2026
Johannes Jordan; Oliver Antons; Julia Arlinghaus
From Data Quality Deficiencies to Production Inefficiency: Data Quality as a Bottleneck for AI-Based PPC Proceedings Article
In: Pezzotta, Giuditta; Gaiardelli, Paolo; Cimini, Chiara; Sala, Roberto; Romero, David; Baalsrud-Hauge, Jannicke (Ed.): Advances in Production Management Systems: Shaping the Future of Industry Through Sustainable, Data-Driven, and Human-Centric Production Systems, pp. 269–284, Springer Nature Switzerland, Cham, 2026, ISBN: 978-3-032-38606-9.
Abstract | Links | BibTeX | Tags: Articifial Intelligence, Data, Data analytics, Data Quality
@inproceedings{10.1007/978-3-032-38606-9_19,
title = {From Data Quality Deficiencies to Production Inefficiency: Data Quality as a Bottleneck for AI-Based PPC},
author = {Johannes Jordan and Oliver Antons and Julia Arlinghaus},
editor = {Giuditta Pezzotta and Paolo Gaiardelli and Chiara Cimini and Roberto Sala and David Romero and Jannicke Baalsrud-Hauge},
url = {https://link.springer.com/chapter/10.1007/978-3-032-38606-9_19},
doi = {10.1007/978-3-032-38606-9_19},
isbn = {978-3-032-38606-9},
year = {2026},
date = {2026-09-18},
urldate = {2026-09-18},
booktitle = {Advances in Production Management Systems: Shaping the Future of Industry Through Sustainable, Data-Driven, and Human-Centric Production Systems},
pages = {269\textendash284},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Data-driven decision support systems utilizing Artificial Intelligence (AI) are increasingly applied in Production Planning and Control (PPC) to improve operational performance. However, AI-based methods are found to be more sensitive to data quality deficiencies than conventional approaches, limiting their practical applicability. While previous research primarily highlights the requirement for high data quality, less attention has been paid to their impact on specific PPC tasks and the effectiveness of improvement strategies. This paper develops a conceptual linkage between data quality deficiencies, uncertainty in PPC tasks, and contingency theory, describing how improvement strategies can be aligned to support both conventional and AI-based smart PPC. A structured literature review is conducted to identify data quality deficiencies, their causes, and associated improvement strategies in PPC. The results indicate that data quality deficiencies increase uncertainty and demonstrate that AI-based methods are more sensitive to these deficiencies than conventional approaches. Furthermore, identified improvement strategies are structured according to their ability to either reduce the need for information processing or increase information processing capacity. The paper contributes to theory by providing guidance for selecting appropriate improvement strategies in smart manufacturing.},
keywords = {Articifial Intelligence, Data, Data analytics, Data Quality},
pubstate = {published},
tppubtype = {inproceedings}
}
Data-driven decision support systems utilizing Artificial Intelligence (AI) are increasingly applied in Production Planning and Control (PPC) to improve operational performance. However, AI-based methods are found to be more sensitive to data quality deficiencies than conventional approaches, limiting their practical applicability. While previous research primarily highlights the requirement for high data quality, less attention has been paid to their impact on specific PPC tasks and the effectiveness of improvement strategies. This paper develops a conceptual linkage between data quality deficiencies, uncertainty in PPC tasks, and contingency theory, describing how improvement strategies can be aligned to support both conventional and AI-based smart PPC. A structured literature review is conducted to identify data quality deficiencies, their causes, and associated improvement strategies in PPC. The results indicate that data quality deficiencies increase uncertainty and demonstrate that AI-based methods are more sensitive to these deficiencies than conventional approaches. Furthermore, identified improvement strategies are structured according to their ability to either reduce the need for information processing or increase information processing capacity. The paper contributes to theory by providing guidance for selecting appropriate improvement strategies in smart manufacturing.
Johannes Jordan; Oliver Antons; Julia C. Arlinghaus
Assessing Data Quality Dimensions And Their Impact On Production Planning And Control Proceedings Article
In: Proceedings of the Conference on Production Systems and Logistics: CPSL 2026, pp. 401–411, Offenburg: publish-Ing. 2026.
Abstract | Links | BibTeX | Tags: Data Quality, Key Performance Indicators, Manufacturing, Production control, Production Planning, Production planning and control
@inproceedings{jordan2026assessing,
title = {Assessing Data Quality Dimensions And Their Impact On Production Planning And Control},
author = {Johannes Jordan and Oliver Antons and Julia C. Arlinghaus},
url = {https://repo.uni-hannover.de/handle/123456789/21158},
doi = {10.15488/21002},
year = {2026},
date = {2026-01-01},
urldate = {2026-01-01},
booktitle = {Proceedings of the Conference on Production Systems and Logistics: CPSL 2026},
pages = {401\textendash411},
organization = {Offenburg: publish-Ing.},
abstract = {Accurate, reliable and timely data are the foundation for decision-making in production planning and control (PPC). Relying on an ever-growing number of heterogeneous and interconnected data sources and increasing internal and external uncertainties, software systems are required to gather and process data utilized in decision-making processes. At the same time, data quality is often viewed as given in production research while interconnected production facilities, cyber-physical systems, workers and software systems form a complex network for data exchange and propagation. This paper identifies and categorizes data quality deficiencies relevant to PPC tasks across strategic, tactical and operational planning horizons in order to identify the specific requirements of the tasks. To achieve this, a structured literature review was conducted to study the influence of data quality on the performance of production systems. First, the existing literature was analyzed to determine data quality deficiencies that occur in PPC. Based on the findings the cause-and-effect relationships between data quality and the propagation of these deficiencies through data processing, management, decision-making, and production performance were examined. The results indicate that data quality deficiencies lead to manifold issues, from limiting the effectiveness of PPC tasks to the failure of automated PPC solutions in production after successful prototyping.},
keywords = {Data Quality, Key Performance Indicators, Manufacturing, Production control, Production Planning, Production planning and control},
pubstate = {published},
tppubtype = {inproceedings}
}
Accurate, reliable and timely data are the foundation for decision-making in production planning and control (PPC). Relying on an ever-growing number of heterogeneous and interconnected data sources and increasing internal and external uncertainties, software systems are required to gather and process data utilized in decision-making processes. At the same time, data quality is often viewed as given in production research while interconnected production facilities, cyber-physical systems, workers and software systems form a complex network for data exchange and propagation. This paper identifies and categorizes data quality deficiencies relevant to PPC tasks across strategic, tactical and operational planning horizons in order to identify the specific requirements of the tasks. To achieve this, a structured literature review was conducted to study the influence of data quality on the performance of production systems. First, the existing literature was analyzed to determine data quality deficiencies that occur in PPC. Based on the findings the cause-and-effect relationships between data quality and the propagation of these deficiencies through data processing, management, decision-making, and production performance were examined. The results indicate that data quality deficiencies lead to manifold issues, from limiting the effectiveness of PPC tasks to the failure of automated PPC solutions in production after successful prototyping.