Real-Time Delivery Control for ICT Programs: A Telemetry-Driven Framework for Cost, Schedule, and Risk Optimization in Enterprise Project Execution

Abstract

High schedule overruns, cost deviations, and scope failure rates persist in information and communication technology (ICT) programs of business enterprises, with industry analyses showing that fewer than one-third of large-scale software projects are on time and on budget. These results are, in large part, avoidable because conventional project management paradigms are structurally limited: their reliance on periodic milestone reporting, fixed dashboards, and lagging performance indicators, which, by their very nature, are incapable of detecting meaningful sources of delivery risk in real time.

This paper presents the Telemetry-Driven Delivery Control Framework (TDCF), a broad, scaled-up approach to achieving sustainable visibility, anticipatory risk recognition, and responsive control across numerous complex enterprise ICT programs. The TDCF defines a five-layer architecture that includes: acquisition of telemetry signals, normalization of telemetry signals, mapping of composite performance indicators, a predictive analytics engine, and a role-stratified decision support interface. Each layer has a formal mathematical model that includes a signal confidence function, a composite Delivery Risk Index (DRI) determined by logistic regression using historical program outcome data, an ARIMA-based forecasting model, and a Monte Carlo-based estimator of delivery probability.

Empirical validation was conducted on three enterprise ICT programs, including cloud infrastructure migration, implementation of an enterprise SaaS platform, and transformation of organizational cybersecurity, with a combined budget of USD 26.7 million and 105 program professionals. Findings indicate a portfolio-average reduction in schedule deviation severity of 44.2, which is lower than the results reported by stakeholders in the bank, whose targets are set at a minimum of 2.6 on a five-point scale. All main results were also significant at p < 0.05. The framework can be applied to large-scale ICT programs such as cloud migration, SaaS implementation, cybersecurity transformation, and data platform modernization.