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  • System Cost Modeling:

    Estimating Manufacturing Cost in Large Complex Systems+

    Francisco Veloso2, 3,* and Richard Roth1 1MIT Materials System Laboratory

    2Carnegie Mellon University 3Universidade Catolica Portuguesa

    * Corresponding Author fveloso@cmu.edu

    Carnegie Mellon University Department of Engineering and Public Policy

    Pittsburgh, PA 15213 Tel: +1 412 268 4640; Fax: +1 412 268 3757

    November 2004

    + We would like to thank Joel Clark, Frank Field, Randolph Kirchain and Sebastian Fixson for comments and suggestions on earlier versions of this paper. All errors remain our own.

    mailto:fveloso@cmu.edu

  • System Cost Modeling:

    Estimating Manufacturing Cost in Large Complex Systems Abstract

    This paper proposes a methodology, Systems Cost Modeling (SCM), to evaluate the cost of complex systems with a large number of individual components and subsystems. Simple characteristics associated with each component are used as inputs to parametric estimates of fixed and variable costs, as well as process use time of relevant resources. These estimates are then used to calculate individual component costs, which are then aggregated to the relevant system level. SCM focuses, not on obtaining accurate evaluations of component manufacturing cost, but rather on providing reliable calculations of the overall system cost and enable a robust analysis of the influence of key external parameters such as volume or factor input costs on total cost behavior. To show the use and validity of the SCM, an application of the methodology to the calculation of the supply chain costs for a set of stamped parts is presented. Keywords

    System Cost Modeling, Manufacturing, Complex Systems, Product Development

    1. Introduction

    The cost dimension is of fundamental importance in the context of product development or

    process planning stages. Therefore, a number of approaches and methods have been developed

    over time to enable an estimation of cost during the design stages, when limited information

    regarding component and process are available (see Sims 1995 for a full review of issues

    associated to manufacturing cost estimation). As a result, it is now possible to find

    comprehensive cost modeling methodologies that enable a good understanding of the economic

    implications of particular decisions related to processes and critical design parameters (e.g.

    material or production volume) in one or a limited number of parts. But the large majority of

    todays products are the result of complex combinations of a rather large number of parts that

    require an increasingly diverse set of manufacturing operations, as well as a substantial assembly

    effort. The seat of an automobile, for example, may require 40 different individual parts and

    more than 10 different manufacturing processes. This complexity is generating a new level of

    difficulty in terms of cost estimation for the complete system, requiring new approaches in terms

    of system cost modeling.

    - 1 -

  • Most existing cost models, because they have been developed to provide good accuracy in

    estimating the cost of individual parts, require a large number of detailed information about the

    component and the processing conditions. An average individual process based cost model may

    require the introduction of 25 descriptive variables (Kirchain 1999, chapter 2), which would lead

    to having more than 10,000 variables to be accounted for in the seat example described above.

    This makes the estimation process extremely difficult and time consuming. Moreover, there are a

    number of situations where extreme accuracy in the component cost estimation is not the desired

    feature. For example, if the objective is to understand how car volume will affect total seat cost,

    some degree of error in individual component cost may be tolerated, as long as the total seat cost

    is within a certain error margin, with the key interest being an accurate display of the relative

    variation in cost due to volume changes. To respond this new set of challenges in cost

    estimation, there is a growing effort towards expanding nature of cost models, so that they can be

    used to estimate costs for larger and ever more complex products (Han 1994; Kang 1998;

    Kirchain 1999).

    This paper aims to contribute to this literature. It proposes a method for estimating cost of

    complex systems, where a large number of components are present. It uses a bottom-up approach

    by incorporating basic part and processing information to build a manufacturing cost

    approximation. The method, called System Cost Modeling, is a semi-parametric estimation

    procedure, that is built on a set of observed regularities in the relation between component

    characteristics and processing equipment costs, as well as notions of activity based costing. The

    paper will discuss existing costing methods, describe the SCM approach and then test the

    methodology against existing detailed cost estimations procedures.

    2. Estimating Manufacturing Cost

    2.1. Top down approaches to estimation of component cost

    The earliest and still one of the most widely used techniques for evaluating the cost of

    manufacturing processes are simple rules of thumb. Designers or engineers with experience with

    the relevant technologies and processes usually develop rules that relate key variables of the

    process or material to the final cost (Busch and Field III 1988). For example, markup over

    - 2 -

  • materials cost is common among such rules. Another common manufacturing cost estimation

    procedure is to use accounting principles. A particularly popular application of these principles

    is activity based costing (ABC), which attributes direct and overhead costs to products and

    services on the basis of the underlying activities that generate the costs gathered from firm

    accounting information (Cooper and Kaplan 1988). Accounting ABC is based on historical and

    descriptive information. Therefore, when successfully applied, it enables a good understanding

    of existing critical cost drivers and it allows more accurate decisions of pricing and marketing.

    Nevertheless, because of its retrospective and contextual nature, ABC it is of limited help to

    engineers and designers concerned with improving manufacturing lines or choosing between

    alternative materials in during design.

    The lack of accuracy and the reliance on contextual data to estimate manufacturing cost has

    prompted the development of more accurate methods, and especially ones that enable an

    estimation of cost at the design stage. Cost estimation during design, especially in early stages, is

    a difficult task because there is limited information about the characteristics of the product. It is

    also of crucial importance because design decisions condition most of the product cost.

    According to OGrady et al. (1991), Ford estimates that design decisions determine 70% of the

    production cost.

    Two different approaches have been followed in the development of manufacturing cost models.

    The first can be characterized as a top-down approach. This method aims at establishing a

    relationship between cost and critical product and/or process parameters. These range from high-

    level functional attributes to detailed manufacturing characteristics. Bode (2000) provides a good

    example of high-level functional estimation. He tests the relationship between the cost of a

    personal computer and the size of the memory and hard disk, the type of CPU, the speed,

    presence of a CD-Rom, screen type as well as production volume. By contrast, Mileham et al.

    (1993) relates cost of an injection-molded part to product weight, production volume, cycle time

    and machine size. Regardless of the desired level of abstraction, all these methods entail

    gathering information about cost and relevant parameters for a large number of items in the same

    category or group and then using a statistical method to establish the desired relationship

    between both sets of information.

    - 3 -

  • Parametric regression based cost estimation methods are the most common (Cochran 1976;

    Datar, Kekre et al. 1993; Mileham, Currie et al. 1993; Shtub and Versano 1999). The cost

    engineer will a priori determine a cost function with relevant parameters based on his experience

    and then derives it through statistical regression methods using past case data. The major

    difficulty of these methods is that the cost engineer must have a sufficiently accurate cost

    function a priori to be able to make the estimation. When such functional relationship is not

    obvious, these methods may not yield adequate results. As a consequence of these shortcomings,

    research in top-down estimation methods has evolved towards experimenting with non-

    parametric approximations (Asiedu, Besant et al. 2000), neural network calculations (Shtub and

    Versano 1999; Bode 2000; Wang, Stockton et al. 2000) and pattern recognition approaches

    (Rehman and Guenov 1998; Ten Brinke, Lutters et al. 2000). Neural networks fit curves through

    data without being provided a predetermined function, estimating both the shape and parameter

    values of the cost function in one step. Therefore, they are able to detect hidden functional

    relationships between product attributes and cost, including relationships unknown to