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What's taking so long? A collaborative method of collecting designers’ insight into what factors increase design effort levels in projects

Published online by Cambridge University Press:  11 September 2020

Alexander Freddie Holliman Open the ORCID record for Alexander Freddie Holliman [Opens in a new window] ,
Avril Thomson ,
Abigail Hird  and
Nicky Wilson
Alexander Freddie Holliman*
Affiliation:
Department of Design, Manufacturing and Engineering Management, University of Strathclyde, James Weir Building, GlasgowG1 1XJ, UK
Avril Thomson
Affiliation:
Department of Design, Manufacturing and Engineering Management, University of Strathclyde, James Weir Building, GlasgowG1 1XJ, UK
Abigail Hird
Affiliation:
Department of Design, Manufacturing and Engineering Management, University of Strathclyde, James Weir Building, GlasgowG1 1XJ, UK
Nicky Wilson
Affiliation:
Department of Design, Manufacturing and Engineering Management, University of Strathclyde, James Weir Building, GlasgowG1 1XJ, UK
*
Author for correspondence: Alexander Freddie Holliman, E-mail: alexander.holliman@strath.ac.uk
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Abstract

Design effort is a key resource for product design projects. Environments where design effort is scarce, and therefore valuable, include hackathons and other time-limited design challenges. Predicting design effort needs is key to successful project planning; therefore, understanding design effort-influencing factors (objective considerations that are universally accepted to exert influence on a subject, that is, types of phenomena, constraints, characteristics, or stimulus) will aid in planning success, offering an improved organizational understanding of product design, characterizing the design space and providing a perspective to assess project briefs from the outset. This paper presents the Collaborative Factor Identification for Design Effort (CoFIDE) Method based on Hird's (2012) method for developing resource forecasting tools for new product development teams. CoFIDE enables the collection of novel data of, and insight into, the collaborative understanding and perceptions of the most influential factors of design effort levels in design projects and how their behavior changes over the course of design projects. CoFIDE also enables design teams, hackathon teams, and makerspace collaborators to characterize their creative spaces, to quickly enable mutual understanding, without the need for complex software and large bodies of past project data. This insight offers design teams, hackathon teams, and makerspace collaborators opportunities to capitalize on positive influences while minimizing negative influences. This paper demonstrates the use of CoFIDE through a case study with a UK-based product design agency, which enabled the design team to identify and model the behavior of four influential factors.

Keywords

Design effortdesign space characterizationinfluential factorsproduct designproject management
Type
Research Article
Information
AI EDAM , Volume 34 , Special Issue 3: Thematic Collection: Fablabs, Makerspaces, and Design Spaces , August 2020 , pp. 341 - 362
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Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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References

