Leveraging Technology for a Sustainable World

David A. Dornfeld • Barbara S. LinkeEditors

Leveraging Technologyfor a Sustainable WorldProceedings of the 19th CIRP Conference onLife Cycle Engineering, University of California at Berkeley,Berkeley, USA, May 23–25, 2012

ABC

EditorsProf. David A. DornfeldLaboratory for Manufacturing and Sustainability (LMAS)University of California at BerkeleyBerkeley, CAUSA

Dr. Barbara S. LinkeLaboratory for Manufacturing and Sustainability (LMAS)University of California at BerkeleyBerkeley, CAUSA

ISBN 978-3-642-29068-8 e-ISBN 978-3-642-29069-5DOI 10.1007/978-3-642-29069-5Springer Heidelberg New York Dordrecht London

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Preface

Welcome to the 19th CIRP Conference on Life Cycle Engineering hosted by the University of California, Berkeley! The Berkeley campus isboth the University of California’s flagship campus as well as a renowned research center that continues a legacy of innovation in engineering,science, society, culture, and politics. We hope that this environment will lead to a productive discussion within our Life Cycle Engineering (LCE)community.

The 19th CIRP LCE conference continues a strong tradition of scientific meetings in the areas of sustainability and engineering. Thetheme for this year’s conference is Leveraging Technology for a Sustainable World. As resources have become increasingly scarce and theenvironmental impact of business and industry has grown, it has become vital for engineers to provide leadership in developing those innovationsthat will enable green businesses and industries that remain socially responsible and economically successful. It is our goal that this conferencewill serve as an international forum for researchers to review and discuss the current developments, technology improvements, and futureresearch directions that will allow engineers to meet this societal need.

The conference includes over 100 technical papers that have been accepted after a rigorous peer review and revision process. The researchcovers Businesses and Organizations, Case Studies, End of Life Management, Life Cycle Design, Machine Tool Technologies for Sustainability,Manufacturing Processes, Manufacturing Systems, Methods and Tools for Sustainability, Social Sustainability, Supply Chain Management.Keynote talks will be given by Dr. Julian Allwood of the University of Cambridge, Dr. Michael Overcash of Wichita State University, Mr. RichardHelling of Dow Chemical, Ms. Karen Huber of Caterpillar, and Mr. Adam Hansel of DTL/Mori Seiki. We hope that these presentations and theproceedings will serve as a valuable source of information on the state of LCE.

We would like to thank all of the participants for their contributions to the conference program and proceedings, as well as the organizingteam at the Laboratory for Manufacturing and Sustainability for their support. We would also like to extend our gratitude to the members of theScientific Committees for their continued support in helping to make this a successful conference!

The conference program would not be possible without the generous financial support of our industry sponsors who, at the time of thiswriting, include: Samsung, Mori Seiki/DMG, Esprit by DP Technologies, and Dow Chemical. In addition our thanks go to the National ScienceFoundation NSF, who provided financial support for graduate students and postdoctoral researchers attending the conference.

Thank you again for your support of the 19th CIRP LCE conference and we look forward to a great meeting!

David A. DornfeldBarbara S. Linke

Organization

Chairs

Prof. David A. Dornfeld

Dr.-Ing. Barbara S. Linke

Organizing Committee

Mr. Antoine Chaignon Dr. Hyunseoup LeeMs. Yi-Fen Chen Ms. Jennifer MangoldMr. Joshua Chien Ms. Katherine McKinstryMr. Seungchoun Choi Ms. Joanna NobleMr. Lee Clemon Ms. Emily RiceMs. Nancy Diaz Ms. Stefanie RobinsonMr. Moneer Helu Ms. Kathy Schermerhorn-CousensMs. Yuchu Huang Mr. Anton SudradjatDr. Margot Hutchins Dr. Karl WalczakMr. Daeyoung Kong

International Scientific CommitteeProf. S. Ahn / KR Prof. T. Lien / NOProf. L. Alting / DK Prof. C. Luttropp / SEProf. F. Badurdeen / US Prof. H. Meier / DEProf. A. Bernard / FR Prof. N. Nasr / USProf. H. Bley / DE Prof. A. Nee / SGProf. B. Bras / US Prof. R. Neugebauer / DEProf. D. Brissaud / FR Prof. J.F.G. Oliveira / BRProf. G. Byrne / IE Prof. A. Ometto / BRProf. W. Dewulf / BE Prof. M. Overcash / USProf. F. Doyle / US Prof. G. Reinhart / DEProf. J. Duflou / BE Prof. O. Romero-Hernandez / MXProf. H. ElMaraghy / CA Prof. G. Seliger / DEProf. W. ElMaraghy / EG Prof. M. Shpitalni / ILProf. K. Feldmann / DE Prof. W. Sihn / ATProf. J. Greene / US Prof. S. Skerlos / USProf. T. Gutowski / US Prof. R. Steinhilper / DEProf. K. Haapala / US Prof. J. Sutherland / USProf. M. Hauschild / DK Prof. S. Takata / JPProf. C. Herrmann / DE Prof. S. Tichkiewitch / FRProf. J. Hesselbach / DE Prof. T. Tomiyama / NLProf. J. Issacs / US Prof. J. Twomey / USProf. I.S. Jawahir / US Prof. Y. Umeda / USProf. J. Jeswiet / CA Prof. H. van Brussel / BEProf. H. Kaebernick / AU Prof. F. van Houten / NLProf. S. Kara / AU Dr. A. Vijayaraghavan / USProf. F. Kimura / JP Prof. E. Westkamper / DEProf. T. Kingsbury / US Prof. C. Yuan / USProf. F. Klocke / DE Prof. H. Zhang / USProf. G. Lanza / DE

