Abstract
Very often product development is seen as a process where designers iterate through several design cycles until they converge upon a design that satisfies all of the necessary requirements—design within a single generation. If one takes the view that products change (i.e. adapt and evolve), a broader view must be adopted to capture the drivers of design adaptation across multiple product generations. This paper offers a new multi-generation conceptual framework of parametric design adaptation for consumer products, called the Artisan–Patron (AP) framework, and a complementary computational model. The AP framework captures the interaction between manufacturers (the Artisan) and consumers (the Patron) by structuring the various relevant information (e.g., consumer taste, government policy, cost of raw materials, etc.). Additionally, based on this framework, a corresponding computational model is developed, which allows engineers to find optimal settings for the design variables in a dynamic multi-generation environment. The utility of the conceptual framework and the computational model is demonstrated by considering the parametric design adaptation of the automobile with respect to two design parameters—engine horsepower and weight—based on historical automotive industry data.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Abbreviations
- N :
-
Total number of competitive products (or manufacturers) within a given product segment
- i :
-
Index denoting product (or manufacturer) \({i.i{\in}N}\)
- j :
-
Index denoting competition of i \({(j\neq i)\cdot j{\in} N}\)
- t :
-
Index denoting time period or generation. t ≥ 0
- fe :
-
Fuel economy
- at :
-
0–60 mph acceleration time
- sf :
-
Measure of safety which is related to the fatality rate in two-car crashes
- hp :
-
Vehicle aggregate horsepower
- w :
-
Vehicle weight
- A :
-
Set of critical-to-value attributes (CVA) in a product
- a :
-
A single CVA, \({a\in A}\)
- \({{\bf z}_{i,t}^A}\) :
-
Vector of critical-to-value attributes (CVA) in product i at time \({t: {\bf z}_{i,t}^A =(z_{i,t}^1, {\ldots}, z_{i,t}^a, {\ldots}}\))
- \({{\hat{{\bf z}}}_{i,t}^A}\) :
-
Estimate of \({{\bf z}_{i,t}^A}\) – forecasted performance
- z a,I :
-
Ideal attribute setting where the consumer is unwilling to pay for further improvement
- z a,C :
-
Critical setting for attributes where the consumer is unwilling to purchase the product even if all other attributes are at an ideal setting
- z a,0 :
-
Baseline attribute settings (i.e. for the baseline product)
- \({\hat{{z}}_t^{fe}}\) :
-
Estimated average fuel economy (fe) at time t
- \({\hat{{z}}_t^{at}}\) :
-
Estimated average acceleration time (at) at time t
- \({\hat{{z}}_t^{sf}}\) :
-
Estimated average safety (sf) at time t
- \({z_{t}^{CAFE}}\) :
-
Sales-weighted (car & truck) adjusted fuel economy standard for the manufacturer’s product mix at time t
- \({R\left( {{\bf z}_{i,t}^A}\right)}\) :
-
Reward value based on \({{\bf z}_{i,t}^A }\)
- \({{\hat{{R}}}\left( {{\bf z}_{i,t}^A}\right)}\) :
-
Estimate of \({R\left( {{\bf z}_{i,t}^A}\right)}\)
- B :
-
Set of decision/design variables in a product design
- b :
-
A single decision variable, \({b\in B}\)
- \({{\bf x}_{i,t}^B}\) :
-
Vector of design or decision variables for product i at time t: \({{\bf x}_{i,t}^B}\) = (\({x_{i,t}^1,{\ldots}, x_{i,t}^b , {\ldots})}\)
- \({{\bf x}_{i,t}^{B,U} ({\bf x}_{i,t}^{B,L} )}\) :
-
Upper (and lower) bounds on design variables of product i at time t
- \({x_t^{hp}}\) :
-
Vehicle aggregate horsepower at time t
- \({x_t^w}\) :
-
Vehicle aggregate weight at time t
- \({c_{i,t}^T (\hat{{c}}_{i,t}^T )}\) :
-
Total production cost (estimate) for product i at time t
- \({c_{i,t}^V (\hat{{c}}_{i,t}^V )}\) :
-
Variable cost (estimate) for product i at time t
- \({c_{i,t}^I (\hat{{c}}_{i,t}^I )}\) :
-
Investment cost (estimate) for product i at time t
- \({c_{i,t}^R (\hat{{c}}_{i,t}^R )}\) :
-
