Technology and manufacturing process selection : The product life cycle perspective

Springer Series in Advanced Manufacturing

Elsa Henriques

Paulo Peças

Arlindo Silva Editors

Technology and

Manufacturing

Process

Selection

The Product Life Cycle Perspective

Springer Series in Advanced Manufacturing

Series editor

Duc Truong Pham, Cardiff, UK

For further volumes:

http://www.springer.com/series/7113

Elsa Henriques • Paulo Peças

Arlindo Silva

Editors

Technology and

Manufacturing Process

Selection

The Product Life Cycle Perspective

123

Editors

Elsa Henriques

Paulo Peças

Arlindo Silva

IDMEC, Instituto Superior Técnico

Universidade de Lisboa

Lisbon

Portugal

ISSN 1860-5168 ISSN 2196-1735 (electronic)

ISBN 978-1-4471-5543-0 ISBN 978-1-4471-5544-7 (eBook)

DOI 10.1007/978-1-4471-5544-7

Springer London Heidelberg New York Dordrecht

Library of Congress Control Number: 2013953217

Springer-Verlag London 2014

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Editorial Board

Wim Dewulf, Katholieke Universiteit Leuven, Leuven, Belgium

Joost Duflou, Katholieke Universiteit Leuven, Leuven, Belgium

Paulo Ferrão, Universidade de Lisboa, Lisbon, Portugal

Michael Z. Hauschild, Technical University of Denmark, Lyngby, Denmark

Elsa Henriques, Universidade de Lisboa, Lisbon, Portugal

Paulo Martins, Universidade de Lisboa, Lisbon, Portugal

Paulo Peças, Universidade de Lisboa, Lisbon, Portugal

Roy Rajkumar, Cranfield University, Bedfordshire, UK

Inês Ribeiro, Universidade de Lisboa, Lisbon, Portugal

Rich Roth, Massachusetts Institute of Technology, Cambridge, USA

Arlindo Silva, Universidade de Lisboa, Lisbon, Portugal

v

Preface

In a global market, competitive advantage lies not only on the mastering of

existing processes and methodologies, but most of all on the ability to pursue

different avenues, with an increased value. This can only be achieved with an up￾to-date technological knowledge and scientific principles materialized in the

design and manufacturing of new products, with the goal of protecting the envi￾ronment and conserving resources, while encouraging economic progress, keeping

in mind the need for sustainability. Design and process engineering problems are

frequently of an ill-defined nature, demanding for the analysis and evaluation of

complex alternative solutions, in which environmental, economic, and functional

performance criteria interact in a complex net of influences, with an emergent

behavior. Moreover, even when decisions are made in a well-defined and narrow

timeframe, their effects are normally felt over a larger time sphere and scope

domain, shaping the future further than anticipated and in eventually unsought

ways.

Technology and manufacturing process selection is essential in the continuous

improvement of existing products and processes as a key factor to competitiveness

and sustainability. Technology-based innovation relies on the combination of

design and manufacturing areas, bringing together a multidisciplinary team with

different expertise and perspectives. The complexity of the decision-making pro￾cess under such a widespread knowledge framework implies the use of efficient

and reliable approaches. The analysis and synthesis mechanisms to support this

decision-making process must also be effective in the early design phases and

integrate all the aspects related with the life cycle stages of both product and

technologies.

To deploy a technology evaluation and selection process under a life cycle

scope, it is essential to capture all the evolutions and impacts of the selected

alternatives, frequently supported on vague information and uncertain data. In fact,

nowadays product developers need to address not only the production costs, but

also all the costs incurred throughout the entire product life cycle (Life Cycle Cost

-LCC). The estimation of all the costs associated with a product in a ‘‘cradle to

grave’’ perspective—or, even in a broader way, from ‘‘cradle to cradle’’—inte￾grates the analysis of the impact of design for cost, design for maintainability,

design for assembly, design for recycling, etc. With the aim of providing drivers

and indicators for a sustainable engineering practice, it is also important to design

vii

and evaluate the technological alternatives on a life cycle environmental basis,

namely involving Life Cycle Assessment (LCA) methods. Accordingly, the use of

methodologies like LCA to estimate the environmental performance supports the

disciplines of design for the environment, design for recycling, design for stan￾dards, etc.

