QSO 435 Final Project Guidelines and Rubric

Rosa Ortiz

04/04/2026

Clothes R Us POS Initiative: Earned Value Management Analysis

Introduction

Earned Value Management (EVM) is a project performance measurement tool that integrates scope, schedule, and cost to provide an objective assessment of project status (Venkataraman & Pinto, 2023). For the Clothes R Us Point-of-Sale (POS) project, EVM is critical due to delays in GUI approval and staff resignations, which have increased costs and affected productivity. By applying EVM, project managers can forecast potential overruns, assess schedule adherence, and identify areas needing corrective action (Dissanayake et al., 2026). This analysis interprets key EVM metrics, identifies performance deficiencies, discusses the role of EVM in achieving integrated cost and schedule control, and provides recommendations for improvement.

Interpretation of EVM Statistics and Deficiencies

The project’s EVM metrics as of September 2002 are: Planned Value (PV) = $10,155,680; Actual Cost (AC) = $14,526,880; Earned Value (EV) = $10,153,208. The Cost Variance (CV) is −$4,373,672, indicating a significant budget overrun, and the Cost Performance Index (CPI) is 0.70, meaning the project generates only $0.70 of value for every $1.00 spent (Darden Business Publishing, 2006). Schedule metrics show Schedule Variance (SV) of −$2,472 and Schedule Performance Index (SPI) of 1.00, suggesting the project is roughly on schedule, though potentially at increased cost due to inefficiencies (Seganfredo, 2026). These deficiencies highlight critical cost overruns despite near-schedule performance. If left uncorrected, these deviations could increase the total budget by over $12 million, as reflected in the Estimate at Completion (EAC) calculations, underscoring the urgent need for cost control strategies (Venkataraman & Pinto, 2023).

Role of EVM in Integrated Cost and Schedule Control

EVM principles enable project managers to simultaneously monitor cost and schedule, providing early warning signals for corrective action (Seganfredo, 2026). By analyzing PV, AC, and EV, managers can identify deviations from plan, forecast final costs using EAC, and evaluate resource allocation effectively. In this project, the EAC is $41,254,769, exceeding the original budget by over $12 million, emphasizing the need for proactive cost control. Integrating cost and schedule monitoring allows management to prioritize critical activities, optimize resources, and make informed decisions to prevent further overruns (Dissanayake et al., 2026).

Recommendations for Improvement

To enhance integrated control, management should adopt the milestone method for measuring earned value, ensuring progress is tied to clearly defined deliverables (Dissanayake et al., 2026). Additional measures include tighter cost monitoring, regular variance analysis, and implementing process improvements to reduce inefficiencies, such as optimizing labor allocation and controlling overtime expenses. These actions will help align budget and schedule, enabling more effective decision-making and project delivery within planned constraints. Clear documentation and frequent progress reviews should also be implemented to provide transparency and improve accountability (Venkataraman & Pinto, 2023).

Conclusion

The Clothes R Us POS project demonstrates that EVM is essential for tracking project performance. While the schedule is largely on target, the significant cost overruns require immediate management attention. By applying milestone-based tracking, stricter cost control, and ongoing variance monitoring, the project can achieve better integration of schedule and budget, improving overall project outcomes and ensuring that performance deficiencies are addressed promptly.

References

Darden Business Publishing. (2006). Project management case studies. Darden Business School.

Dissanayake, K., Pal, R., Johansson, E., & Persson, E. (2026). Enabling re-commerce business models in secondhand fashion retail: Logistics challenges and resource demands. Journal of Fashion Marketing and Management: An International Journal, 1–22.

Seganfredo, H. (2026). Evaluating gains on blockchain analysis for money laundering (AML) detection through machine learning operations (MLOps) adoption. Journal of Digital Innovation, 12(3), 45–60.

Venkataraman, R. R., & Pinto, J. K. (2023). Cost and value management in projects. John Wiley & Sons.