Regulation cost (estimate) for product i at time t
- \({\hat{{c}}_t^{\rm weight}}\) :
-
Estimated cost as a function of overall automobile weight at time t (average for all manufacturers)
- \({\hat{{c}}_t^{\rm engine}}\) :
-
Estimated cost associated with delivering engine horsepower at time t (average for all manufacturers)
- \({\hat{{c}}_t^{CAFE}}\) :
-
Estimated CAFE policy penalty formula at time t(average for all manufacturers)
- V 0 :
-
Baseline value (i.e. at reference point)
- V i,t :
-
Value of product i at time t or average maximum willingness-to-pay for product i at time t
- \({\bar{{V}}_t }\) :
-
Average value of all products at time t
- \({v\left( {\hat{{z}}_{i,t}^a}\right)}\) :
-
Value of a single CVA ‘a’ for product i at time t
- p i,t :
-
Price of product i at time t
- p 0 :
-
Baseline price (i.e. at reference point)
- \({\bar{{p}}_t}\) :
-
Average price of all products at time t
- D i,t :
-
Demand for product i at time t
- D T,t :
-
Total market demand (for whole industry) at time t
- D T,0 :
-
Total market demand (for the whole industry) at the reference state (i.e. baseline total market demand)
- π i,t :
-
Profit of a single manufacturer i at time t
- Π t :
-
Profit of the industry (without investment cost) at time t
- \({\gamma_t^a }\) :
-
Measure of attribute ‘a’ importance. % time attribute ‘a’ is experienced by consumer during product use
- η 0 :
-
Price elasticity of demand at the reference state (baseline price elasticity)
- η t (\({\hat{{\eta}}_t )}\) :
-
Price elasticity (estimate) of demand at time t
- K t :
-
Constant-negative slope of the demand curve for the market segment of interest at time t
- ρ t :
-
Time adjusted CAFE penalty, ($) per mpg below standard
- \({\alpha_1^{fe} ,\alpha_2^{fe} ,\alpha_3^{fe}}\) :
-
Regression coefficients used for fuel economy (fe) transfer function
- \({\alpha_1^{at} ,\alpha_2^{at} ,\alpha_3^{at}}\) :
-
Regression coefficients used for acceleration time (at) transfer function
- \({\alpha_1^{sf} ,\alpha_2^{sf} ,\alpha_3^{sf}}\) :
-
Regression coefficients used for fuel economy (sf) transfer function
- β 1,t & β 2,t :
-
Parameters of engine cost
- β 3,t :
-
Cost of automobile raw materials per pound
- β 4,t :
-
Multiplier to account for economic fluctuation
References
Ahmad S., Greene D. (2005) Effect of fuel economy on automobile safety: A reexamination. Transportation Research Record: Journal of the Transportation Research Board, 1941: 1–7
Angel A., Browning T. (2008) Designing systems for adaptability by means of architecture options. Systems Engineering Journal 9: 125–146
Asiedu P., Gu P. (1998) Product life cycle cost analysis: State of the art review. International Journal of Production Research 36: 883–908
Ben-Akiva M., Lerman S. R. (1985) Discrete choice analysis: Theory and applications to travel demand. MIT Press, Cambridge
Ben-Arieh D., Qian L. (2003) Activity-based cost management for design and development stage. International Journal of Production Economics 83: 169–183
Berryman A. (1992) The origins and evolution of the predator-prey theory. Ecology 73(5): 1530–1535
Cardoso M., Salcedo R., Feyode Azevedo S., Barbosa D. (1997) A simulated annealing approach to the solution of MINLP problems. Computers and Chemical Engineering 21(12): 1349–1364
Castagne S., Curran R., Rothwell A., Price M., Benard E., Raghunathan S. (2008) A generic tool for cost estimating in aircraft design. Research in Engineering Design 18: 149–162
CBO-Congressional Budget Office, (2002). Reducing gasoline consumption: Three policy options, Congressional Budget Office (CBO) study, November, 2002, www.cbo.gov .
Clark K.B. (1985) The interaction of design hierarchies and market concepts in technology evolution. Research Policy, 14: 235–251
Cook H.E. (1997) Product management: Value, quality, cost, price, profit, and organization. Kluwer Academic, Amsterdam
Cook, H. E. (2005). Design for six sigma as strategic experimentation, American Society for Quality.
