Models for estimating design effort and time

doi:10.1016/S0142-694X(00)00014-4

Design Studies

Volume 22, Issue 2, March 2001, Pages 141-155

https://doi.org/10.1016/S0142-694X(00)00014-4 Get rights and content

Abstract

In today's competitive environment, it is necessary to deliver products on time and within budget. Unfortunately, design projects have been plagued by severe cost and schedule overruns. This problem persists in spite of the significant advances that have been made in design technology over the last two decades. In most of the cases, the problem of overruns was due to poor estimations. The search for a solution has become even more pressing in the present era of shrinking product cycle times. Driven primarily by this need, this paper proposes parametric estimation models. Unlike existing estimation techniques which are based on process or product physical decomposition, the proposed models are based on product functional decomposition. The models were applied to project data collected from two Canadian companies. The results indicate that the proposed models have good accuracy for estimating design effort.

Section snippets

Related work

Developing estimation models for design projects is not an easy task. This is because of the abstract nature of the task, and the lack of easily identifiable items to measure. Only a small amount of research has dealt with the estimation of design effort, and very few models have been suggested or tested. Recently, Jacome and Lapinskii[7]proposed a model for estimating effort for electronics design which takes into account three major factors: size, complexity, and productivity. The first

Methodology

Parametric estimation models use historical data from previous completed projects to establish mathematical relationships capable of generating effort estimates for future projects. In this study, historical data from previously completed projects from two Canadian companies were collected. The companies develop new products for markets where company 1 designs and manufactures battery chargers, and company 2 designs and manufactures communication systems. To ensure consistency of data, one

Parametric estimation models

Depending on the number of predictors used, two types of parametric models were constructed and investigated: a single variable model and a multivariable model. A single variable model uses one basic variable, namely, product complexity (PC), as a predictor of effort, while a multivariable model uses two variables, product complexity and severity of requirements. Product complexity was estimated by the metric defined by Eq. (1), while severity of requirements was rated on a scale of 1 to 3,

Evaluating the performance of the models

To test whether the above models produce reasonably accurate estimates, the following objective criteria were used:

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the mean magnitude of relative error (MMRE),
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prediction at a given level PRED(l), and
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the coefficient of multiple determination (R²).

Project duration estimation

Norden[24]noted that there are regular patterns of manpower increase and decrease independent of the type of work done that is related to the way people solve problems. On the basis of his analysis of many projects, he succeeded in creating a useful model (Eq. (13)) that describes the utilization of manpower during a design project phase. The use of manpower in each of the phases: planning, design, model, and release, is described by the model. Depending on the amount of overlap between the

Conclusions

Using traditional regression analysis, two types of parametric models were developed: a single variable model based on product complexity, and a multivariable model based on product complexity and severity of requirements. Generally, the models performed well according to a number of accuracy tests. For the effort estimation models, product complexity explained more than 80% of variation in estimating effort. This confirms that product complexity as an indicator for project size is the dominant

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View full text

Models for estimating design effort and time

Abstract

Section snippets

Related work

Methodology

Parametric estimation models

Evaluating the performance of the models

Project duration estimation

Conclusions

Journal of Product Innovation Management

Design Studies

Journal of Product Innovation Management

Journal of Product Innovation Management

The accuracy of project cost and duration estimates in industrial R&D

R&D Management

Criteria for controlling projects according to plan

Project Management Journal

NREC: Risk assessment and planning of complex designs

IEEE Design and Test of Computers

A method of programming measurement and estimation

IBM System Journal

The cost of developing large-scale software

IEEE Transactions on Computers