Optimal Design Decisions in Product Portfolio Valuation

Georgiopoulos, Panayotis; Fellini, Ryan; Sasena, Michael J.; Papalambros, Panos Y.

doi:10.1115/detc2002/dac-34097

Cited by 31 publications

(12 citation statements)

References 15 publications

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“…An important next step is to integrate the engine model into a full vehicle model to obtain a more comprehensive operating cycle. From a methodology viewpoint, the next step is to link the engineering decisions with financial analysis models (Georgiopoulos et al 2002) so that product families can be examined in the context of a combined business-engineering product development and assessment process.…”

Section: Discussionmentioning

confidence: 99%

Quantitative platform selection in optimal design of product families, with application to automotive engine design

Fellini

Kokkolaras

Papalambros

2006

Journal of Engineering Design

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Product variants with similar architecture but different functional requirements may have common parts. We define a product family to be a set of such products, and refer to the set of common parts as the product platform. Product platforms enable rapid adjustment to changing market needs while keeping development costs and time-cycles low. In many cases, however, the individual product requirements are conflicting when designing a product family. The designer must balance the tradeoff between maximizing commonality and minimizing individual product performance deviations. The design challenge is to select the product platform that will generate family designs with minimum deviation from individual optima. We propose a methodology that combines two previous approaches developed for making commonality decisions. In the first approach optimal values and sensitivity information from the individually optimized variants are used to indicate components that are probable candidates for sharing. In the second approach a relaxed combinatorial problem is formulated to maximize sharing among variants subject to bounds on performance reduction for the individually optimized values. In the combined methodology the first approach is used to identify an initial set of shared components and define the candidate platform to be considered by the second approach. The computational load is reduced significantly and the platform-selection problem is solved in a more robust manner. The proposed methodology is demonstrated on the design of an automotive engine family.

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Section: Discussionmentioning

confidence: 99%

Quantitative platform selection in optimal design of product families, with application to automotive engine design

Fellini

Kokkolaras

Papalambros

2006

Journal of Engineering Design

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“…However, it is not a surprising result based on common understanding that being profitable solely in the compact car segment is very difficult for manufacturers. When a product portfolio is expanded to include SUV's, the profit scenario can change due to high profit associated with SUV production and the effect of CAFÉ regulations [13].…”

Section: Resultsmentioning

confidence: 99%

“…ATC does not include the investment cost that the manufacturer is required to install an AFM manufacturing system in-house. We assume AFM process will be applied by a supplier, see [6], [7].…”

mentioning

confidence: 99%

Model Based Analysis of Performance-Cost Tradeoffs for Engine Manifold Surface Finishing

Li¹,

Georgiopoulos²,

Papalambros³

et al. 2004

SAE Technical Paper Series

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The link between manufacturing process and product performance is studied in order to construct analytical, quantifiable criteria for the introduction of new engine technologies and processes. Cost associated with a new process must be balanced against increases in engine performance and thus demand for the particular vehicle. In this work, the effect of the Abrasive Flow Machining (AFM) technique on surface roughness is characterized through measurements of specimens, and a predictive engine simulation is used to quantify performance gains due to the new surface finish. Subsequently, economic cost-benefit analysis is used to evaluate manufacturing decisions based on their impact on firm's profitability. A demonstration study examines the use of AFM for finishing the inner surfaces of intake manifolds for two engines, one installed in a compact car and the other in an SUV.

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“…The current model is static in the sense that market share is a deterministic function of the product characteristics and price, and demand does not vary over the time period in question. A number of potential extensions are possible such as modeling market dynamics by considering investment time (Georgiopoulos et al, 2002) and demand fluctuation (Asl and Ulsoy, 2002), or by including considerations of product life cycle economic modeling (Birge et al, 2000) and machine reconfiguration (Koren et al, 1999).…”

Section: Discussionmentioning

confidence: 99%

Manufacturing Investment and Allocation in Product Line Design Decision-Making

Michalek

Ceryan

Papalambros

et al. 2005

Volume 2: 31st Design Automation Conference, Parts a and B

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An important aspect of product development is design for manufacturability (DFM) analysis that aims to incorporate manufacturing requirements into early product decisionmaking. Existing methods in DFM seldom quantify explicitly the tradeoffs between revenues and costs generated by making design choices that may be desirable in the market but costly to manufacture. This paper builds upon previous work coordinating models for engineering design and marketing product line decision-making by incorporating quantitative models of manufacturing investment and production allocation. The result is a methodology that considers engineering design decisions quantitatively in the context of manufacturing and market consequences in order to resolve tradeoffs, not only among performance objectives, but also between market preferences and manufacturing cost.

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Optimal Design Decisions in Product Portfolio Valuation

Cited by 31 publications

References 15 publications

Quantitative platform selection in optimal design of product families, with application to automotive engine design

Quantitative platform selection in optimal design of product families, with application to automotive engine design

Model Based Analysis of Performance-Cost Tradeoffs for Engine Manifold Surface Finishing

Manufacturing Investment and Allocation in Product Line Design Decision-Making

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