Parametric Estimating: Can it be used in Non-Engineering Domain?

This report discusses the possibility of using parametric estimating in non-engineering projects. It explains the technique of parametric estimating and justifies its applicability in non-engineering projects.

Complex Project Management
Name of the Student
Name of the University
Author Note

Table of Contents
Introduction:...............................................................................................................................3
Parametric Estimating:...............................................................................................................3
Justification:...............................................................................................................................4
Conclusion:................................................................................................................................4

Introduction:
In the report the paper that will be described is the Terry Cooke-Davies Aspect of
Complexity. The main discussion will be made whether the parametric estimating can be
used in the non-engineering domain or not. The discussion will be made based on the topic
and a proper justification will be given to the reason.
Parametric Estimating:
The technique of parametric estimating is used for estimating the time and the cost for
a particular project. The unit of cost and time is used to define the parametric estimating.
There are several steps to apply the parametric estimating. The parametric estimation is
mainly used in the engineering projects where the technical work done frequently (Martin &
Pham, 2015). The steps of implementing the parametric estimating is given below:
 Parametric cost identification cost estimation and then value will be returned
to the ROI.
 Assumptions and drawbacks will be identified.
 Old projects data will be collected (technical data, design data, cost data and
schedule data) (Cespedes & Sun, 2014).
 Gathered data then normalized for further use.
 With the cost drivers independent variables are related.
 Using the variable a proper hypothesis is made.
 Cost estimation is documented
From the article it can be said that the parametric estimation works well in the
technical department of a project. Different various maturity model are used in
this estimation technique. There is a chance that it can be used in the non-
engineering domain as well.

Justification:
In the above section of the report it is discussed that how the parametric estimating
works. The provided article stated that the parametric estimation is mainly works in the
engineering projects (Heess et al., 2015). So there is an even possibility that the parametric
estimating can be used in the engineering projects and on the non-engineering projects.
The parametric estimating cannot be used in the non-engineering projects. From the
article it can be identified that the parametric estimating can perform better in the estimation
of the technical work. And in the non-engineering project the amount of technical work is so
less that the parametric estimating will not be needed (Berges et al., 2017). A normal
estimation process will be sufficient for this kind of project to estimate the cost and the tie for
the project.
There is a possibility that the parametric estimating can be implemented in the non-
engineering domain projects. The main reason behind this is that for every project there is a
factor of time and cost presented. To complete the project successfully it is needed to
estimate the duration and the budget of the project. The parametric estimating is a technique
that will estimate the time and the cost accurately than the other methods.
Conclusion:
From the above discussion it ca be concluded that the parametric estimation is the
most accurate technique to estimate the project’s cost and time. The parametric estimation
can be used in the non-engineering projects like the welfare projects and there is also a
chance that the parametric estimating cannot be used in the non-engineering projects.

References:
Berges, J., Reygers, K., Tanji, N., & Venugopalan, R. (2017). Parametric estimate of the
relative photon yields from the glasma and the quark-gluon plasma in heavy-ion
collisions. Physical Review C, 95(5), 054904.
Cespedes, M., & Sun, J. (2014). Adaptive control of grid-connected inverters based on online
grid impedance measurements. IEEE Transactions on sustainable energy, 5(2), 516-
523.
Heess, N., Wayne, G., Silver, D., Lillicrap, T., Erez, T., & Tassa, Y. (2015). Learning
continuous control policies by stochastic value gradients. In Advances in Neural
Information Processing Systems (pp. 2944-2952).
Martin, W., & Pham, C. S. (2015). Estimating the gravity model when zero trade flows are
frequent and economically determined. The World Bank.