Table of Contents

Keynotes

Unit Process Life Cycle Inventory (UPLCI) – A Structured Framework to Complete Product Life Cycle Studies . . . . . . . . . . . . . . . . . . . . . . . 1Michael Overcash, Janet Twomey

Businesses and Organizations

Quality Information Management for Advanced Quality Planning in Small and Medium-Sized Companies . . . . . . . . . . . . . . . . . . . . . . . . . . . 5Sophie Groger, Toni Eiselt

Making the Business Case for Eco-Design and Sustainable Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Alireza Veshagh, Salvador Marval, Tim Woolman

Goal Programming-Based Approach to Identify Sustainable Product Service System Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19Aihua Huang, Ken Wijekoon, Fazleena Badurdeen

Supporting Scenario Design in Planning Long-Term Business Strategies Based on Sustainability Scenarios . . . . . . . . . . . . . . . . . . . . . . . . 25Yusuke Kishita, Maki Hirosaki, Yuji Mizuno, Haruna Wada, Shinichi Fukushige, Yasushi Umeda

Exploiting Life Cycle Innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31J.E. Parada Puig, S. Hoekstra, L.A.M. van Dongen

Businesses and Organizations − Product Service Systems

Risk Management of Industrial Product-Service Systems (IPS2) – How to Consider Risk and Uncertainty over the IPS2 Lifecycle? . . . . . . 37Johannes Keine gen. Schulte, Marion Steven

Product-Service System Types and Implementation Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43Britta Pergande, Paula L.A. Nobre, Anderson C. Nakanishi, Eduardo S. Zancul, Leandro Loss, Lucas C. Horta

Life Cycle Oriented Quality Assessment of Technical Product-Service Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49Sebastian Waltemode, Carsten Mannweiler, Jan C. Aurich

Case Studies for Automotive Applications

A Framework to Analyze the Reduction Potential of Life Cycle Carbon Dioxide Emissions of Passenger Cars . . . . . . . . . . . . . . . . . . . . . . . . 55Christoph Herrmann, Karsten Kieckhafer, Mark Mennenga, Steven Skerlos, Thomas Stefan Spengler, Julian Stehr, Vineet Raichur,Grit Walther

Material Substitution for Automotive Applications: A Comparative Life Cycle Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61Laura Marretta, Rosa Di Lorenzo, Fabrizio Micari, Jorge Arinez, David Dornfeld

Decision Making Methodology to Reduce Energy in Automobile Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67Katherine C. McKinstry, Jorge Arinez, Stephan Biller, David Dornfeld

Water Consumption in a Vehicle Life Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73Francisco Tejada, John Zullo, Tina Guldberg Tejada, Bert Bras

Simplified Life Cycle Analysis of a Forming Tool in the Automotive Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79F. Klocke, G. Schuh, B. Dobbeler, M. Pitsch, R. Schlosser, D. Lung, D. Assmann, C. Hein, R. Malek

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Case Studies for Selected Applications

LCA Application: The Case of the Sugar Cane Bagasse Electricity Generation in Brazil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85Diogo A. Lopes Silva, Ivete Delai, Mariana Maia de Miranda, Mary Laura Delgado Montes, Aldo Roberto Ometto

Energy Efficiency Potential for China’s Cement Industry: A Bottom-Up Technology-Level Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91Ali Hasanbeigi, William Morrow, David Fridley, Eric Masanet, Tengfang Xu, Jayant Sathaye, Nina Zheng, Lynn Price

Drivers and Barriers of Environmentally Conscious Manufacturing: A Comparative Study of Indian and German Organizations . . . . . . . . . 97Varinder Kumar Mittal, Kuldip Singh Sangwan, Christoph Herrmann, Patricia Egede, Christian Wulbusch

Stimulating Students to Experience Sustainable Engineering: The CQS Group T Racing Team . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103Nicolas Dekeyser, Sietze Swolfs, Geert Waeyenbergh, Wim Dewulf

Reducing Energy Demands of Printing Machines by Energy Reuse Options and Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107Reimund Neugebauer, Matthias Putz, Carsten Keller, Sascha Falsch

Environmental Footprint of Magnetite Nanoparticle Application: Iron Oxide Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113Suphunnika Ibbotson, Sami Kara

