIC WEB CONFERENCE, 4., 2005. Proceedings… Galway, Ireland: Lecture Notes in Computer Science, vol. 3729. p. 262-276.

GENNARI, J. H. et al. The evolution of Protégé: an environment for knowledge-based systems development’, International Journal of Human-Computer Studies, v. 58, n. 1, p. 89-123, 2003. GIL, A. C. Como elaborar projetos de pesquisa: métodos e técnicas de pesquisa social. 6. ed. São Paulo: Atlas, 2002. p. 22-23.

GOUGH, D. et al. An introduction to systematic reviews. London: Sage Publications Ltd., 2012.

GRUBER, T. R. A translation approach to portable ontology specifications. Knowledge Acquisition, v. 5, n. 2, p. 199-220, 1993. GRÜNINGER, M.; FOX, M. S.; GRUNINGER, M. Methodology for the design and evaluation of ontologies. In: INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELIGENCE (IJCAI95). WORKSHOP ON BASIC ONTOLOGICAL ISSUES IN KNOWLEDGE SHARING, 1995, Galway. Proceedings… Galway, Ireland: Computer Science . p. 1-10.

HEVNER, A.; CHATTERJEE, S. Design research in information systems: theory and practice. USA: Springer Science & Business Media, 2010. v. 22.

HORRIDGE, M. Justification based explanation in ontologies. 2011. 304 f. Thesis (PhD in Computer Science). Engineering and Physical Sciences, University of Manchester, Manchester, Inglaterra, 2011.

HORRIDGE, M.; MUSEN, M. Snap-SPARQL: a java framework for working with SPARQL and OWL. In: Ünay, D. Çataltepe, Z. Aksoy, S. Lecture notes in computer science: including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics. USA: Springer Verlag, 2016. p. 154-165. I N T E R N AT I O N A L O R G A N I Z AT I O N F O R

STANDARDIZATION – ISO. ISO 9126: Information technology – software product quality. Geneva: ISO, 2000. p. 34.

KOLLIA, I.; GLIMM, B.; HORROCKS, I. SPARQL query answering over OWL ontologies. In: Crawford, B. Castro, C. Monfroy E.. Lecture notes in computer science: including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics. USA: Springer Verlag, 2011. p. 382-396.

KUEI-CHEN, C.; YEU-SHIANG, H.; I-CHI, H. A study of software reliability growth with imperfect debugging for time-dependent potential errors. Computer-Aided Design, v. 26, n. 3, p. 376-393, 2019.

LEOPOLDINO, K. D. M. et al. Creativity techniques: a systematic literature review. Product: Management & Development, v. 14, n. 2, p. 95-100, 2016.

LU, Y. Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration, p. 1-10, 2017.

NOY, N. F.; MACGUINNESS, D. L. Ontology development 101: a guide to creating your first ontology. Stanford: Stanford University, 2001.

NUÑEZ, D. L.; BORSATO, M. An ontology-based model for prognostics and health management of machines. Journal of Industrial Information Integration, v. 6, p. 33-46, 2017.

PAHL, G.; BEITZ, W. Engineering design: a systematic approach. Edited by WALLACE, K. Título. 2nd ed. Darmstadt, Germany: USA: Springer Science & Business Media, 2013.

PEFFERS, K. et al. A design science research methodology for information systems research. Journal of Management Information Systems, v. 24, n. 3, p. 45-77, 2007.

SAUNDERS, M. et al. Research methods for business students. London: Pearson Education Limited, 2012.

SERRA, I.; GIRARDI, R.; NOVAIS, P. Evaluating techniques for learning non-taxonomic relationships of ontologies from text. Expert Systems with Applications, v. 41, n. 11, p. 5201-5211, 2014.

SHROUF, F.; ORDIERES, J.; MIRAGLIOTTA, G. Smart factories in Industry 4.0: a review of the concept and of energy management approached in production based on the Internet of Things paradigm. In: INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT, 2014, USA. Proceedings… USA: IEEE, 2014. p. 697–701. http://dx.doi. org/10.1109/IEEM.2014.7058728.

SIRIN, E. et al. Pellet: a practical OWL-DL reasoner. Journal of Web Semantics, v. 5, n. 2, p. 51-53, 2007.

SIRIN, E.; PARSIA, B. SPARQL-DL: SPARQL query for OWL-DL. In: CEUR WORKSHOP, 2007, Cidade. Proceedings… Berlin: Springer, 2007.

STAAB, S.; STUDER, R. Handbook on ontologies. In: STAAB, S.; STUDER, R. (Ed.). Handbook on ontologies. 2nd ed. Berlin: Springer Berlin Heidelberg, 2009.

STANFORD CENTER FOR BIOMEDICAL INFORMATICS RESEARCH. PROTÉGÉ: versão 5.2.0. Stanford: Stanford University, 2017. Available from: <https://protege.stanford. edu/>. Access in: 13 Dec. 2019.

TANASIJEVIC, M. et al. A fuzzy-based decision support model for effectiveness evaluation – a case study of examination of bulldozers. International Journal of Industrial Engineering: Theory, Applications and Practice, v. 26, n. 6, 2019.

TRAPPEY, A. J. C. et al. A review of essential standards and patent landscapes for the Internet of things: a key enabler for industry 4.0. Advanced Engineering Informatics, v. 33, p. 208-229, 2017.

WAGHMODE, L. Y.; PATIL, R. B. Reliability analysis and life cycle cost optimization: a case study from Indian industry. International Journal of Quality & Reliability Management, v. 33, n. 3, p. 414-429, 2016.

YANG, L. et al. Design‐for‐reliability implementation in microelectronics packaging development. Microelectronics International, v. 28, n. 1, p. 29-40, 2011.

ZHONG, R. Y. et al. Intelligent manufacturing in the context of industry 4.0: a review. Engineering. Elsevier Ltd, v. 3, n. 5, p. 616-630, 2017.

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