The main reason for including a life cycle perspective in the early stages of

product and process development is that decisions taken at the front end of the

development largely influence the production of competitive products with high

quality standards in regards to functional performance, cost and environmental

impact for their entire life. Therefore, to better design for the entire life, Design￾for-X strategies, supported by the corresponding tools, have been increasingly and

successfully applied. These strategies drive the design team in the creation of

products, processes, and services that achieve a specific target or that maximize the

performance in a wide range of engineering fields (cost, environment, assembly,

etc.). The problem then becomes one of striking a balance between different

‘‘optimizations,’’ as optimizing for recycling will necessarily lead to a different

outcome than optimizing for manufacturing and assembly, which further enhances

the need to better understand the way in which these dispersed approaches/tools

need to be used in a coherent and comprehensive way.

The consideration of all life cycle stages of a product in the early design phase

allows a more complete perception of the product’s value in the market and in

society. This way of designing and developing a product can be called Design for

the Life Cycle. To differentiate it from the regular DfX strategies, several authors

prefer to denominate it as Life Cycle Engineering, understood as a decision￾making methodology that considers functional performance, environmental, and

cost dimensions throughout the duration of a product or, in a narrower sense,

throughout the time horizon affected by an engineering decision, guiding design

engineers toward informed decisions.

The research in Life Cycle Engineering challenges the academic world because

it endorses a multidisciplinary approach on a problem solving framework. In fact

the development of Life Cycle Engineering tools and its implementation in

product design and development requires the collaboration of different areas of

expertise during several phases of such a project. Therefore, the incorporation of

concurrent engineering practices is recommended, if not mandatory.

In conclusion, the development of decision-making methodologies based on

Life Cycle approaches is extremely important to support informed and reliable

assessment and selection of technological solutions. Based only on singular types

of performance or integrating several types of performance, these methodologies

are under development by several research groups worldwide.

This book provides specific topics intending to contribute to an improved

knowledge on Technology Evaluation and Selection in a Life Cycle Perspective.

Although each chapter will present possible approaches and solutions, there are no

recipes for success. Each reader will find his/her balance in applying the different

topics to his/her own specific situation. Case studies presented throughout will help

in deciding what fits best to each situation, but most of all any ultimate success

viii Preface

will come out of the interplay between the available solutions and the specific

problem or opportunity the reader is faced with. Contributions were accepted from

47 authors in seven countries from around the world: China, France, Germany,

Italy, Portugal, Sweden, and the United States of America.

Editing a book embodies team work and represents considerable work from the

authors, editors, and editorial advisory board. This collaborative teamwork

involves keeping track of contacts of authors and their contributions, exchanging

information and ideas, managing the review process, feeding back review to the

authors, managing conflicting perspectives, and integrating contents into a rea￾sonable structure, with the ultimate goal of developing a product that adds value to

the readers’ body of knowledge.

As team leaders we, the editors, have to thank our team members for the effort

involved in this initiative. This book is primarily supported by the team of pro￾fessionals from Springer. We thank them for the opportunity and constant support

in editing the book, timely suggestions, prompt feedback, and friendly reminders

about deadlines. To the Members of the Editorial Board, our gratitude for sharing

with us their knowledge and experience in the support of the decision-making

processes inherent to the project, for assisting in the review process, and for their

help in shaping the book. We acknowledge all the authors, without whom there

would be no book in the first place! Many contributions were not considered,

despite their merit, either because they were out of the scope for this book, of time

limitations, or other constraints. A special word to our home institution, the In￾stituto Superior Técnico of the Technical University of Lisbon, for providing the

infrastructure, material resources, and logistics required for our work.

We hope the book will enlighten the reader in the same way it enlightened us

during the editing process, and that its contents will help foster new and innovative

research worldwide.

Elsa Henriques

Paulo Peças

Arlindo Silva

Preface ix

Contents

Product Architecture Decision Under Lifecycle Uncertainty

Consideration: A Case Study in Providing Real-time Support

to Automotive Battery System Architecture Design............... 1

Qi D. Van Eikema Hommes and Matthew J. Renzi

Consideration of Legacy Structures Enabling a Double Helix

Development of Production Systems and Products . . . . . . . . . . . . . . . 21

Magnus Wiktorsson

Six Sigma Life Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Pedro A. Marques, Pedro M. Saraiva, José G. Requeijo

and Francisco Frazão Guerreiro

On the Influence of Material Selection Decisions on Second

Order Cost Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Marco Leite, Arlindo Silva and Elsa Henriques