Modular Server – Client – Server (MSCS) Approach for Process Optimization in Early R&D of Emerging Technologies by LCA . . . . . . . . . 119Eva Zschieschang, Peter Pfeifer, Liselotte Schebek

Life Cycle Perspectives for Biosensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125Ali Hakimian, Pegah N. Abadian, Jacqueline A. Isaacs

Life Cycle for Engineering the Healthcare Service Delivery of Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131Janet Twomey, Michael Overcash, Seyed Soltani

Defining and Mapping LCA Networks: Initial Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137Anders Bjørn, Mikołaj Owsianiak, Alexis Laurent, Christine Molin, Torbjørn B. Westh, Michael Z. Hauschild

End of Life Management

Obsolescence Management as a Tool for Effective Spare Parts Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143Sven Schulze, Christian Engel, Henning Leichnitz

Assessment of Automation Potentials for the Disassembly of Automotive Lithium Ion Battery Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149Christoph Herrmann, Annika Raatz, Mark Mennenga, Jan Schmitt, Stefan Andrew

Remanufacturing: An Alternative for End of Use of Wind Turbines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155Katherine Ortegon, Loring F. Nies, John W. Sutherland

A Study on an E-Waste Management Strategy Considering Global Inverse Supply Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161Kuniko Mishima, Nozomu Mishima, Masaru Nakano

Separating Manufacturing Metal Chips for Recycling through a Combined Hydrodynamic and Electromagnetic Approach . . . . . . . . . . . . . . 167Qiang Zhai, Chris Yuan

A Framework for Using Cognitive Robotics in Disassembly Automation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173Supachai Vongbunyong, Sami Kara, Maurice Pagnucco

Recyclability Evaluation of LCD TVs Based on End-of-Life Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179Takahiro Mizuno, Eisuke Kunii, Shinichi Fukushige, Yasushi Umeda

Strategic Decision Making for End-of-Life Management of Fuel Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185E.I. Wright, S. Rahimifard

Table of Contents XI

Life Cycle Design

Impact of Technology on Product Life Cycle Design: Functional and Environmental Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191Seung Jin Kim, Sami Kara

A Conceptual Framework for Sustainable Engineering Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197Kai Lindow, Robert Woll, Rainer Stark

Prioritizing ‘Design for Recyclability’ Guidelines, Bridging the Gap between Recyclers and Product Developers . . . . . . . . . . . . . . . . . . . . . . 203Harm A.R. Peters, Marten E. Toxopeus, Juan M. Jauregui-Becker, Mark-Olof Dirksen

Holistic Approach for Jointly Designing Dematerialized Machine Tools and Production Systems Enabling Flexibility-Oriented BusinessModels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209

Giacomo Copani, Marco Leonesio, Lorenzo Molinari-Tosatti, Stefania Pellegrinelli, Marcello Urgo, Anna Valente, Juanjo Zulaika

LeanDfd: A Design for Disassembly Approach to Evaluate the Feasibility of Different End-of-Life Scenarios for Industrial Products . . . . . . 215Claudio Favi, Michele Germani, Marco Mandolini, Marco Marconi

Mapping the Life Cycle Analysis and Sustainability Impact of Design for Environment Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221Lora Oehlberg, Cindy Bayley, Cole Hartman, Alice Agogino

Machine Tool Technologies for Sustainability

Development of an Empirical Model to Describe the Service Life of Ball Screws for Machine Tools under Consideration of JerkInfluences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227

Christian Brecher, Turker Yagmur

Simulation of the Energy Consumption of Machine Tools for a Specific Production Task . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233Eberhard Abele, Christian Eisele, Sebastian Schrems

Energy Efficient Cooling Systems for Machine Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239Christian Brecher, Stephan Baumler, David Jasper, Johannes Triebs

Simulation and Prediction of Process-Oriented Energy Consumption of Machine Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245S. Braun, U. Heisel

Mapping Energy Consumption for Industrial Robots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251Pal Ystgaard, Tone Beate Gjerstad, Terje Kristoffer Lien, Per Aage Nyen

Prototypical Implementation of a Remanufacturing Oriented Grinding Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257Eraldo Jannone da Silva, Aldo Roberto Ometto, Henrique Rozenfeld, Diogo A. Lopes Silva, Daniela Cristina Antelmi Pigosso,Vinicius Ricco Alves Reis

Manufacturing Processes − Casting, Forming, Injection Molding, Joining

Assessment of Energy Consumption in Injection Moulding Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263Ines Ribeiro, Paulo Pecas, Elsa Henriques

Unit Process Energy Consumption Models for Material Addition Processes: A Case of the Injection Molding Process . . . . . . . . . . . . . . . . . 269Farhan Qureshi, Wen Li, Sami Kara, Christoph Herrmann

Dynamic Total Cost of Ownership (TCO) Calculation of Injection Moulding Machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275Sebastian Thiede, Tim Spiering, Stephan Kohlitz, Christoph Herrmann, Sami Kara

