Human-Centered Decision Support for Data Analytics in Production Systems

Abstract

Manufacturing companies increasingly rely on production data to enable data-driven decisions, yet the feasibility and reliability of analytics are often constrained by insufficient data quality. To support practitioners in selecting suitable analytics methods under real-world constraints, this paper validates a human-centered decision-support approach that links a data-quality maturity assessment to a catalog of production- and quality-related analytics methods. Building on an ISO/IEC based process assessment logic and data-quality measures, the approach was evaluated through an industrial single-case study in a manufacturing plant. An exploratory analysis of an SAP quality dataset informed three semi-structured expert interviews spanning return-quality analytics, production-quality analytics, and data engineering. The scoped assessment focused on the data lifecycle phase Data Processing and selected measures for completeness, consistency, and currentness. Findings show that completeness and semantic accuracy are the most consequential limiting factors for downstream analytics, while duplicates and some inconsistencies can be mitigated effectively through automated, rule-based controls. Data quality is shown to be a socio-technical outcome shaped by enterprise systems, process design, and work practices. Documented processes may diverge from lived practice, and automation can both reduce input variability and introduce new failure dependencies. Based on the case evidence, the paper derives practical requirements for maturity-based decision support, including reduced implicit expertise demands, clearer separation of process and data documentation versus execution, and explicit checks for operational process adherence and automation context.

Keywords: Smart Manufacturing, Production Analytics, Data Quality, Maturity Model, Case Study

DOI: 10.54941/ahfe1007780