Product family design and platform-based product development has received much attention over the last decade. This paper provides a comprehensive review of the state-of-the-art research in this field. A decision framework is introduced to reveal a holistic view of product family design and platform-based product development, encompassing both front-end and back-end issues. The review is organized according to various topics in relation to product families, including fundamental issues and definitions, product portfolio and product family positioning, platform-based product family design, manufacturing and production, as well as supply chain management. Major challenges and future research directions are also discussed.

Product family design and platform-based product development: a state-of-the-art review

In an effort to improve customization for today's highly competitive global marketplace, many companies are utilizing product families and platform-based product development to increase variety, shorten lead times, and reduce costs The key to a successful product family is the product platform from which it is derived either by adding, removing, or substituting one or more modules to the platform or by scaling the platform in one or more dimensions to target specific market niches This nascent field of engineering design has matured rapidly in the past decade, and this paper provides a comprehensive review of the flurry of research activity that has occurred during that time to facilitate product family design and platform-based product development for mass customization Techniques for identifying platform leveraging strategies within a product family are reviewed along with metrics for assessing the effectiveness of product platforms and product families Special emphasis is placed on optimization approaches and artificial intelligence techniques to assist in the process of product family design and platform-based product development Web-based systems for product platform customization are also discussed Examples from both industry and academia are presented throughout the paper to highlight the benefits of product families and product platforms The paper concludes with a discussion of potential areas of research to help bridge the gap between planning and managing families of products and designing and manufacturing them

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Product platform design and customization: Status and promise

Linking Marketing and Engineering Product Design Decisions via Analytical Target Cascading

Our research is motivated by the need for developing an approach to demand modeling that is critical for assessing the profit a product can bring under the decision-based design framework. Even though demand modeling techniques exist in market research, little work exists on demand modeling that addresses the specific needs of engineering design, in particular, that facilitates engineering decision making. In this work, we enhance the use of discrete choice analysis to demand modeling in the context of decision-based design. The consideration of a hierarchy of product attributes is introduced to map customer desires to engineering design attributes related to engineering analyses. To improve the predictive capability of demand models, the Kano method is employed to provide econometric justification when selecting the shape of the customer utility function. A (passenger) vehicle engine case study, developed in collaboration with the market research firm, J. D. Power & Associates, and the Ford Motor Company, is used to demonstrate the proposed approaches.

Enhancing Discrete Choice Demand Modeling for Decision-Based Design

Plug-in hybrid electric vehicle (PHEV) technology has the potential to help address economic, environmental, and national security concerns in the United States by reducing operating cost, greenhouse gas (GHG) emissions and petroleum consumption from the transportation sector. However, the net effects of PHEVs depend critically on vehicle design, battery technology, and charging frequency. To examine these implications, we develop an integrated optimization model utilizing vehicle physics simulation, battery degradation data, and U.S. driving data to determine optimal vehicle design and allocation of vehicles to drivers for minimum life cycle cost, GHG emissions, and petroleum consumption. We find that, while PHEVs with large battery capacity minimize petroleum consumption, a mix of PHEVs sized for 25–40 miles of electric travel produces the greatest reduction in lifecycle GHG emissions. At today’s average US energy prices, battery pack cost must fall below $460/kWh (below $300/kWh for a 10% discount rate) for PHEVs to be cost competitive with ordinary hybrid electric vehicles (HEVs). Carbon allowance prices have marginal impact on optimal design or allocation of PHEVs even at $100/tonne. We find that the maximum battery swing should be utilized to achieve minimum life cycle cost, GHGs, and petroleum consumption. Increased swing enables greater all-electric range (AER) to be achieved with smaller battery packs, improving cost competitiveness of PHEVs. Hence, existing policies that subsidize battery cost for PHEVs would likely be better tied to AER, rather than total battery capacity.Copyright © 2010 by ASME

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Optimal Plug-In Hybrid Electric Vehicle Design and Allocation for Minimum Life Cycle Cost, Petroleum Consumption, and Greenhouse Gas Emissions

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. DOI: 10.1115/1.2125972

Analytical Target Setting: An Enterprise Context in Optimal Product Design

Product portfolio valuation is a core business milestone in a firm’s product development process: Determine what will be the final value to the firm derived from allocating assets into an appropriate product mix. Optimal engineering design typically deals with determining the best product based on technological (and, occasionally, cost) requirements. Linking technological with business decisions allows the firm to follow a product valuation process that directly considers not only what assets to invest but also what are the appropriate physical properties of these assets. Thus, optimal designs are determined within a business context that maximizes the firm’s value. The article demonstrates how this integration can be accomplished analytically using a simple example in automotive product development.Copyright © 2002 by ASME

Optimal Design Decisions in Product Portfolio Valuation

Resource allocation is a core business milestone in a firm's product development process: Maximize the final value derived from allocating resources into an appropriate product mix. Optimal engineering design typically deals with determining the best product based on technological (and, occasionally, cost) requirements. Linking technological with business decisions allows the firm to follow a resource allocation process that directly considers not only the resources to invest in different products but also the appropriate physical properties of these products. Thus, optimal designs are determined within an enterprise context that maximizes the firm's value. The article demonstrates how this integration can be accomplished analytically using a simple example in automotive product development.

Linking Optimal Design Decisions to the Theory of the Firm: The Case of Resource Allocation

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.

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

We review basic concepts from economics and finance relevant to product development. Case studies are used to demonstrate how these concepts relate to complex design decisions within the producing enterprise, with a specific formulation for product portfolios. We conclude with a proposed synthesis of engineering design, economics and finance under a single decision-making model. Nomenclature C: cost

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Panayotis Georgiopoulos

Papers

Analytical Target Setting: An Enterprise Context in Optimal Product Design

Optimal Design Decisions in Product Portfolio Valuation

Linking Optimal Design Decisions to the Theory of the Firm: The Case of Resource Allocation

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

A Designer's View to Economics and Finance