A Material and Energy Flow Oriented Method for Enhancing Energy and Resource Efficiency in Aluminium Foundries . . . . . . . . . . . . . . . . 281Michael Krause, Sebastian Thiede, Christoph Herrmann, Franz Friedrich Butz

Multi-objective Analysis on Joining Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287Joshua M. Chien, Katherine C. McKinstry, Chul Baek, Arpad Horvath, David Dornfeld

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Manufacturing Processes − Machining

Life Cycle Analysis of Grinding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293Barbara Linke, Michael Overcash

Total Cost Analysis of Process Time Reduction as a Green Machining Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299Moneer Helu, Benjamin Behmann, Harald Meier, David Dornfeld, Gisela Lanza, Volker Schulze

Energy Analysis of Micro-drilling Process Used to Manufacture Printed Circuit Boards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305Hae-Sung Yoon, Binayak Bhandari, Jong-Seol Moon, Chung-Soo Kim, Gyu-Bong Lee, Kwang Wook Park, Chul-Ki Song,Sung-Hoon Ahn

Using Jatropha Oil Based Metalworking Fluids in Machining Processes: A Functional and Ecological Life Cycle Evaluation . . . . . . . . . . . . 311Marius Winter, Gerlind Ohlschlager, Tina Dettmer, Suphunnika Ibbotson, Sami Kara, Christoph Herrmann

Evaluation of Two Competing Machining Processes Based on Sustainability Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317Juliano Bezerra de Araujo, Joao Fernando Gomes de Oliveira

Using a New Economic Model with LCA-Based Carbon Emission Inputs for Process Parameter Selection in Machining . . . . . . . . . . . . . . . 323Kadra Branker, Jack Jeswiet

Evaluation of Abrasive Processes and Machines with Respect to Energy Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329Jan C. Aurich, Marina Carrella, Manfred Steffes

Manufacturing Systems − Factory Planning and Peripheral Systems

An Approach for the Planning and Optimization of Energy Consumption in Factories Considering the Peripheral Systems . . . . . . . . . . . . . 335Holger Haag, Jorg Siegert, Thomas Bauernhansl, Engelbert Westkamper

Development of a Procedure for Energy Efficiency Evaluation and Improvement of Production Machinery . . . . . . . . . . . . . . . . . . . . . . . . . . . 341Rolf Steinhilper, Stefan Freiberger, Frank Kubler, Johannes Bohner

Compressed Air Leak Detection Using Microphone Array Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347Petr Eret, Craig Meskell

Automated Handling of Limp Foils in Lithium-Ion-Cell Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353J. Fleischer, E. Ruprecht, M. Baumeister, S. Haag

A Life Cycle Oriented Evaluation of Changeable Manufacturing and Assembly Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357Jochen Hartung, Barbara Baudzus, Jochen Deuse

Energy Efficiency in Pneumatic Production Systems: State of the Art and Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363Paul Harris, Garret E. O’Donnell, Tom Whelan

Manufacturing Systems − Process Planning and Control

Interface Requirements and Planning Framework for the Integration of Non-conventional Processes in Production Lines . . . . . . . . . . . . . . 369Jochen Bock, Jorg Siegert, Thomas Bauernhansl, Engelbert Westkamper

Simulation-Based Assessment of the Energy Consumption of Manufacturing Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375Eberhard Abele, Sebastian Schrems, Christian Eisele, Philipp Schraml

Automated Provision and Exchange of Energy Information throughout the Production Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381Philipp Eberspacher, Holger Haag, Raphael Rahauser, Jan Schlechtendahl, Alexander Verl, Thomas Bauernhansl,Engelbert Westkamper

Automated Linkage of Consumption Models and Control Information in Control Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387Jan Schlechtendahl, Philipp Sommer, Philipp Eberspacher, Alexander Verl

Table of Contents XIII

Sustainable Interlinked Manufacturing Processes through Real-Time Quality Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393Daniel Lieber, Benedikt Konrad, Jochen Deuse, Marco Stolpe, Katharina Morik

Energy-Sensitive Production Control in Mixed Model Manufacturing Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399Reimund Neugebauer, Matthias Putz, Andreas Schlegel, Tino Langer, Enrico Franz, Soren Lorenz

An Eco-Improvement Methodology for Reduction of Product Embodied Energy in Discrete Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . 405H. Ozgur Unver, M. Ural Uluer, Aysegul Altin, Yigit Tascioglu, S. Engin Kilic

Cost and Energy Consumption Optimization of Product Manufacture in a Flexible Manufacturing System . . . . . . . . . . . . . . . . . . . . . . . . . . . 411Nancy Diaz, David Dornfeld

Concept of Energy-Efficient Job Sequencing for Heat Treatment Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417Daniel Bucker, Jochen Deuse

Methods and Tools for Sustainability

Resource Efficiency Assessment System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423Thomas Lundholm, Michael Lieder, Guido Rumpel

Developing a Simple Carbon Footprint Calculation Tool with the Criteria of Inventory Data Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429Jahau Lewis Chen, Yi-Hsing Wen

