Experience Based Decisional DNA (DDNA) to Support Sustainable Product Design

  • Muhammad Bilal AhmedEmail author
  • Cesar Sanin
  • Edward Szczerbicki
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 130)


This paper presents the idea of providing engineering design knowledge to designers working on sustainable product design and development process. The new product development process often requires significant amount of design knowledge which can be saved and recalled by designers during the design process. This knowledge is very important for successful sustainable product development as it can include material selection, product geometric features and process parameters, etc. This paper presents a decision support technique for recalling engineering design knowledge which can be useful for small and medium enterprises (SMES) involved in new product design and development. Proposed system is based on smart knowledge management technique called Set of Experience Knowledge Structure (SOEKS) and Decisional DNA. The SOEKS is a flexible and structured knowledge representation structure used to gain and store the experiential knowledge. The article includes a case study explaining the presented approach. Decision-making in industrial design will benefit from this study, as it includes capturing, storing and reusing of experience and knowledge of engineering design processes.


Sustainable product development Decisional DNA Set of experience knowledge structure Product design Product geometric features Material selection 


  1. 1.
    Hayes, C.C., Goel, A.K., Tumer, I.Y., Agogino, A.M., Regli, W.C.: Intelligent support for product design: looking backward, looking forward. J. Comput. Inf. Sci. Eng. 11, 021007 (2011)CrossRefGoogle Scholar
  2. 2.
    Hosseinpour, A., Peng, Q., Gu, P.: A benchmark-based method for sustainable product design. Benchmarking: Int. J. 22, 643–664 (2015)CrossRefGoogle Scholar
  3. 3.
    Nonaka, I., Takeuchi, H.: The knowledge-creating company: How Japanese Companies Create the Dynamics of Innovation. Oxford University Press, Oxford (1995)Google Scholar
  4. 4.
    Rodgers, P.A., Clarkson, P.J.: Knowledge usage in New Product Development (NPD) (1998)Google Scholar
  5. 5.
    Ahmed, M.B., Sanin, C., Szczerbicki, E.: Experience-Based Decisional DNA (DDNA) to Support Product Development. Cybernetics and Systems, pp. 1–13 (2018)Google Scholar
  6. 6.
    Hansen, M.T., Nohria, N., Tierney, T.: What’s your strategy for managing knowledge. Knowl.Manag. Yearb. 2000–2001, 1–10 (1999)Google Scholar
  7. 7.
    Rubina, O., Constantinides, E., de Vries, S.A.: The Internet of Things: the next big thing for new product development? J. Mark. Trends (2018)Google Scholar
  8. 8.
    Yang, C.J., Chen, J.L.: Forecasting the design of eco-products by integrating TRIZ evolution patterns with CBR and Simple LCA methods. Expert Syst. Appl. 39, 2884–2892 (2012)CrossRefGoogle Scholar
  9. 9.
    Ottosson, S.: Dynamic product development—DPD. Technovation 24, 207–217 (2004)CrossRefGoogle Scholar
  10. 10.
    Kim, S.J., Kara, S., Kayis, B.: Analysis of the impact of technology changes on the economic and environmental influence of product life-cycle design. Int. J. Comput. Integr. Manuf. 27, 422–433 (2014)CrossRefGoogle Scholar
  11. 11.
    Inoue, M., Lindow, K., Stark, R., Tanaka, K., Nahm, Y.-E., Ishikawa, H.: Decision- making support for sustainable product creation. Adv. Eng. Inform. 26, 782–792 (2012)CrossRefGoogle Scholar
  12. 12.
    Bauch, C.: Lean product development: Making Waste Transparent (2004)Google Scholar
  13. 13.
    Eppinger, S., Ulrich, K.: Product Design and Development. McGraw-Hill Higher Education, New York (2015)Google Scholar
  14. 14.
    Unger, D., Eppinger, S.: Improving Product Development Process Design: A Method for Managing Information Flows, Risks, and Iterations. J. Eng. Des. 22, 689–699 (2011)CrossRefGoogle Scholar
  15. 15.
    Ullman, D.G.: The Mechanical Design Process. McGraw-Hill Science/Engineering/Math, New York (2015)Google Scholar
  16. 16.
    Bereketli, I., Genevois, M.E.: An integrated QFDE approach for identifying improvement strategies in sustainable product development. J. Clean. Prod. 54, 188–198 (2013)CrossRefGoogle Scholar
  17. 17.
    Sanin, C., Szczerbicki, E.: Experience-based knowledge representation: SOEKS. Cybern. Syst. Int. J. 40, 99–122 (2009)CrossRefGoogle Scholar
  18. 18.
    Sanin, C., Szczerbicki, E.: Knowledge supply chain system: a conceptual model. Knowledge Management: Selected Issues, 79–97 (2004)Google Scholar
  19. 19.
    Shafiq, S.I., Sanín, C., Szczerbicki, E.: Set of experience knowledge structure (SOEKS) and decisional DNA (DDNA): past, present and future. Cybern. Syst. 45, 200–215 (2014)CrossRefGoogle Scholar
  20. 20.
    Shafiq, S.I., Sanin, C., Toro, C., Szczerbicki, E.: Virtual Engineering Object (VEO): Toward experience-based design and manufacturing for industry 4.0. Cybern. Syst. 46, 35–50 (2015)CrossRefGoogle Scholar
  21. 21.
    Shafiq, S.I., Sanin, C., Toro, C., Szczerbicki, E.: Virtual Engineering Process (VEP): a knowledge representation approach for building bio-inspired distributed manufacturing DNA. Int. J. Prod. Res. 54, 7129–7142 (2016)CrossRefGoogle Scholar
  22. 22.
    Mansor, M.R., Sapuan, M.S.: Materials Selection. Concurrent Conceptual Design and Materials Selection of Natural Fiber Composite Products, pp. 27–44. Springer, Singapore (2018)Google Scholar
  23. 23.
    Lu, T., Gupta, A., Jayal, A., Badurdeen, F., Feng, S.C., Dillon Jr, O., Jawahir, I.: A framework of product and process metrics for sustainable manufacturing. In: Advances in Sustainable Manufacturing, pp. 333–338. Springer, Heidelberg (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Muhammad Bilal Ahmed
    • 1
    Email author
  • Cesar Sanin
    • 1
  • Edward Szczerbicki
    • 2
  1. 1.The University of NewcastleCallaghanAustralia
  2. 2.Gdansk University of TechnologyGdanskPoland

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