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Research Papers: Design Theory and Methodology

Architecture, Performance, and Investment in Product Development Networks

[+] Author and Article Information
Ali A. Yassine

Department of Industrial
Engineering and Management,
American University of Beirut,
Beirut 1107-2020, Lebanon
e-mail: ali.yassine@aub.edu.lb

Joe Naoum-Sawaya

Ivey Business School,
Western University,
London N6G 0N1, Ontario, Canada

1Corresponding author.

Contributed by the Design Theory and Methodology Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received June 13, 2016; final manuscript received August 30, 2016; published online October 11, 2016. Assoc. Editor: Julie Linsey.

J. Mech. Des 139(1), 011101 (Oct 11, 2016) (11 pages) Paper No: MD-16-1443; doi: 10.1115/1.4034673 History: Received June 13, 2016; Revised August 30, 2016

Firms engaging in product development (PD) face the imperative problem of allocating scarce development resources to a multitude of opportunities. In this paper, we propose a mathematical formulation to optimize PD investment or resource allocation decisions. The model maximizes the performance of a product under development, based on its architecture and the firm's available resource, by choosing the optimal resource allocation across product modules and design rules that govern the relationships between these modules. Results based on a comprehensive experiment (with various architectural patterns, escalating number of dependencies, and different problem sizes) shed light on three important hypotheses. First, product architecture affects resource allocation decisions and ultimately product performance. The second hypothesis tests whether modular or integral architectures can attain higher performance levels based on our formulation. A third hypothesis states that there is a shift in the temporal allocation of resources from design rules to individual modules, thus supporting the move from integral to modular architectures as the product evolves across multiple generations. Finally, the model and the experimental results provide design and managerial insights to both development engineers and managers. Specifically, for development engineers, the model and its analysis provide guidance for selecting the product architecture which leads to maximum performance. For development managers, the model and its analysis assist in deciding the optimal budget proportions to be allocated to modules and to design rules, given a fixed architecture and budget.

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Figures

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Fig. 1

Investment decisions and its relationship to integral-modular dynamics DSM of a product system evolution of performance, and architecture fraction of development budget spent on modules and on design rules: (a) DSM of a product system, (b) evolution of performance and architecture, and (c) fraction of development budget spent on modules and on design rules

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Fig. 2

The eight different DSM architectures. All with the same number of total interactions: N = 12 and K = 2; NK = 24 total module interactions (Adapted from Rivkin and Siggelkow [23]): (a) random, (b) diagonal, (c) block diagonal, (d) local, (e) hierarchical, (f) dependent, (g) small world, and (h) scale free.

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Fig. 3

ANOM (99% confidence intervals) grouped N, K, and architecture: (a) performance, (b) total investment in modules, and (c) total investment in design rules

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Fig. 4

ANOM (99% confidence intervals) for N and K: (a) total performance (performance), (b) total investment in modules (Alpha_tot), (c) total investment in design rules (Theta_tot), and (d) total investment in compatibility (Comp_Inv)

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Fig. 5

ANOM (99% confidence intervals) for eight different DSM architectures: (a) mean performance, (b) total investment in modules, (c) total investment in design rules, and (d) total investment in compatibility

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Fig. 6

Performance evolution and proportion of budget spent on modules and design rules (using average values for the output of the 100 problems)

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Fig. 7

Software upgrade project DSM data (showing fij values (off-diagonal) and ci values (along diagonal))

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Fig. 8

ANOM (99% confidence intervals) for architecture and K, and architecture and N: (a) architecture and N interaction; (b) architecture and K interaction

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