A Metrics-Based Methodology for Establishing Product Sustainability Index (ProdSI) for Manufactured Products . . . . . . . . . . . . . . . . . . . . . 435X. Zhang, T. Lu, M. Shuaib, G. Rotella, A. Huang, S.C. Feng, K. Rouch, F. Badurdeen, I.S. Jawahir

Modeling Gaps and Overlaps of Sustainability Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443Anna E. D’Alessio, Paul Witherell, Sudarsan Rachuri

A Forecasting Model for the Evaluation of Future Resource Availability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449Mark Mennenga, Sebastian Thiede, Jan Beier, Tina Dettmer, Sami Kara, Christoph Herrmann

Reduction of Water Consumption within Manufacturing Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455Madhu Sachidananda, Shahin Rahimifard

Water Footprint Quantification of Machining Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 461Jonathan Ogaldez, Ashley Barker, Fu Zhao, John William Sutherland

Methods and Tools for Sustainability in Aerospace Manufacturing

Methodology for Ecological and Economical Aircraft Life Cycle Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 467Markus Große Bockmann, Robert Schmitt

Life Cycle Engineering in Preliminary Aircraft Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 473Katharina Franz, Ralf Hornschemeyer, Arthur Ewert, Martina Fromhold-Eisebith, Markus Große Bockmann, Robert Schmitt,Katja Petzoldt, Christoph Schneider, Jan Erik Heller, Jorg Feldhusen, Kerstin Buker, Johannes Reichmuth

Lifecycle Impact Profile for Efficient Product Development – Jet Engine Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 479Patrick Ansgar Hacker, Gunther Schuh, Frank von Czerniewicz

Survey of Common Practices in Sustainable Aerospace Manufacturing for the Purpose of Driving Future Research . . . . . . . . . . . . . . . . . . . 485Yuriy Romaniw, Bert Bras

Methods and Tools for Sustainability in Process Chains

Energy Efficient and Intelligent Production Scheduling – Evaluation of a new Production Planning and Scheduling Software . . . . . . . . . . . 491Agnes Pechmann, Ilka Scholer, Rene Hackmann

XIV Table of Contents

Availability Focused Risk Analysis to Support Life Cycle Costing Prognosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497Oliver Mannuß, Alexander Schloske, Thomas Bauernhansl

A Hierarchical Evaluation Scheme for Industrial Process Chains: Aluminum Die Casting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503Tim Heinemann, Wataru Machida, Sebastian Thiede, Christoph Herrmann, Sami Kara

Characterization and Weighting Scheme to Assess the Resource Efficiency of Manufacturing Process Chains . . . . . . . . . . . . . . . . . . . . . . . 509Saskia Reinhardt, Maria Fischl, Gunther Reinhart

Methods and Tools for Sustainability in Processes

Comprehensive Model to Evaluate the Impact of Tooling Design Decisions on the Life Cycle Cost and Environmental Performance . . . . . . 515Ines Ribeiro, Paulo Pecas, Elsa Henriques

Data Collection of Chemicals used in Microelectronic Manufacturing Processes for Environmental Studies . . . . . . . . . . . . . . . . . . . . . . . . . . 521Ingwild Baudry, Alan Lelah, Daniel Brissaud

Measuring and Visualizing Different Energy Sources: A Concept for an Energy Management System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527Agnes Pechmann, Rene Hackmann, Ilka Scholer

Analysis of Electrical Power Data Streams in Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 533Stylianos Chiotellis, Martin Grismajer

Energy Monitoring in Manufacturing Companies – Generating Energy Awareness through Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 539G. Bogdanski, T. Spiering, W. Li, C. Herrmann, S. Kara

On the Implementation of Exergy Efficiency Metrics in Discrete Manufacturing System: The Dissipative Nature of ProductionProcesses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 545

Renaldi, Karel Kellens, Wim Dewulf, Joost R. Duflou

Integrating Sustainability Assessment into Manufacturing Decision Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 551Hao Zhang, Karl R. Haapala

Environmental Impact Modeling of Discrete Part Manufacturing Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 557Karel Kellens, Renaldi, Wim Dewulf, Joost R. Duflou

Social Sustainability

Role of Transformation Principles in Enabling Environmentally Significant Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 563Jay JungIk Son, L.H. Shu

Designing Products to Encourage Conservation: Applying the Discretization Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 569Jayesh Srivastava, L.H. Shu

A Framework for the Evaluation and Redesign of Human Work Based on Societal Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 575Wei-Tau Lee, Karl R. Haapala, Mark E. Edwards, Kenneth H. Funk

Working with the Social Hotspots Database - Methodology and Findings from 7 Social Scoping Assessments . . . . . . . . . . . . . . . . . . . . . . . 581Catherine Benoıt Norris, Deana Aulisio, Gregory A. Norris

Supply Chain Management

A Generic Simulation Model for Green Supplier Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 587Kanda Boosothonsatit, Sami Kara, Suphunnika Ibbotson