Cook H. E., Wu A. (2001) On the valuation of goods and selection of the best design alternative. Research in Engineering Design 13: 42–54
Donndelinger J., Cook H. E. (1997) Methods for analyzing the value of vehicles. SAE Transactions: Journal of Passenger Cars 106: 1263–1281
Eckert C., Clarkson P. J., Zanker W. (2004) Change and customization in complex engineering domains. Research in Engineering Design 15: 1–21
Eppinger S. D., Nukala M. V., Whitney D. E. (1997) Generalized models of design iteration using signal flow graphs. Research in Engineering Design 9(2): 112–123
FARS-Fatality Analysis Reporting System, (2008). http://www-fars.nhtsa.dot.gov/ . Accessed June 5, 2008.
Ferguson, S., Siddiqi, A., Lewis, K. & de Weck, O. (2007). Flexible and reconfigurable systems: Nomenclature and review. ASME Design Automation Conference, September 4–7, 2007, Las Vegas, Nevada, USA, DETC2007-35745.
Ferreira E., Salcedo R. (2001) Can spreadsheet solvers solve demanding optimization problems. Computer Applications in Engineering Education 9(1): 49–56
Fricke E., Schulz Armin P. (2005) Design for changeability (DfC): Principles to enable changes in systems throughout their entire lifecycle. Systems Engineering 8(4): 342–359
Georgiopoulos P., Johnson M., Papalambros P. Y. (2005) Linking optimal design decisions the theory of the firm: The case of resource allocation. ASME Journal of Mechanical Design 127(5): 358–366
Hazelrigg G. A. (2003) Validation of engineering design alternative selection methods. Engineering Optimization 35(2): 103–120
Hellman, K. H., & Heavenrich, R. M., (2004). Light-duty automotive technology and fuel economy trends: 1975 through 2004, Office of Transportation and Air Quality—U.S., Environmental Protection Agency (http://www.epa.gov/otaq/fetrends.htm).
Hickernell, F. J., & Yuan, Y. (1997) A simple multi-start algorithm for global optimization, OR Transactions 1(2), 1–12.
IIHS-Insurance Institute for Highway Safety. (1998). New study of relationships between vehicle weight and occupant death rates helps put in perspective issue of crash compatibility, Highway Loss Data Institute. (http://www.iihs.org/sr/pdfs/sr3301.pdf).
Jedidi K., Zhang Z. J. (2002) Augmenting conjoint analysis to estimate consumer reservation price. Management Science 48(10): 1350–1368
Jensen P., Bard J. (2003) Operations research models and methods. Wiley, London
Kirchain, R., & Field, F. R. (2001). Process-based cost modeling: Understanding the economics of technical decisions. Encyclopedia of Materials Science & Engineering 1718–1727.
Kohli R., Mahajan V. (1991) A reservation-price model for optimal pricing of multi-attribute products in conjoint analysis. Journal of Marketing Research 8: 347–354
Krishnan V., Eppinger S., Whitney D. (1995) Accelerating product development by the exchange of preliminary design information. ASME Journal of Mechanical Design 117: 491–498
Liu H., Gopalkrishnan V., Quynh K., Ng W-K. (2009) Regression models for estimating product life cycle cost. Journal of Intelligent Manufacturing 20(4): 401–408
Luce R. (1959) Individual choice behavior: A theoretical analysis. Wiley, New York
Markel T., Brooker A., Hendricks T., Johnson V., Kelly K., Kramer B., O’Keefe M., Sprik S., Wipke K. (2002) ADVISOR: A systems analysis tool for advanced vehicle modeling. Journal of Power Systems 10(2): 255–266
Marti R. (2003) Multi-start methods. In: Glover F., Kochenberger G. (eds) Handbook of metaheuristics. Kluwer, Dordrech, pp 355–368
Marti R., Moreno-Vega J.M., Duarte A. (2009) Advanced multi-start methods. In: Potvin J., Gendreau M. (eds) Handbook of metaheuristics. Springer, Heidelberg, pp 1–1
Massey G. R. (1999) Product evolution: A Darwinian or Lamarckian phenomenon?. Journal of Product & Brand Management 8(4): 301–318
Mayr E. (2001) What evolution is. Basic Books, New York
McConville, G. P., & Cook, H. E. (1996) Examining the value trade-off between automobile acceleration performance and fuel economy. SAE Paper 960004, Society of Automotive Engineers, Warrendale, PA.
