Authors - Gulshat Amirkhanova, Alikhan Amirkhanov, Gulnur Tyulepberdinova, Bauyrzhan Amirkhanov, Yenlik Faruzkyzy Abstract - Digital twins for manufacturing usually rely on a Manufacturing Execution System (MES) that records what each operation does and when. Many small and medium-sized enterprises (SMEs) lack such systems yet increasingly meter the electricity of individual machines. This paper asks whether a plant's process can be reconstructed, simulated and optimised from electrical metering alone, and formalises the answer as Energy-Driven Process Reconstruction (EDPR). The subject is a commercial bakery in Kazakhstan whose fifteen machines are metered and integrated through OpenEgiz, a digital-twin platform built on the open-source OpenTwins framework; about 65 million readings span 198 days. EDPR detects machine states from active power, abstracts them into events, and links events into per-batch chains by a batch-anchored lead-lag operator with one-to-one assignment in O(N log N) time. With no MES ground truth available, EDPR is scored on a labelled synthetic benchmark, where it reaches an eventdetection F1 of 0.97 and, against three baselines, is the only method that combines competitive chain accuracy with a valid one-to-one batch partition. On the real plant, conformance checking raises model precision from 0.22 for a naive day-case model to 1.00 for the EDPR reconstruction, and a discrete-event twin parameterised only by the energy-derived lead times reproduces the observed throughput of about 21 batches per day. Using the twin, three demand-side measures are estimated to give a potential electricity-cost reduction of 20.5 per cent at constant output. The pipeline uses only open-source software and permachine power.
