The Need for Project Management Metrics, Case Study Example

Performance metrics are essential to ensuring a successful project, realizing the intended goals and objectives within the parameters defined by the constraints. In selecting performance metrics, project managers find it considerably challenging determining and selecting the right number of metrics to employ. There are certain risks that are associated with selecting too many or too few performance metrics.

Section I: Implications of Too Many Metrics

In order to accurately depict the health of a given company, it is vital that there be a wealth of data and information. This information has readily become available with the development of business intelligence tools. As companies seek to collect more data, both financial and non-financial, there is the risk of losing the key facts that define the performance of the company.

The Overhead and Cost

The collection of metrics entails the use of company resources to maintain a tracking system for these metrics. This may include human resources and technology required to accomplish such endeavors. However, the use of these company resources translates to an increase in cost incurred in the tracking and analysis of these metrics (Pries and Quigley). When the overhead costs are too high, the project experiences too many costs that may potentially lead to the cessation of the project.

Time Consumption

One of the important aspects of collecting metrics for analysis of performance is the amount of time taken in this process. The number of metrics that is employed by any given project not only influences the costs that will be incurred, but the amount of time consumed throughout the project. The management and tracking of performance metrics may potentially steal time that may be allocated for the accomplished of the project’s work.

Critical Information

In tracking and monitoring performance of a project, the number of metrics employed is usually an indicator of the information that is critical to understanding the performance of the company. This is usually vital in helping with translating performance. However, when information pertaining to certain aspects of the project is too much, stakeholders find it difficult to determine the critical information from ordinary information. (International Project Management Assocation (IPMA)) Stakeholders require a simple and straight forward analysis of a projects performance. However, too many metrics may lead to a situation where stakeholders are not able to understand the project as a result of too many factors to take into consideration to analyze the projects.

Information with No Value

All information that is collected pertaining to the project may be an indicator of performance of a given aspect of the projects. Though this information may indicate the performance of a given aspect of the project, it may not necessarily have value to the project manager or stakeholders (Rad and Levin). When such information with no value is analyzed, it may potentially cause a situation of misguided metrics. Owing to the fact that the decision making process is dependent on the analysis derived from the collected metrics, there is a high risk of managers running down the project. Information with no value can cause managers to make misguided decisions about the project, which may potentially lead to scope creep and increased costs within the project.

Section II: Implications of Too Few Metrics

While managers are constantly looking to increase efficiency within a given project, there is the tendency for project managers to adopt an overly conservative approach, leading to the use of too few metrics in monitoring project performance. When the project has too few metrics, it is prone to suffer two significant drawbacks, (1) having insufficient right information, and (2) insufficient capacity to make informed decisions (Pavlak) (Basu).

Insufficient Right Information

Metrics are crucial indicators of the performance of the different processes entailed in the production process. Insufficient information may yield to metrics that fail to align with vital business/project functions (VBF). When the vital business functions are not adequately represented, the decision making process is skewed. Insufficient information would lead to an imbalance in the performance of some vital project processes, and develop an inability to address critical issues that affect performance when they occur (Eckerson). As a matter of fact, when too few metrics are employed, some problems that may curtail the performance of some critical project areas, affecting the performance of the whole project. Underlying problems may continue undetected as a result of the lack of measures to monitor such indicators. The project managers will be unable to make adjustments accordingly, eventually leading to the overrunning of costs and expenditures.

Insufficient Capacity to Make Informed Decisions

The main purpose that metrics are developed an integrated throughout the project is for the purpose of decision making. Metrics are crucial indicators or gauges of process operation. They identify the underlying gaps in resources and/or skills that are required to realize the project’s goals and objectives (Kerzner, Project Management 2.0). This allows for the project’s team to make the necessary adjustments, filling in the gaps to boost performance. Metrics helps the project manager focus on the potential and underlying issues that affect the performance of the team, relative to the project goals and objectives.

Without the capacity to make informed decisions, project managers are left to make decisions founded on guess work, eventually leading to scope creep. This deficiency in capacity also leads to untimely and inaccurate decisions. As much as management may employ complementary decision making tools, the project is likely to make decisions based on metrics that do not effectively contribute to the decision making process.

