New Product Ideation: Technology Platforms, AI, and Data Driven Methods

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New Product Ideation: Technology Platforms, AI, and Data Driven Methods

By Carlos M. Rodriguez posted 01-03-2022 17:49

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The kHUB Curator Team members have each been assigned a BoK section to own. This includes seeking, editing and sharing content related to that section. The curators are also sharing their perspective of various sub-sections of their chapter and contributing personal examples, experience, or related articles corresponding to the subject matter.

Chapter 4 Insights # 6– Product Design & Development Tools

“If you have always done it that way, it is probably wrong.”

—

Charles Kettering

Ideation is essential in new product development initiatives. Being a critical stage, ideation implies the use of different methods to generate ideas, evaluate their potential as formal new concepts, and ultimately evaluate the feasibility in generating revenues for the firm.

Industry has recently started to evaluate different technology platforms to support ideation. Some of these are extension of previous work such as IDEATRIZ, which is based on heuristics derived from the TRIZ methodology. Others apply artificial intelligence with data mining techniques to generate a semantic ideation network in combination with a visual concept model aim to provoke ideation.

Ideation Methods

Ideation methods stretch from the traditional qualitative methods and techniques to computational creativity research ones. Several ideation techniques are available to the designer, product and service developer, and new product development team. These are: Ishikawa diagrams (root-cause analysis), SCAMPER, Mind mapping, Brainstorming (including brainwriting and brain drawing), Morphological Analysis, Opposite thinking, Analogy thinking, and TRIZ among others. For a detailed description of all other ideation techniques, the reader might like to review the kHUB PDMA Knowledge Hub, Product Design and Development Tools, Chapter 4, NPDP Certification Body of Knowledge, Second edition, 2020 (Rodriguez, 2020).

IDEATRIZ Innovation Methodology

IDEATRIZ is a methodology based on heuristics for new product ideation. It aims at generating ideas that are both new and valuable to a company's customers. It is based on heuristics derived from TRIZ, Value Analysis, Disruptive Innovation and the General Theory of Innovation (GTI).

The methodology includes four main stages as shown in figure 1.

Figure 1: IDEATRIZ New Product Ideation Methodology

Source: De Carvalho, M., Grillo, J. and Tessari, R. 2015. Methodology and Software for New Product Ideation. Procedia Engineering 131.

According to the IDEATRIZ methodology, the actual creative and ideation process starts with stage 2: Heuristics to increase value. These heuristics are a compilation of those from TRIZ and GTI regarding functions in particular. Stage 3 focused on resolving contradictions through the use of separation principles. After the ideation process is completed, a final selection and evaluation task is performed through the use of multicriteria and the assignment of values to each criterion.

Data Driven Creative Concept Methodology

The central focus of this methodology is to support design through the analysis and incorporation of emergent technologies during early stages of the new product development. It involves two main components: Computational concept generation and computational concept evaluation. The logical flow is shown in figure 2. The computation concept generation component includes knowledge input regarding different technologies generally created with text mining and data analytical techniques and processed through network analysis. Later, conceptual evaluation uses combinational processes to generate new ideas from associations and links previously identified. Distances between ideas show possible analogies which have the potential for more creative designs.

Figure 2: Data Driven Approach to Design

Source: Adapted from Han, J., Forbes, H., Shi, F., Hao, J. and Schaefer, D. (2020), “A data-driven approach for creative concept generation and evaluation”, In: Proceedings of the Design Society: DESIGN Conference, pp. 167-176.

Data ideation driven approaches have included technology space maps to support visualization of design opportunities. For an application of this approach please review Luo et al. (2018). The use of artificial intelligence supported on data driven approaches continues to gain interest among industry experts. A recent exploration attempt to combine semantic ideation network analysis with visual conceptualization with promising contribution to the ideation process. Still, these methodologies are far for providing the strategic dimension of creativity. In concluding, our new product development efforts and creative insights need from the traditional ideation approaches and image ideation sustain in combinational software platforms.

Further Reading

Albers, A., Burkhardt, N. and Meboldt, M. (2005). “Spalten Problem Solving Methodology in the Product Development”, In: International Conference on Engineering Design ICED. Melbourne, Australia.

Daly, S. R., Yilmaz, S., Christian, J., Seifert, C. and Gonzalez, R. (2012), "Design Heuristics in Engineering Concept Generation", Journal of Engineering Education, Vol. 101, pp. 601-629.

De Carvalho, M., Grillo, J. and Tessari, R. (2015). Methodology and Software for New Product Ideation. Procedia Engineering 131.

Han, J., Forbes, H., Shi, F., Hao, J. and Schaefer, D. (2020), “A data-driven approach for creative concept generation and evaluation”, In: Proceedings of the Design Society: DESIGN Conference, pp. 167-176.

Ireland, R. and Liu, A. (2018), Application of data analytics for product design: Sentiment analysis of online product reviews. CIRP Journal of Manufacturing Science and Technology 23.

Luo, J., Song, B., Blessing, L. and Wood, K. (2018), Design opportunity conception using the total technology space map. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 32(4): 449-461.

Yilmaz, S., Daly, S. R., Seifert, C. M. and Gonzalez, R. (2016), "Evidence-based design heuristics for idea generation", Design Studies, Vol. 46, pp. 95-124.

About the Author

Carlos Rodriguez

Carlos M Rodriguez is an Associate Professor of Marketing and Quantitative Methods and Director of the Center for the Study of Innovation Management, CSIM in the College of Business, Delaware State University, USA. His publications have appeared in the Journal of Business Research, Journal of Business to Business Marketing, Journal of International Marketing, International Marketing Review, Management Decision, International Journal of Business and Social Sciences, Journal of Business and Leadership, and Journal of Higher Education Research & Development among others and several conference proceedings. Currently, he serves in the editorial board of several journals. His research interests are in the areas of entrepreneurship and strategic capabilities, luxury branding and experiences, product design and new product development teams, and relationship marketing. He recently published the book entitled Product Design and Innovation: Analytics for Decision Making centered in the design techniques and methodologies vital to the product design process. He is engaged in several international educational, research, and academic projects, as well, as, international professional activities.

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