Impact of Parameter Changes in a Service Provider Closed-Loop Supply Chain with Customer-Owned-Stock . . . . . . . . . . . . . . . . . . . . . . . 593Kirsten Tracht, Michael Mederer, Daniel Schneider

Coping with Supply Chain Dynamics – Review and Synthesis of Existing Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 599Volker Stich, Jerome Quick, Niklas Hering

Table of Contents XV

Supply Chain Life Cycle Implications of Shipping Goods from Mexico – A Case on PV Solar Panels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 605Sergio Romero-Hernandez, Omar Romero-Hernandez, Celeste Lindsay, Tony Kingsbury, David Dornfeld

Transport Optimization by Logistics Oriented Production Scheduling in the Automotive Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 611Daniel Palm, Markus Florian, Wilfried Sihn

A Framework to Integrate Product Design and Supply Chain Decisions to Minimize CO2 Emission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 617Jack C.P. Su, Chih-Hsing Chu, Yu-Te Wang

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 623

Transport optimization by logistics oriented production scheduling in the Automotive Industry

Daniel Palm1, 2

, Markus Florian1, 2

, Wilfried Sihn1, 2

1 Institute of Management Science, Vienna University of Technology, Vienna, Austria

2 Division Production and Logistics Management, Fraunhofer Austria Research GmbH, Vienna, Austria

Abstract

The integrated planning of production sequence and logistics transports for vehicle manufacturers is the focus of the

research project InterTrans. The aim of the project is to develop sustainable and cost efficient concepts in multi-site

production networks with multi-modal supply chain structures. The basic idea is to integrate logistics constraints in

production scheduling of a vehicle manufacturer in order to improve inbound and outbound transportation processes in

terms of CO2 emissions and transportation costs. The project results show, that the introduction of logistics constraints

in production scheduling is possible and that logistics savings can be significant.

Keywords:

Transportation; Automotive; Production Scheduling

19th CIRP International Conference on Life Cycle Engineering, Berkeley, 2012

1 INTRODUCTION

Production scheduling in the Automotive Industry for sequenced

assembly lines is the matching of the required capacities out of the

production program with the existing fundamental factors of

production. Workforce, equipment and material are the three basic

planning sectors (see Figure 1). They can be defined as planning

constraints in production scheduling determining the output of the

production system. Current planning algorithms try to create a valid

production sequence under consideration of all given production

constraints.

dispositive

factors

fundamental

factors

consumablespotential

factors

derivative

factors

planning equipmentmaterial workforce

productstransformation

processes

production

factors

Figure 1: Production factors [1]

Such restrictions narrow the solution space by prohibiting certain

events or sequences of events. By introducing additional logistics-

oriented constraints to the production scheduling, an optimization of

transport parameter like costs or CO2-emissions can be achieved

[2].

The project InterTrans, supported by the German Ministry of

Economics and Technology, the Austrian Research Promotion

Agency (FFG) and the European research initiative EUREKA, had

the aim to research the possibilities in the European Automotive

Industry to increase capacity utilization of transport means, to

reduce kilometers traveled to supply Automotive production with

parts (inbound transportation), to deliver finished cars to the

customer (outbound transportation) and to determine the possibility

to shift transports from truck to more eco-friendly transportation

modes like ship or railway.

2 PRODUCTION CONSTRAINTS FOR SEQUENCED

ASSEMBLY LINES

The Automotive Industry produces according to the principle of a

high-variant line production [3]. The production sequence on the

assembly line is a result of a constraint planning. There are two

types of constraints: Inherent constraints which are balancing

equations or conditions and are valid for the complete production

system and second task related constraints which represent

technological, organizational and economic characteristics of the

production system [4]. Task related planning constraints are

relevant for the planning of the production sequence on assembly

lines. Hence the originators of planning constraints are generally

classified within five groups: Equipment, Workforce, Material,

Product and Market. These five groups build the branches of the

Ishikawa-diagram in Figure 2. [5]

The upper three branches represent the fundamental factors that

describe the production system. They are essential to achieve the

required output:

Equipment

Workforce

Material

The output of the production system is characterized by the branch

Product. It defines which brands, models and types are available for

the customer and how those products can be configured. The last

branch of the diagram – the Market branch – represents all

customers and their requirements as well as the outbound logistics

Figure 2: Originators of planning constraints [3]

which became more important as minimization of transport is in

focus [6]. All described constraints can be defined as absolute or

relative Constraints [4]:

Absolute constraints are quantity or time constraints. A quantity

constraint has a variable quantity and fixed time period (e.g.:

The weekly capacity is 1200 parts.). On the other hand time

constraints have fixed quantities and a variable time period

(e.g.: A product carrier has a capacity of ten components and it

is just shipped when the carrier is full).

A relative constraint is always characterized by a combination

of at least two events. Such constraints are for example

sequence or distance constraints. A sequence constraint for

example prohibits that a white car body is followed by a black

body in the paint shop. The distance constraint can define that

there are three cycles required until the same option is allowed

to be assembled again.

