Real-Time Cost Object Controlling: Algorithmic Integration of Production Shop Floors with Enterprise Financial Ledgers in Continuous-Flow Manufacturing

Abstract

Continuous-flow manufacturing environments, cement production, chemical processing, and industrial pellet fabrication among them accumulate cost events at a rate that conventional ERP posting cycles cannot resolve without creating a persistent gap between the physical state of the production floor and what the enterprise financial ledger reflects. This paper presents a four-layer algorithmic integration framework that connects industrial Internet of Things edge infrastructure to the SAP S/4HANA cost object controlling layer through OPC UA and MQTT protocol translation, aiming for sub-500ms latency from sensor event to ACDOCA universal journal posting. The framework includes costing calendar design, valuation variant configuration, activity type assignment, work-in-progress calculation automation, variance extraction synchronized with statistical process control thresholds, material ledger actual costing with multi-level rollup, by-product net realizable value costing with live market price feeds, and predictive cost variance modeling. Published benchmarks indicate that deep learning models achieve 85-90% accuracy in manufacturing cost estimation contexts and hybrid approaches reach 80-90%, while ERP and AI integration in predictive maintenance delivers a 40% improvement in mean time to failure and a 30% reduction in maintenance costs. The framework addresses a documented gap in the literature: the algorithmic demands of real-time cost object controlling in continuous-flow manufacturing have not previously been treated as a unified integration architecture problem.