Aircraft Industrialization Process: A Systematic and Holistic

Approach to Ensuring Integrated Management

of the Engineering Process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

José Manuel Lourenço da Saúde and José Miguel Silva

Material Flow Cost Accounting: A Tool for Designing

Economically and Ecologically Sustainable Production Processes . . . . 105

Ronny Sygulla, Uwe Götze and Annett Bierer

Life Cycle Based Evaluation and Interpretation of Technology

Chains in Manufacturing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

F. Klocke, B. Döbbeler, M. Binder, R. Schlosser and D. Lung

Selecting Manufacturing Process Chains in the Early Stage

of the Product Engineering Process with Focus

on Energy Consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

Martin Swat, Horst Brünnet and Dirk Bähre

xi

Manufacturing with Minimal Energy Consumption:

A Product Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175

Alexandra Pehlken, Alexandra Kirchner and Klaus-Dieter Thoben

Integrated Framework for Life Cycle-Oriented Evaluation

of Product and Process Technologies: Conceptual Design

and Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193

Uwe Götze, Andrea Hertel, Anja Schmidt, Erik Päßler

and Jörg Kaufmann

Life Cycle Engineering Framework for Technology

and Manufacturing Processes Evaluation . . . . . . . . . . . . . . . . . . . . . . 217

Inês Ribeiro, Paulo Peças and Elsa Henriques

Proposal for an Architectural Solution for Economic

and Environmental Global Eco-Cost Assessment:

Model Combination Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239

Nicolas Perry, Alain Bernard, Magali Bosch-Mauchand,

Julien Le Duigou and Yang Xu

The Ecodesign of Complex Electromechanical Systems:

Prioritizing and Balancing Performance Fields,

Contributors and Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257

S. Esteves, M. Oliveira, F. Almeida, A. Reis and J. Pereira

Composite Fiber Recovery: Integration into a Design

for Recycling Approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281

Nicolas Perry, Stéphane Pompidou, Olivier Mantaux and Arnaud Gillet

Design for Disassembly Approach to Analyze and Manage

End-of-Life Options for Industrial Products

in the Early Design Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297

Claudio Favi and Michele Germani

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323

xii Contents

Product Architecture Decision Under

Lifecycle Uncertainty Consideration:

A Case Study in Providing Real-time

Support to Automotive Battery System

Architecture Design

Qi D. Van Eikema Hommes and Matthew J. Renzi

Abstract Flexibility is valuable when the future market and customer needs are

uncertain, especially if the product development process is long. This chapter

focuses on what the firm can do to increase their flexibility before a product is

produced and sold. The flexibility is built into the product architecture, which then

enables the firm to take a staged decision process. Flexibility-in-the-Project

approach was developed by de Neufville and Sholtes (2011), and has been suc￾cessfully applied to large infrastructure projects. Real options analysis has only

been utilized in high-level product planning decisions. The case study described in

this chapter is the first successful application of the Flexibility-in-the-Project

framework, providing real-time engineering design decision support to Ford Motor

Company engineering efforts in future vehicle electrification. In hybrid and

electric vehicle applications, the high voltage battery pack hardware and control

system architecture will experience multiple engineering development cycles in

the next 20 years. Flexibility in design could mitigate risk due to uncertainty in

both engineering and consumer preferences. Core engineering team decisions on

battery pack voltage monitoring, thermal control, and support software systems

will iterate as technology evolves. The research team valued key items within the

technology subsystems and developed flexible strategies to allow Ford to capture

upside potential while protecting against downside risk, with little-to-no extra cost

at this early stage of development. The methodology used to evaluate the uncer￾tainty, identify flexibility, and provide the real options value of flexibility is

presented.

Q. D. V. E. Hommes (&) M. J. Renzi

Engineering Systems Division, Massachusetts Institute of Technology, 77 Massachusetts

Ave, Cambridge, Massachusetts, USA

e-mail: [email protected]

M. J. Renzi

e-mail: [email protected]

E. Henriques et al. (eds.), Technology and Manufacturing Process Selection,

Springer Series in Advanced Manufacturing, DOI: 10.1007/978-1-4471-5544-7_1,

Springer-Verlag London 2014

1

1 Introduction

Much data have shown that the most important decisions about a product are made

in the early phase of the design process, when the design is still fluid, and changes

are relatively inexpensive (Fig. 1). However, making decisions in this phase of the

design can be very challenging, because the prediction about the future markets

and operations demand has high uncertainty, especially when the product devel￾opment cycle is long.

