QSO 420 Milestone Three Guidelines and Rubric Overview

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Clothes R Us POS Initiative Earned Value Management (EVM) Analysis.

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Introduction

Earned Value Management (EVM) is a project performance measurement system that combines scope, schedule, and cost into a single system. It also gives project managers an objective method for determining whether the project is being executed within the scheduled and planned budget. EVM is useful in that it is a tool that is numerically based on planned work, actual spending, and completed work. In the case of the Clothes R Us Point-of-Sale (POS) project, EVM is particularly significant, as there were delays in signing the GUI and in the resignation of the product management staff. Such incidents must have raised expenses and decreased productivity. This milestone uses the EVM formula to measure the project's performance and interpret the findings.

Earned Value Management Calculations.

EVM is anchored on three values:

Planned Value (PV): (Budgeted cost of) scheduled work.

• Actual Cost (AC): This is the cost of the work done.

• Earned Value (EV): Work done value that is budgeted .

The project values as of September 2002 are:

• PV (BCWS) = $10,155,680

• AC (ACWP) = $14,526,880

• EV (BCWP) = $10,153,208

Calculating EVM Measures

Cost Variance (CV)

Formula: CV = EV − AC

Calculation: CV = $10,153,208 − $14,526,880 = −$4,373,672

Interpretation: The negative CV indicates that the project is off-budget by 4,373,672.

Cost Performance Index (CPI)

Formula: CPI = EV ÷ AC

Calculation: CPI = $10,153,208 ÷ $14,526,880 = 0.699 ≈ 0.70

Interpretation: A CPI of 0.70 indicates that the project will generate a value of 0.70 for every 1.00 spent, which is not very cost-efficient.

Schedule Variance (SV)

Formula: SV = EV − PV

Calculation: SV = $10,153,208 − $10,155,680 = −$2,472

Interpretation: The project is somewhat lagging.

Schedule Performance Index (SPI)

Formula: SPI = EV ÷ PV

Calculation: SPI = $10,153,208 ÷ $10,155,680 = 0.9998 ≈ 1.00

Interpretation: SPI is approximately 1.00, indicating the project is almost on schedule.

Project Performance Prognostication.

Budget at Completion (BAC) of the project is:

• BAC = $28,878,338

(Darden Business Publishing, 2006)

Estimate at Completion (EAC)

Formula: EAC = BAC ÷ CPI

Calculation: EAC = $28,878,338 ÷ 0.70 = $41,254,768.57

Estimate to Complete (ETC)

Formula: ETC = EAC − AC

Calculation: ETC = $41,254,768.57 − $14,526,880 = $26,727,888.57

Variance at Completion (VAC)

Formula: VAC = BAC − EAC

Calculation: VAC = $28,878,338 − $41,254,768.57 = −$12,376,430.57

The VAC indicates that it has an estimated budget in excess of nearly 12.38 million dollars.

Implications of CPI and SPI

If CPI is greater than 1.0, the project is cost-effective and is within budget. According to Seganfredo (2026), when CPI is less than 1.0, then the project is over-budgeted and inefficient. In this scenario, CPI = 0.70, indicating significant overspending that may have resulted from overtime, rework, or poor planning.

In case SPI is less than 1.0, the project is keeping up or ahead of schedule. According to Venkataraman (2023), if the SPI is less than 1.0, the project is behind schedule. In this case, SPI = 1.00, indicating that the project is near schedule. Nevertheless, this could have been achieved at a high cost, e.g., by adding labor or expediting the work, which might have been behind the low CPI.

Conclusion

Clothes 'R' Us POS project is in a dire financial situation. Although the schedule performance is stable, the cost performance is in a very critical state. The project is over budget by 4.37 million, and the CPI of 0.70 indicates. Project forecasting indicates the project would cost $41.25 million, exceeding the expected budget of $ 12.38 million. To reduce subsequent overruns, management should implement additional cost control measures and address the causes of inefficiency.

References

Alamsyah, A., & Ramdhani, S. F. N. Waste Management Bulletin.

Aigbavboa, C., & Kissi, E. (2025). Digital Management of Construction Costs. Elsevier.

Khan, T., Boda, P. P., Björklund, A., & Malmberg, S. (2026). Seconds Matter: Rapid Non-Contact Monitoring of Heart and Respiratory Rate from Face Videos. Sensors, 26(5), 1506.

Seganfredo, H. (2026). Evaluating Gains on Blockchain Analysis for Money Laundering (AML) detection through Machine Learning Operations (MLOps) adoption.

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