Relative constraints are mainly relevant in the short-term planning

process (sequencing). In long- and mid-term planning the relative

constraints have to be translated to quantity or time constraints.

It is possible that the limit of a constraint is flexible. Such constraints

are also called soft constraints. Other restrictions that are often

caused by technological limitations cannot be exceeded and must

be seen as hard constraints and cannot be violated. An example for

a soft restriction is a weekly delivery lot size of a supplier that can

be exceeded under special conditions.

3 TRANSPORT PLANNING IN AUTOMOTIVE INDUSTRY

The task of the transport planning can be divided in the design of

the network and the planning and control of the executed transport

processes. The task can be segmented in: [7]

Design of transport network (long-term)

Planning of transport routes and means of transports (middle-

term)

Planning of vehicle usages (middle and short-term)

The design and the long-term planning define sources, nodes and

drains of the automotive supply chain (suppliers, crossdocks,

component factories, automotive plants, transshipment points,

dealers etc.) and the transportation network of roads, rails and

waterways. These networks are used to forward freight between

origins and destinations of the production network. Shipments may

be transshipped from road to road, from rail to rail, from water to

road or vice versa using adequate facilities. [8]

The short term transport planning takes the result of the production

scheduling and assigns the derived demand to the available

transport capacities. This transport planning process is highly

reactive and leads to inefficiencies like higher logistics costs or

unnecessary CO2 emissions. [9]

4 LOGISTICS RESTRICTIONS TO IMPROVE EFFICIENCY IN

TRANSPORT LOGISTICS

In order to improve this transport planning process it is necessary to

define strategies for a higher efficiency of transport logistics. It can

be distinguished between ecological and economical efficiency.

Efficiency describes the extent to which effort is used for the

intended task or purpose – economically in terms of total costs and

ecologically in terms of external effects caused by the transport (ex.

CO2 emissions, pollution, noise). The aim to increase efficiency can

be reached by:

Shift of carrier or transportation mean (more ecological or more

economical)

Traffic reduction or avoidance

Optimization of capacity utilization of the carrier

Ecological and economical aims can herby be conflicting (shift of

carrier may be ecologically good but economically bad) or common

(an increase in capacity utilization and traffic reduction or avoidance

is ecologically and economically favorable; total costs and

unwanted external effects are reduced). [10]

In order to reach the goals to increase efficiency, several measures

in production sequencing can be taken to positively influence these

three aims.

4.1 Shift of carrier or transportation mean

From an ecological point of view, rail or ship transports can be

generally considered as more efficient than truck transports. The

characteristic trait of these two transport modes is their higher

capacity. For example the capacity in outbound logistics of a train is

aprox. 200 cars whereas a truck capacity is 8 cars. To support the

shift of carrier to rail or water, a planning strategy to bundle the car

production from or for a specific destination according to their time

schedule would be favorable.

4.2 Traffic reduction or avoidance

The bundling of cars for the same outbound destination or the

bundling of parts for inbound logistics is also an appropriate

strategy for the traffic reduction or avoidance. Inbound bundling

means to concentrate the demand of certain parts into a shorter

time span. By bundling parts, which are supplied over the same

transport relation, high transport utilization and low inventory costs

can be achieved. A traffic reduction can be realized by a

consolidated transport instead of direct deliveries.

4.3 Optimization of capacity utilization of the carrier

The principle of bundling under consideration of the capacity of the

carrier can optimize the capacity utilization of the carrier and as a

result can reduce or avoid traffic. This should be combined with the

smoothing of demand over several planning periods in order to

optimize the capacity utilization and the planning process. A weekly

train to supply the demand of a specific market for example can

only be considered as ecologically efficient if the fluctuation of

capacity demand does not exceed frequently the maximum

capacity. The resulting disturbances and the need of additional

transportation of the overrun may reduce or overcompensate the

benefits.

Smoothing means realizing a steady demand of parts. The resulting

constant stock movement and flow of parts provide a basis for a

steady transport schedule [11].

4.4 Logistics restrictions to improve efficiency

To summarize, the following planning restrictions in the production

scheduling would provide ecological and economical efficiency in

transportation: [12]

Capacity oriented bundling of parts from suppliers or regions

(inbound; ex. parts for a specific option can be bundled)

Capacity oriented bundling of cars for destinations or markets

(outbound; cars for a specific region with the same ship carrier

can be bundled)

Smoothing over several periods

Timing according to a schedule (timetable)

Occurring conflicts in ecological or economical aims in the case of a

shift of carrier or transportation mean must be solved by a

prioritization decision.