The historical gasoline price data is a good example to illustrate the challenges in

forecasting (Fig. 2). The United States Energy Information Administration (EIA)

provides a concise explanation of the factors influencing the gasoline prices (EIA

2012), many of which are attributed to global social, political, and economical

dynamics that are impossible to accurately predict. Therefore, the large fluctuation

of gasoline prices often surprises and frustrates industries and individuals, and

sends the equity market on a roller coaster ride.

The inability to accurately forecast gasoline price has a strong impact on the US

automotive sales in various segments such as small car, SUV, etc., as demonstrated

during the 2009 financial crisis period. Typically, new automobile models take

3–5 years to design, engineer, and manufacture. Forecast based on the 2003

gasoline price made the truck and SUV segment seem highly profitable. The sales

volume assumptions were based on consumer purchase decisions at the low gas￾oline prices. After developing these new models of the SUVs and trucks for several

years and bringing them to market, many automotive companies found themselves

stuck with a large inventory of SUVs and trucks as consumers quickly switched to

buying small cars, reacting to the soaring gasoline price in 2008. The automotive

companies weren’t able to quickly change to making small cars. Years of engi￾neering efforts seemed to have been set in the wrong direction.

The main reason for which the automotive companies weren’t able to quickly

react to market changes is that their entire cost structure were optimized to making

SUV and trucks, based on the point forecast made in earlier years. Thirty years

before the 2009 financial crisis and the struggle of the American automotive

Fig. 1 Committed lifecycle

cost against time

2 Q. D. V. E. Hommes and M. J. Renzi

companies, Abernathy (1978) argued that automobiles running on gasoline internal

combustion engines had arrived at a dominant architecture. The focus of the

manufacturers turned to process innovation—optimizing the productivity of the

production process for a few mature architectures. Abernathy gave an example on

why it could not be profitable for an automotive company to manufacture small

vehicles in manufacturing plants optimized for making large vehicles. He pointed

out that in order to stay competitive, firms should be careful not to let productivity

kill the flexibility to innovate. Unfortunately, history repeated itself 30 years later,

due to precisely the same cause that Abernathy had identified—lack of flexibility

to react to the market when the market isn’t what is forecasted years ago.

Remaining flexible is important because forecasting is inherently uncertain, as

no one has been able to predict the future accurately. Many assumptions enter

forecasting models so that mathematical calculations can be performed (Stock and

Watson 2007, Train 2003). The data collection methods for market and consumer

information used to feed the forecasting models are also not perfect (Aaker et al.

2010). Questionnaire design can strongly affect the responses, depending on how

questions are worded, and how they are interpreted (Brace 2004; and Harkness

et al. 2003). Consumers’ actual purchase decision may be very different from what

they say in a market clinic or when they answer a survey (Kahneman and Tversky

1979; Tversky and Kahneman 1981; Kahneman et al. 1990; Gladwell 2005).

Although the forecasted values are often uncertain, the customary practice is to

use the average forecasted value in planning (Ulrich and Eppinger 2008 (Chap. 15)).

Many of the optimization and trade-off studies are done based on average forecasted

values. Yet, average values are highly flawed (Savage 2009). The Iridium fleet of

communication satellites was a good example on decisions made based on average

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

5.00

Jan-02 Jan-04 Jan-06 Jan-08 Jan-10 Jan-12

Dollars per gallon, including all taxes

Fig. 2 United States motor gasoline price data (Source U.S. Department of Energy, Energy

Information Administration, Weekly Retail Gasoline Prices, available at http://eia.doe.gov/as of

April 2012)

Product Architecture Decision 3

forecasted demand, which was so far off the reality that the company went into

bankruptcy (de Weck et al. 2004). In their 2011 book, de Neufville and Scholtes

provide many additional real examples to illustrate this point. To make things worse,

many large capital-intensive products, such as automobiles, take years to develop

and manufacture. Even if the forecasted average value was close to reality at the

time, things change overtime, and the future can be very uncertain (Fig. 3).