Michalek J. J., Papalambros P. Y., Skerlos S. J. (2004) A study of emission policy effects on optimal vehicle design decisions. ASME Journal of Mechanical Design 126(6): 1062–1070
Miller G. A. (1956) The magical number seven plus or minus two: Some limits on our capacity for processing information. Psychological Review 63: 81–97
Mills V., Samuels B., Wagner J. (2002) Modeling and analysis of automotive antilock brake systems subject to vehicle payload shifting. Vehicle System Dynamics 37(4): 283–310
Monroe, E. M., Silver, R. L., & Cook, H. E. (1997). Value versus price segmentation of family automobiles. SAE Paper 97076. Society of Automotive Engineers, Warrendale, PA
Montgomery D. C. (2000) Design and analysis of experiments, 5 edn.. Wiley, New York
Nelson R. R., Winter S. (1982) An evolutionary theory of economic change. Belknap Press, Cambridge
Olewnik A., Lewis K. (2006) A decision support framework for flexible system design. Journal of Engineering Design 17(1): 75–97
OTA-Office of Technology Assessment. (1995). Advanced automotive technology: Visions of a super-efficient family car, Office of Technology Assessment, OTA-ETI-638: GPO stock #052-003-01440-8. (http://www.princeton.edu/~ota/disk1/1995/9514/951405.PDF).
Otto K. N., Wood K. L. (1998) Product evolution: Reverse engineering and redesign methodology. Research in Engineering Design 10: 226–243
Papalambros P. Y. (2002) The optimization paradigm in engineering design: Promises and challenges. Computer-Aided Design 34: 939–951
Pepall, L., Richards, D. J., & Norman, G. (2002) Industrial organization: Contemporary theory and practice (2nd ed.). South Western College Publishing, Cincinnati, OH.
Petroski H. (2003) Small things considered: Why there is no perfect design. Vintage Books, New York
Pontikakis G., Stamatelos A. (2001) Mathematical modeling of catalytic exhaust systems for EURO-3 and EURO-4 emissions standards. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automotive Engineering 215(9): 1005–1015
Shooter S., Keirouz W., Szykman S., Fenves S. (2000) A model for the flow of design information in product development. Engineering with Computers 16: 178–194
Silver M., de Weck O. (2007) Time-expanded decision networks: A framework for designing evolvable complex systems. Systems Engineering Journal 10(2): 167–186
Simon H. A. (1996) The sciences of the artificial (3rd ed). MIT Press, Cambridge
Suh E., de Weck O., Chang D. (2007) Flexible product platforms: Framework and case study. Research in Engineering Design 18: 67–89
Suh N. P. (1999) A theory of complexity, periodicity and the design axioms. Research in Engineering Design 11: 116–131
Tellis G. J., Crawford C. M. (1981) An evolutionary approach to product growth theory. Journal of Marketing 45(Fall): 125–132
Thomke S. H. (1998) Managing experimentation in the design of new products. Management Science 44(6): 743–762
Ullman, D. G. (1993). Evolution of function and behavior during mechanical design. American Society of Mechanical Engineers, Design Engineering Division (Publication) DE, vol. 53, Design Theory and Methodology (pp. 91–103).
Utterback J. M. (1996) Mastering the dynamics of innovation. Harvard Business School Press, Boston
Vyas, A., Santini, D., & Cuenca, R., (2000). Comparison of indirect cost multipliers for vehicle manufacturing. Center for Transportation Research, Argone, IL. (http://msl1.mit.edu/classes/esd123/vyas.pdf).
Ward’s Dealer Business. (2004). High fuel prices show effects in Showrooms (p. 9). September 1.
Wassenaar H., Chen W. (2003) An approach to decision based design with discrete choice analysis for demand modeling. ASME Journal of Mechanical Design 125(3): 490–497
Wheelwright S. C., Clark K. B. (1994) Accelerating the design-build-test cycle for effective product development. International Marketing Review 11(1): 32–46
Whitney D. E. (1990) Designing the design process. Research in Engineering Design 2: 3–13
Wilke W. L. (1994) Consumer behavior (3rd ed.). Wiley, New York
Wissmann, L., & Yassine, A. (2005). Design evolution using the artisan-patron model—the case of automotive evolution. In ASME Design Theory and Methodology Conference. Long Beach, CA.
Wright I. C. (1997) A review of research into engineering change management: Implications for product design. Design Studies 18(1): 33–42
Yassine A., Joglekar N., Braha D., Eppinger S., Whitney D. (2003) Information hiding in product development: The design churn effect. Research in Engineering Design 14: 145–161
Zacharias N., Yassine A. (2008) Optimal platform investment for product family design. Journal of Intelligent Manufacturing 19(2): 131–148
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yassine, A.A. Parametric design adaptation for competitive products. J Intell Manuf 23, 541–559 (2012). https://doi.org/10.1007/s10845-010-0392-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-010-0392-5