Sample Accounting Research Paper Essay on Metrics

Metrics

Introduction

Metrics are standard ways of measuring and assessing the different indicators such as efficiency, performance, progress as well as the quality of the project deliverables. Metrics are important because they assist the project managers predict, improve together with the decision-making during the implementation of the project. As a result, it can be argued that matrices are essential tools because they assist the project managers to manage the organization projects in the right and intended manner. Moreover, metrics also help the managers to monitor the general performance of the project together with the identification and evaluation of the potential risks that can frustrate the overall implementation of the project. Additionally, metrics can assist in estimating the profitability of the project as well as monitoring and assessing the productivity of the project team. In this paper, four types of the metrics would be considered. These metrics include schedule and effort and cost variance, productivity and resource utilization metric, gross margin metric, and the triple bottom line metric (Klubeck, 2014).

Schedule and effort cost variance

I selected this metric because of its ability to predict accurately and assess the profitability of the project as a whole. This metric depends on the concept of Earned Value Management (EVM) as a tool for tracking the profitability of a project. The type of the data that this metric can support is ordinal. Since it includes varried project concepts. However, this metric has the ability to estimate the quality of data through focusing on the project scope together with the project cost. As a result, schedule and effort cost variance is a powerful tool for assessing the overall project performance and cost. As a cost determinant, this metric is capable of revealing the value, schedule variance together with the actual cost to accurately predict the project profitability. This implies that this the analysis of the data variables can support the metric (Klubeck, 2013).

Productivity and resource utilization

This is my second metric that I selected. I selected it because of its ability to assist the project managers to monitor how the resources utilized during the project implementation. It is important to note that resources utilization can be estimated by comparing the total effort that is spent on the total budget effort that is factored in for the project. Therefore, this metric can support the real value multi captive data. In establishing the data quality, this metric can establish whether there is underutilization or overutilization of the project resources. Consequently, managers are able to find out if they are working within the planned budget. Besides, productivity metric can also be used in assessing the project profitability. However, through the analysis of the data variables, the metric points at the need of time as an essential requirement for the success of this metric (Klubeck, 2014).

Triple Bottom Line

This type of metric encompasses the three main dimensions of performance. These three dimensions include the finance, environment, and society. I have selected this metric because it has incorporated both the social together with the ecological measures that are usually missing in the other metrics. Therefore, it can be argued that triple bottom line is one of complete metrics. The triple bottom line can support numerous data, but the most appropriate data are the random vector. However, the metric can be used to estimate the quality of the data with the help of the3Ps (people, planet, and profit). These 3Ps are vital since they point at period at which the project will remain in place. It is important to note that the triple bottom line can only be supported when there is a balance among the 3Ps that indicate the sustainability of the project (Klubeck, 2012).

References

Klubeck, M. (2012). Metrics. Project Management, 1(3), 112 134.

Klubeck, M. (2013). Using Metrics as Indicators. Metrics, 2(1), 83-95.

Klubeck, M. (2014). Advanced Metrics. Metrics, 1(1), 243-269.