Literature does advise conducting what-if scenario sensitivity analysis, after

assessing the most-likely case using average numbers (Ulrich and Eppinger 2008

(Chap. 15)). This additional step is much better than basing the decision only on an

average forecast. However, as de Neufville and Scholtes (2011) point out, this

approach is a ‘‘bunker mentality:’’ Will we be able to survive adverse futures? Will

we be able to sustain risks? It is an afterthought of having optimized the design

following the averaged forecast. It does not design with the uncertainties in mind

so that uncertainties can be leveraged to our advantage.

The discussions in this chapter is about gaining the ability for a product design

to remain flexible for long term future uncertainty, and even taking advantage of

the uncertainty when possible. As Fig. 1 has illustrated, the best place to incor￾porate such thinking is in the early phase of the product design process. Specifi￾cally, this chapter focuses on how to assess the value of flexibility embedded in

product architecture, during the concept design phase of the product development

process (Ulrich and Eppinger 2008). The concept of architecture used in this

chapter follows the definition in Ulrich and Eppinger (2008):

The architecture of a product is the scheme by which the functional elements of the

product are arranged into physical chunks and by which the chunks interact.

The methodology presented in this chapter is a support framework for product

architecture selection in real time. This framework focuses on three questions:

Why do we need flexibility, when will we need it, and how much will it cost? The

framework contains four steps (de Neufville and Scholtes 2011): (1) establish the

key uncertainties, (2) determine points of flexibility, (3) provide a financial model

incorporating the key uncertainties, and (4) establish the value of flexibility. The

framework was proven successful when applied as real-time support for the Ford

Motor Company’s decision process on core technology for the thermal control of

an electrified vehicle battery system.

Market

Analysis Engineering Manufacturing Sales Operations,

Service

Time

A?

B?

C?

t1 t2 t3 t4

External Factors

Fig. 3 Lifecycle view of the early product decisions

4 Q. D. V. E. Hommes and M. J. Renzi

2 Literature Review

2.1 Uncertainty Consideration in Product Design

and Manufacturing

There exists rich literature in addressing uncertainties in product design and

manufacturing. This section organizes the literature around two questions:

1. What are the uncertainties being considered?

2. What are the strategies developed to address these uncertainties?

2.1.1 Types of Uncertainties

The first type of uncertainty is the recognition that customer requirements are

usually not set at fixed points. Products are usually designed for a market segment,

in which the customer requirement is not uniform, but rather a distribution. For an

individual user, the utility for a certain performance metric varies within a range of

acceptable values. Requirements can be balanced to maximize the utility of the

overall product. Work in the area of Multidisciplinary Design Optimization

(MDO) represents concern of this type of uncertainty (Donndelinger et al. 2003;

Papalambros and Wilde 2000; Ferguson and Siddiqui 2007; Chen and Yuan 1999;

Ross et al. 2008).

The second type of uncertainty is that the product usage may change after the

product is deployed (Ferguson and Siddiqui 2007; Olewnik et. al. 2004; Olewnik

and Lewis 2006; Saleh et al. 2003; Skiles et al. 2006; Haulbelt et al. 2002; Frick

and Shulz 2005; Ross et al. 2008; Shah et al. 2008; Matin and Ishii 2002; Lieke

et al. 2008). Customers may face new usage situations. The operating environment

may be unpredictable. The product may degrade over time.

The third type of uncertainty involve customer and market needs change over

time (Saleh et al. 2003; Keese et al. 2006 and 2007; Clarkson et al. 2004; Eckert

et al. 2004; Fricke and Shulz 1999; 2005; Ross et al. 2008; Shah et al. 2008; Martin

and Ishii 2002; Allada and Jiang 2001 and 2002; Sethi and Sethi 1990; Gustavsson

1984; Gerwin 1982, and Kapoor and Kazmer 1997). Customers may want new

functionalities or higher quality. Government regulatory requirements may

change. Industry standards can change. Technology competition may change the

requirements. Societal and economical trends may also change what consumers

want. The market demand (quantity) may change over time (Pandey and Thurston

2008).

Additional uncertainties mentioned in many literature include the introduction

of new technology (Keese 2006; Fricke and Shulz 1999; 2005; Ross et. al 2008;

Shah et. al 2008; Martin and Ishii 2002; Sethi and Sethi 1990; Gustavsson 1984;

Gerwin 1982, and Kapoor and Kazmer 1997), manufacturing piece to piece

Product Architecture Decision 5