Predictive Assessment of Digital Transformation in Heavy Equipment Manufacturing Using a Digital Thread and Discrete-Event Simulation

This study develops a digital-thread framework for predictive assessment of digital transformation in heavy equipment manufacturing and evaluates it through resource-constrained discrete-event simulation (DES) of the VFP-1600 route. The model represents nine stages, shared engineering capacity, Poisson order releases, data-readiness lags, and first-come-first-served queues. Seven expert rankings yield Kendall’s W = 0.893 and a normalized Digital Maturity Index of 0.264. Maturity priorities guide the activation of stage-level intervention assumptions. Five specified configurations are compared in 10,000 independent annual-cohort replications per load level, using paired random inputs. At six expected releases per year, simulated mean cycle time is 163.35 days for the baseline, 126.70 days for integrated PLM + MES + QMS/IoT, and 119.46 days with APS-associated duration assumptions. At twelve releases, the corresponding means are 213.02, 159.23, and 149.58 days. Directly measured time-average work-in-process is checked against an exact finite-horizon residence-time identity. A joint sensitivity experiment varies nine intervention-realization factors, resource occupation by delays, and arrival intensity. Its 5th–95th percentile ranges of estimated mean cycle-time benefit are 9.0–25.3% and 11.0–28.7%. The computational experiment shows how resource contention changes both the magnitude and uncertainty of projected benefits. The framework supports ex ante comparison of explicitly specified production and transformation scenarios.