Current research

Exploring System Evolution and Change Management of System Architectures

Project summary: The objective of this research is (i) discovering the principles governing the evolvability of complex engineered systems as future needs emerge after deployment (termed service-phase evolution), and (ii) understanding how those principles can be connected to value-driven design. If this research is successful, we will better understand the extent to which system evolvability can be used to reduce negative emergent system behavior. Further, we will have quantitative measures of evolvability that can be used in conjunction with effective and existing decision making tools to improve the system design process.

  1. Long, D., and Ferguson, S., “Studying System Architecture Using Dynamic Change Propagation,” Journal of Mechanical Design, in press.
  2. Yadav, D., Long, D., Morkos, B., and Ferguson, S., 2019, “Estimating the Value of Excess: A Case Study of Gaming Computers, Consoles, and the Video Game Industry,” Proceedings of the ASME 2019 International Design Engineering Technical Conference & Computers and Information in Engineering Conference, DETC2019-98428.
  3. Long, D., and Ferguson, S., 2019, “An Excess Based Approach to Change Propagation,” Proceedings of the ASME 2019 International Design Engineering Technical Conference & Computers and Information in Engineering Conference, DETC2019-98404
  4. Long, D., and Ferguson, S., 2018, “Exploring System Architecture Attributes and Dynamic Change Propagation,” Proceedings of the ASME 2018 International Design Engineering Technical Conference & Computers and Information in Engineering Conference, DETC2018-86049.
  5. Allen, J., Mattson, C., Thacker, K., and Ferguson, S., 2017, “Design for Excess Capability to Handle Uncertain Product Requirements in a Developing World Setting,” Research in Engineering Design, doi: 10.1007/s00163-017-0253-8.
  6. Allen, J., Mattson, C. A., and Ferguson, S., 2016, “Evaluation of System Evolvability Based on Usable Excess,” Journal of Mechanical Design, 138(9): 091101, doi: 10.1115/1.4033989.
  7. Cansler, E., White, S., Ferguson, S., and Mattson, C., 2016, “Excess Identification and Mapping in Engineered Systems,” Journal of Mechanical Design, 138(8): 081103, doi: 10.1115/1.4033884.
  8. Watson, J. D., Allen, J. D., Mattson, C. A., Ferguson, S. M., 2016, “Optimization of Excess System Capability for Increased Evolvability,” Structural and Multidisciplinary Optimization, 53(6): 1277-1294, doi: 10.1007/s00158-015-1378-x.

 

Exploring the Design Challenges of Market Systems and Product Customization

Project summary: The objective of this project is exploring how integrating information from the marketing domain (customer preference models) and the engineering domain can help us identify which aspects of a product should be customized. To do this, our research explores how to capture and represent customer preference data, link it to engineering requirements, and understand how these requirements can be met through architecture flexibility. We also are working to facilitate the integration of preference modeling and engineering design by creating educational activities that foster multidisciplinary, interdisciplinary, and systems-level thinking in current and future engineers.

  1. Donndelinger, J., and Ferguson, S., 2020, “Design for Marketing Mix: The Past, Present, and Future of Market-Driven Product Design,” Journal of Mechanical Design, 142(6): 060801, doi: 10.1115/1.4045041.
  2. Dunbar, S., and Ferguson, S., 2019, “The Impact of Consumer Preference Distributions on Dynamic Electricity Pricing for Residential Demand Response,” Proceedings of the ASME 2019 International Design Engineering Technical Conference & Computers and Information in Engineering Conference, DETC2019-98219.
  3. Ferguson, S., “The perils of ignoring uncertainty in market simulations and product line optimization,” 2018 Sawtooth Software Conference.
  4. Shin, J., and Ferguson, S., 2016, “Exploring Product Solution Differences Due to Choice Model Selection in the Presence of Noncompensatory Decisions With Conjunctive Screening Rules” Journal of Mechanical Design, 139(2): 021402, doi: 10.1115/1.4035051.
  5. Joglekar, S., Von Hagel, K., Pankow, M., and Ferguson, S., 2016, “Exploring How Optimal Composite Design is Influenced by Model Fidelity and Multiple Objectives,” Composite Structures, doi: 10.1016/j.compstruct.2016.10.089.
  6. Belt, A., Von Hagel, K., and Ferguson, S., 2015, “Navigating Redesign and Market Desirability Implications when Considering Increased Product Variety,” Journal of Engineering Design, 26(7-9): 236-258, doi: 10.1080/09544828.2015.1024209.
  7. Ferguson, S. M., Olewnik, A. T., and Cormier, P., 2014, “A Review of Mass Customization Across Marketing,Engineering, and Distribution Domains Toward Development of a Process Framework,”Research in Engineering Design, 25(1): 11-30, doi: 10.1007/s00163-013-0162-4.
  8. Archer, J. R., Fang, T., Ferguson, S., and Buckner, G. D., 2014, “Multiobjective Design Optimization of a Variable Geometry Spray Fuel Injector,” Journal of Mechanical Design, 136(4), 044501, doi: 10.1115/1.4026263.
  9. Zeliff, K., Bennette, W., and Ferguson, S., 2016, “Multi-objective Composite Panel Optimization Using Machine Learning Classifiers and Genetic Algorithms,” Proceedings of the ASME 2016 International Design Engineering Technical Conference & Computers and Information in Engineering Conference, DETC2016-60125.
  10. Young, K., and Ferguson, S., 2016, “Informed Genetic Algorithm Crossover Operators for Market-Driven Design,” Proceedings of the ASME 2016 International Design Engineering Technical Conference & Computers and Information in Engineering Conference, DETC2016-59534.
  11. Von Hagel, K., and Ferguson, S., 2015, “Simulating Variability of Rework Cost and Market Performance Estimates in Product Redesign,” Proceedings of the ASME 2015 International Design Engineering Technical Conference & Computers and Information in Engineering Conference, DETC2015-47598.