Monitoring and Control as Success Tools in Factory Construction: An Earned Value Analysis

Factory construction projects involve high investment, tight schedules, and multiple stakeholders, making monitoring and control decisive processes for project success. This paper analyses, through a practical case, the application of Earned Value Management (EVM) as an integrated schedule and cost control tool in the construction of an agro-industrial processing plant in northern Mozambique. The applied, quantitative study is grounded in a literature review and a single exploratory case study using simulated monthly Planned Value, Earned Value, and Actual Cost data, parameterized on documented deviation drivers (rainy-season productivity loss, delays in imported inputs, and price escalation) to protect site confidentiality. The data-collection instrument was a structured monthly project-monitoring record with explicit physical-progress rules (quantities completed and weighted milestones); the sample corresponds to six consecutive monthly reporting cycles (months 1 to 6) of a single agro-industrial processing-plant construction project in Ribaué, Nampula province, selected purposively as a case rather than drawn from a probabilistic population. Results show a mean Schedule Performance Index (SPI) of 0.88 (SD = 0.015) and a mean Cost Performance Index (CPI) of 0.91 (SD = 0.024) across the six months, with the CPI declining monotonically from 0.95 to 0.88 and crossing the critical 0.90 threshold in month five; by month six, SPI stood at 0.89 and CPI at 0.88, indicating simultaneous schedule slippage and cost overrun, with an estimate at completion (EAC) of about 13.66 million MZN, roughly 14% above the 12.0 million MZN budget (scenario range +7% to +21%). A work-package breakdown shows that earthworks and foundations concentrate half of the cost overrun, while steel structure and roofing concentrate 71% of the schedule slippage. The study concludes that EVM, framed within the PMBOK monitoring and controlling process group, enables early deviation detection and diagnosis and supports timely corrective decisions, confirming its relevance as a success tool in industrial construction; because the data are simulated, the results demonstrate the method rather than measure real performance.