Analytical Target Setting: An Enterprise Context in Optimal Product Design

[+] Author and Article Information
Adam B. Cooper

Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109abcooper@umich.edu

Panayotis Georgiopoulos

Program in Manufacturing, University of Michigan, Ann Arbor, MI 48109panayio@umich.edu

Harrison M. Kim

Department of General Engineering, University of Illinois, Urbana, IL 61801hmkim@uiuc.edu

Panos Y. Papalambros

Department of Mechanical Engineering, University of Michigan, MI 48109pyp@umich.edu

J. Mech. Des 128(1), 4-13 (Mar 31, 2005) (10 pages) doi:10.1115/1.2125972 History: Received August 18, 2003; Revised March 31, 2005

In this article the process of rigorously setting supersystem targets in an enterprise context is explored as a model-based approach termed “analytical target setting.” Engineering design decisions have more value and lasting impact if they are made in the context of the enterprise that produces the designed product. Setting targets that the designer must meet is often done at a high level within the enterprise, however, with inadequate consideration of the engineering design embodiment and associated cost. For complex artifacts produced by compartmentalized hierarchical enterprises, the challenge of linking the target setting rationale with the product instantiation is particularly demanding. The previously developed analytical target cascading process addresses the problem of translating top level design targets into design targets for all systems in a multilevel hierarchically structured product, so that local targets are consistent with each other and top targets can be met as closely as possible. The effectiveness of linking analytical target setting and target cascading is demonstrated in a hybrid electric automotive truck vehicle example. The manufacturer introduces a new product (hybrid electric truck) in the market under uncertainty in fuel prices during the life cycle of the vehicle. The example demonstrates a clear interaction between the enterprise decision making and the engineering product development.

Copyright © 2006 by American Society of Mechanical Engineers
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Figure 4

The demand curve at different price elasticities of fuel savings

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Figure 1

Coordination and information flow in analytical target setting (ATS) and cascading (ATC) processes

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Figure 2

Decision-making and technical information used at each level of the organization’s hierarchy

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Figure 3

Amount of dollars spent for fuel economy improvement

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Figure 5

A quadratic cost function links production cost with capacity utilization

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Figure 6

Schematic of the integrated vehicle system

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Figure 7

Model description, coordination, and information flow in analytical target setting and analytical target cascading

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Figure 8

Postoptimality analysis on consumer savings for the new technology



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