5 PROJECT PROCEEDING

Sequencing in the Automotive Industry is the planning process

where assembly orders for a specific time period (ex. day or shift)

are brought to an optimal sequence assuring a consideration of

different lead times of orders as a result of different variants. The

assembly order should not break any restrictions and should

maximize the capacity utilization of every station. Exact optimization

approaches are not applicable in practice due to long calculation

times. Therefore several heuristics are used to find a practicable

assembly sequence (see [13], [14], [15], [16]). Most of the Western

European car manufacturers use the car sequencing algorithm (car

sequencing problem, CSP). It does not or will not work with detailed

car configuration information, but ban overloads of partial

sequences with so-called Ho:No rules. After the appliance of the

algorithm on the entirety of the orders, from No sequenced variants,

the maximum amount of Ho options can be included to assure no

overloads. One example for a rule of 1:3 for the option sunroof says

that from three sequenced orders only one order can contain a

sunroof.

One aim of the project InterTrans was to prove, that it is possible, to

integrate logistics restrictions in the sequencing algorithm in

addition to existing production oriented restrictions in order to

improve the usage of transport means (short term). This will be

shown with two examples. To optimize transports, a temporally

adjustment of demand (bundling, smoothing) is necessary. To be

able to define Ho:No-Rules for inbound transports it is necessary to

do a bill explosion of the considered orders and to derive a part

demand for each supplier. For smoothing, the tact time A for a

specific part of a supplier must be related to the tact time B of the

assembly line and the Ho:No-rule 1:(A/B) must be applied.

In the distribution logistics the time of departure of the transport

mean is an important parameter to optimize. By introducing a latest

completion time for orders, the optimization criteria: a) “minimize the

amount of tacts between completion time and latest completion

time” and b) “minimize the amount of orders with a completion time

after the latest completion time” lead to a bulking of orders around

the latest completion time. This can be considered as favorable for

rail or ship transports and therefore for more ecological

transportation.

6 PROJECT RESULTS

In the project InterTrans, the introduction of the logistics restrictions

in the production program planning and sequencing process was

examined and tested under realistic conditions with data sets from a

European OEM. In order to include these logistics requirements in

production planning, integrated information from production and

logistics is necessary (see Figure 3). New processes for a dynamic

transport planning and a logistics-integrated sequencing were

developed and combined to an overall planning process. A software

tool to dynamically plan the transportation was realized as a

prototype.

The dynamic planning of transportation processes and capacities

leads to an increase of efficiency and flexibility also in mid-term

transportation planning aspects (see Figure 4) by opening up the

possibility for example to dynamically assign a transport mean with

appropriate shipping volume or to switch the transportation concept

from a direct transport if the shipping volume decreases to a milk-

run concept.

Figure 3: Solution approach of InterTrans: Integrated sequencing

Figure 4: dynamic transport planning

6.1 Impact on Outbound Logistics

In the distribution logistics, finished cars are assigned directly to the

transport device. The integrated planning of the sequence and the

transport has the following overall aims (see Figure 5):

Maximization of capacity utilization

Preference for Transports with better ecological efficiency

Minimization of stocks (to avoid additional handling after the

end of the production process)

Keeping the deadline of the customer order

In case studies a double-digit percentage rate for the relocation of

truck transports towards train transports outbound could have been

proven. Also the stock level of finished vehicles before distribution

was reduced by over 20%.

6.2 Impact on Inbound Logistics

The smoothing of the demand to eliminate fluctuations and to

improve the quality of the transportation planning is the main driver

on the supply side. The optimization of capacity utilization of the

carrier can be reached by a timetable oriented planning due to

consistent demand (see Figure 6).

The sole adaptation of the production sequence without adjusting

the timetables and the consolidation of transports had no impact in

the case studies of InterTrans. But with adjustments, a reduction of

truck usage up to 9 % together with a higher capacity utilization rate

could inbound have been achieved.

Figure 5: outbound transport planning

Figure 6: inbound transport planning

7 SUMMARY

The project InterTrans proved that the introduction of logistics-

oriented planning restrictions in the production planning and

scheduling process has a very positive impact on the efficiency of

the transportation logistics. The integrated planning of logistics and

production in the Automotive Industry is not only feasible in practice

but showed significant improvements in the logistics efficiency

without affecting the production adversely. The dynamic

transportation planning, the logistics-oriented planning constraints

and the holistic planning process developed in InterTrans lead to a

couple of improvement compared to the state of the art in the

Automotive Industry. Improvements are:

Utilization of dynamic bundling capabilities (reduction of

transports)

Improved capacity utilization of the transport carriers (up to 9%)

Relocation of transports towards ecological transportation

means (reduction of truck kilometer up to 48%)

Reduced distribution times

Reduction of CO2-emissions

Reduced stock level of finished vehicles before distribution (up

to 23%) and less handling costs

Improved communication between all partners in the

Automotive supply chain

Improved planning quality and reliability

8 ACKNOWLEDGMENTS

The authors gratefully acknowledge the support of the German

Ministry of Economics and Technology, the Austrian Research

Promotion Agency (FFG), the European research initiative

EUREKA and the Project management agency TÜV Rheinland. The

project consortium consisted of the partners 4flow AG (project

coordinator), Fraunhofer IML, Schenker AG, Vienna University of

Technology and Volkswagen AG.

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