Andersson, J, Pohl, J and Eppinger, SD (1998) A design process modelling approach incorporating nonlinear elements. Proceedings of 1998 DETC: ASME Design Theory and Methodology Conference. Atlanta, Georgia: American Society of Mechanical Engineers.Google Scholar
Bashir, HA and Thomson, V (1999) Metrics for design projects: a review. Design Studies 20, 263–277.CrossRefGoogle Scholar
Bashir, HA and Thomson, V (2001 a) An analogy-based model for estimating design effort. Design Studies 22, 157–167.CrossRefGoogle Scholar
Bashir, HA and Thomson, V (2001 b) Models for estimating design effort and time. Design Studies 22, 141–155.CrossRefGoogle Scholar
Bashir, HA and Thomson, V (2004) Estimating design effort for GE hydro projects. Computers & Industrial Engineering 46, 195–204.CrossRefGoogle Scholar
Benedetto, H, Bernardes, M.M.e.S and Vieira, D (2018) Proposed framework for estimating effort in design projects. International Journal of Managing Projects in Business 11, 257–274.Google Scholar
Bowen, S, Durrant, A, Nissen, B, Bowers, J and Wright, P (2016) The value of designers’ creative practice within complex collaborations. Design Studies 46, 174–198.CrossRefGoogle Scholar
Brauers, J and Weber, M (1988) A new method of scenario analysis for strategic planning. Journal of Forecasting 7, 31–47.CrossRefGoogle Scholar
Bryson, JM and Bromiley, P (1993) Critical factors affecting the planning and implementation of major projects. Strategic Management Journal 14, 319–337.CrossRefGoogle Scholar
Cho, S-H and Eppinger, SD (2005) A simulation-based process model for managing complex design projects. IEEE Transactions on Engineering Management 52, 316–328.Google Scholar
Christensen, KS (1985) Coping with uncertainty in planning. Journal of the American Planning Association 51, 63–73.CrossRefGoogle Scholar
Eckert, CM and Clarkson, PJ (2010) Planning development processes for complex products. Research in Engineering Design 21, 153–171.CrossRefGoogle Scholar
Eppinger, SD, Nukala, MV and Whitney, DE (1997) Generalised models of design interaction using signal flow graphs. Research in Engineering Design 9, 112–123.CrossRefGoogle Scholar
Fisher, RA (1949) The Design of Experiments, 5th Edn. Edinburgh: Oliver and Boyd.Google Scholar
Griffin, A (1993) Metrics for measuring product development cycle time. Journal of Product Innovation Management 10, 112–125.CrossRefGoogle Scholar
Griffin, A (1997) Modeling and measuring product development cycle time across industries. Journal of Engineering and Technology Management 14, 1–24. http://dx.doi.org/10.1016/S0923-4748(97)00004-0CrossRefGoogle Scholar
Hellenbrand, D, Helten, K and Lindemann, U (2010) Approach for development cost estimation in early design phases, Proceedings of DESIGN 2010, the 11th International Design Conference, Dubrovnik, Croatia, pp. 779–788.Google Scholar
Hird, A (2012) A Systems Approach to Resource Planning in New Product Development (Thesis [Eng. D]). Dept. of Design, M. and E.M., Glasgow: University of Strathclyde, 2012.Google Scholar
Ittner, CD and Larcker, DF (1997) Product development cycle time and organizational performance. Journal of Marketing Research, American Marketing Association 34, 13–23.Google Scholar
Jack, H (2013) Chapter 1 – An Overview of Design Projects BT, Engineering Design, Planning, and Management. Boston, MA: Academic Press, pp. 1–32.Google Scholar
Jacome, MF and Lapinskii, V (1997) NREC: risk assessment and planning of complex designs. IEEE Design & Test of Computers 14, 42–49.CrossRefGoogle Scholar
Jensen, MB, Semb, CCS, Vindal, S and Steinert, M (2016) State of the art of makerspaces – success criteria when designing makerspaces for norwegian industrial companies. Procedia CIRP 54, 65–70.CrossRefGoogle Scholar
Komssi, M, Pichlis, D, Raatikainen, M, Kindström, K and Järvinen, J (2015) What are Hackathons for? IEEE Software 32, 60–67.CrossRefGoogle Scholar
Luck, R (2013) Articulating (mis)understanding across design discipline interfaces at a design team meeting. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 27, 155–166.CrossRefGoogle Scholar
Pe-Than, EPP, Nolte, A, Filippova, A, Bird, C, Scallen, S and Herbsleb, JD (2019) Designing corporate Hackathons with a purpose: the future of software development. IEEE Software 36, 15–22.CrossRefGoogle Scholar
Pollmanns, J, Hohnen, T and Feldhusen, J (2013) An information model of the design process for the estimation of product development effort BT. In Abramovici, M and Stark, R (eds), Smart Product Engineering. Berlin, Heidelberg: Springer, pp. 885–894.Google Scholar
Raatikainen, M, Komssi, M, Bianco, Vd, Kindstöm, K and Järvinen, J (2013) Industrial experiences of organizing a Hackathon to assess a device-centric cloud ecosystem. 2013 IEEE 37th Annual Computer Software and Applications Conference, Kyoto, 2013, pp. 790–799.CrossRefGoogle Scholar
Rondinelli, DA, Middleton, J and Verspoor, AM (1989) Contingency planning for innovative projects. Journal of the American Planning Association 55, 45–56.CrossRefGoogle Scholar
Salam, A and Bhuiyan, N (2016) Estimating design effort using parametric models: a case study at Pratt & Whitney Canada. Concurrent Engineering 24, 129–138.CrossRefGoogle Scholar
Salam, A, Bhuiyan, N, Gouw, GJ and Raza, SA (2009) Estimating design effort for the compressor design department: a case study at Pratt & Whitney Canada. Design Studies 30, 303–319.CrossRefGoogle Scholar
Saravi, S, Joannou, D, Kalawsky, RS, King, MRN, Marr, I, Hall, M, Wright, PCJ, et al. (2018) A systems engineering Hackathon – a methodology involving multiple stakeholders to progress conceptual design of a complex engineered product. IEEE Access 6, 38399–38410.CrossRefGoogle Scholar
Serrat, J, Lumbreras, F and López, AM (2013) Cost estimation of custom hoses from STL files and CAD drawings. Computers in Industry 64, 299–309.CrossRefGoogle Scholar
Shai, O and Reich, Y (2004) Infused design. I. Theory. Research in Engineering Design 15, 93–107.Google Scholar
Shang, Z-G and Yan, H-S (2016) Product design time forecasting by kernel-based regression with gaussian distribution weights. Entropy 18, 231–248.CrossRefGoogle Scholar
Smith, RP and Eppinger, SD (1997) A Predictive Model of Sequential Iteration in Engineering Design. Catonsville, MD: Management Science.Google Scholar
Tatikonda, MV and Rosenthal, SR (2000) Technology novelty, project complexity, and product development project execution success: a deeper look at task uncertainty in product innovation. IEEE Transactions on Engineering Management 47, 74–87.Google Scholar
Wang, Z, Tong, S and Huang, L (2015) Research on the time prediction model of product variant design. 2015 IEEE International Conference on Mechatronics and Automation (ICMA), Beijing, China, IEEE, pp. 572–576.CrossRefGoogle Scholar
Xu, D and Yan, H-S (2006) An intelligent estimation method for product design time. The International Journal of Advanced Manufacturing Technology 30, 601–613.CrossRefGoogle Scholar
Yan, H-S and Shang, Z-G (2015) Method for product design time forecasting based on support vector regression with probabilistic constraints. Applied Artificial Intelligence 29, 297–312. http://dx.doi.org/10.1080/08839514.2015.993558CrossRefGoogle Scholar
Yan, HS and Xu, D (2007) An approach to estimating product design time based on fuzzy -nu-support vector machine. IEEE Transactions on Neural Networks 18, 721–731.Google Scholar
Yan, H, Wang, B, Xu, D and Wang, Z (2010) Computing completion time and optimal scheduling of design activities in concurrent product development process. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 40, 76–89.CrossRefGoogle Scholar
Zhigen, S and Yan, H (2011) Forecasting product design time based on Gaussian Margin Regression. IEEE 2011 10th International Conference on Electronic Measurement & Instruments, Chengdu, IEEE, Vol. 4, pp. 86–89.CrossRefGoogle Scholar
Zirger, BJ and Hartley, JL (1994) A conceptual model of product development cycle time. Journal of Engineering and Technology Management 11, 229–251.CrossRefGoogle Scholar