Diagnostic Measures

These are measures that cannot be categorized completely as lagging or leading indicators. However, capturing these measures are essential for the optimal performance of a project. Diagnostic measures indicate the health or current state of the project. They potentially identify underlying anomalies and be used to forecasts problems before they occur. Three examples of the most commonly used diagnostic measures include; (1) budgeted cost of work scheduled , (2) budgeted cost of work performed, and (3) actual cost of work performed.

• Budgeted Cost of Work Scheduled (BCWS): also known as Planned Value (PV). This is a running accrual of all the planned costs for ever individual activity entailed within the project that was anticipated to be complete up to the present.
• Budgeted Cost of Work Performed (BCWP): also known as Earned Value (EV). This is a running accrual of all the costs associated with all the project’s activities that are complete in the present.
• Actual Cost of Work Performed (ACWP): also known as Actual Cost (AC). This is a running accrual of all the actual costs of all the project’s activities that are complete at the present.

There are cases where the status input data provided does not tally with present and/or past data compared with other inputs. In such a case, the project lead has to discuss the status input data with the individual who provided the status input. This process is crucial in validating the information and ascertaining its accuracy and reliability using open question on the areas of concern. However, at times, these deviances represent a systemic or procedural problem. It is therefore essential to identify the underlying differences between the status input data and the project baseline so as to execute reactionary measures to remedy the underlying problem. When variances are adverse, they are precursors to a major problem in the near future. It is also important that one investigates a variance before handing in their status input. This is only to be done for those variances that are unaccounted for and present a radical variable within the project. It is important to ascertain whether the variance is an event that is likely to recur or it is a single, one-off event.

EVM as a Metric

EVM as a matric can be an effective tool in enhancing control on a given project. However, there are also numerous potential pitfalls to utilizing EVM as a metric.

Advantages

• It is best suited for a short-term work packages
• Easy and objective technique of obtaining the earned value for an activity (Kwak and Anbari)

Disadvantages

• It relies on summary metrics. Thus, poor performance can be hidden by acceptable summary level performance which cause easy to accomplish task and Level of Effort (LOE)
• The use of SPI fails to account for tasks critically and cannot be held true for schedule performance
• It over-relies on data provided in Format 1 and ignores the issues in the staffing or baseline data

It is prone to provision of unreliable data as most assume reliability of data. This can cause incomplete baselines.

The zone method is one of the best alternatives to EVM. This is because there are some projects that cannot sustain the administrative load that is synonymous of EVM. The Zone Method employs two specific metric; labour hoursand schedule events to keep track of the project’s progress. The schedule event provides the measurable beginning and end of a given project activity (Kwak and Anbari). Project costs are usually influenced by the amount of direct labour hours, s tracking labour hours can help track costs. This technique is essential in detecting underlying cost and schedule variances from the predetermined baseline.

Works Cited

Basu, Ron. “Managing quality in projects: An empirical study.” International Journal of Project Management (2014): 178-187. Electronic Source.

Eckerson, Wayne W. Performance dashboards : measuring, monitoring, and managing your business. New York: Wiley, 2010. Print.

International Project Management Assocation (IPMA). Project Perspectives 2012. Helsinki: The Project Management Association Finland (PMAF), 2012. Electronic Source. 29 June 2015. <http://ipma.ch/assets/re-perspectives_2012.pdf>.

Kerzner, Harold. Project Management 2.0. Hoboken: Wiley, 2014. Print.

—. Project Management Metrics, KPIs, and Dashboards: A Guide to Measuring and Monitoring Project Performance. Hoboken: John Wiley & Sons, Inc., 2013. Print.

Kwak, Young H and Frank T Anbari. “History, Practices, and Future of Earned Value Management in Government: Perspectives From NASA.” Project Management Journal 43.1 (2012): 77-90. Electronic Source.

Pavlak, Alex. “Project Troubleshooting: Tiger Teams for Reactive Risk Management.” The Project Management institute35.4 (2004): 5-14. Electronic Source.

Pries, Kim H and Jon M Quigley. Total quality management for project management. Boca Raton: CRC Press, 2013. Print.

Rad, Parviz F and Ginger Levin. Metrics for project management : formalized approaches. Vienna: Management Concepts, 2006. Print.

Schnapper, Melvin and Steven C Rollins. Value-based metrics for improving results : an enterprise project management toolkit. Ft. Lauderdale: J. Ross Pub., 2006. Print.