Preserving Metrological Traceability in Industrial Reporting Systems

A Framework for Sequential Cumulative Dosing

Abstract

Digital weighing systems in computerized manufacturing environments typically report measurement results as single numeric values, creating an appearance of precision that may not be supported by the underlying measurement system. This becomes particularly significant in sequential cumulative dosing systems, where multiple weighing events are combined to form a single batch quantity.

In sequential cumulative dosing, individual measurements are not independent. Each dosing event contributes to an accumulated platform weight that serves as the reference condition for subsequent ingredient additions. Consequently, uncertainty associated with earlier dosing events propagates throughout the batching sequence. When uncertainty is not evaluated and recorded at the point of measurement, it cannot be propagated through the process or reconstructed from historical production records.

In the dosing system investigated, the SCADA implementation contains no embedded measurement uncertainty model. As a result, dosing records and manufacturing reports present cumulative dosing data as deterministic quantities without representation of the underlying measurement uncertainty, creating a disconnect between reported values and the metrological capability of the measurement system.

This paper identifies uncertainty propagation as a metrological challenge unique to sequential cumulative dosing systems and proposes a GUM-consistent conceptual framework in which uncertainty is evaluated at individual dosing events, propagated through the cumulative dosing process, integrated into SCADA records, and incorporated into manufacturing reports. The framework provides a structured basis for improving the metrological completeness and interpretability of manufacturing data.