On the Influence of Material Selection Decisions on Second Order Cost Factors

  • Marco Leite
  • Arlindo SilvaEmail author
  • Elsa Henriques
Part of the Springer Series in Advanced Manufacturing book series (SSAM)


Life cycle cost of manufactured parts and the selection of the manufacturing equipment to produce the parts are of the utmost importance in today’s highly competitive automotive industry. In the context of materials substitution for high volume production, a problem is often encountered on how to accurately predict the cost of manufactured parts. First order cost factors, like the cost of the raw material itself, are normally easily available, but second order factors, like tools and dies cost, amount of scrap, rework and others, are quite difficult to predict to support the substitution decision. Nevertheless, they play a major role in defining the overall life cycle cost. It becomes even harder when the new proposed material is similar to the incumbent material. In these cases, a predictive cost model will have to be sensitive to changes in the material properties to be able to correctly estimate these second order costs. The problem is that material properties are seldom directly related to manufacturing cost parameters in sufficient detail. Very often, relying on empirical models calibrated with historical data represents the only available alternative. This chapter presents discussion around these issues and follows four industrial examples where a methodology is proposed for predicting cost in the presence of alternative materials with the same manufacturing process, using empirical engineering models together with process-based cost models. The relevant manufacturing cost factors are identified and discussed, and conclusions are drawn. A generalization of the methodology is also discussed, enabling further work in different industrial situations.


Magnesium Alloy Injection Molding High Strength Steel Life Cycle Cost Cost Driver 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors want to thank the MIT Portugal Program for funding of this research.


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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.ICEMS, Instituto Superior TécnicoUniversidade de LisboaLisboaPortugal
  2. 2.IDMEC, Instituto Superior TécnicoUniversidade de LisboaLisboaPortugal

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