@article{9356,
  abstract     = {{In today’s manufacturing industry, enterprise-resource-planning (ERP) systems reach their limit when planning and scheduling production subject to multiple objectives and constraints. Advanced planning and scheduling (APS) systems provide these capabilities and are an extension for ERP systems. However, when integrating an APS and ERP system, the ERP data frequently lacks quality, hindering the APS system from working as required. This paper introduces a data quality (DQ) assessment framework that employs a Bayesian Network (BN) to perform quick DQ assessments based on expert interviews and DQ measurements with actual ERP data. We explain the BN’s functionality, design, and validation and show how using the perceived DQ of experts and a semi-supervised learning algorithm improves the BN’s predictions over time. We discuss applying our framework in an APS system implementation project involving an APS system provider and a medium-sized manufacturer of hydraulic cylinders. Despite considering the DQ assessment framework in such a specific context, it is not restricted to a particular domain. We close by discussing the framework’s limits, particularly the BN as a DQ assessment methodology and future works to improve its performance.}},
  author       = {{Herrmann, Jan-Phillip and Tackenberg, Sven and Padoano, Elio and Hartlief, Jörg and  Rautenstengel, Jens and Loeser, Christine and Böhme, Jörg }},
  issn         = {{1877-0509 }},
  journal      = {{Procedia Computer Science}},
  keywords     = {{Data Quality Assessment, Advanced Planning, Scheduling, Bayesian Network, Enterprise Resource Planning}},
  pages        = {{194--204}},
  publisher    = {{Elsevier}},
  title        = {{{An ERP Data Quality Assessment Framework for the Implementation of an APS system using Bayesian Networks}}},
  doi          = {{https://doi.org/10.1016/j.procs.2022.01.218}},
  volume       = {{200}},
  year         = {{2022}},
}

@inproceedings{588,
  abstract     = {{While common measurement techniques like gloss or color measurement are widely used in industry for quality assessment of furniture high gloss surfaces, they indicate only a weak correlation to the quality perceived by the customer. Topography based approaches achieve higher correlation to human perception but are often based on linear measurement which cannot be applied for an overall assessment of larger surfaces. Thus an algorithm was developed to calculate a specific value based on topographic features, such as orange peel, within the research project ‘Development of a comprehensive quality concept for furniture high gloss surfaces’, funded by the federal Ministry of Education and Research. For the evaluation of short waved structures on furniture high gloss surfaces the ratio of hill height to hill area was chosen. This parameter proves to be applicable for an evaluation of the extent of a single.
}},
  author       = {{Huxol, Andrea and Riegel, Adrian and Dekomien, Kerstin}},
  booktitle    = {{Production Engineering and Management}},
  editor       = {{Padoano, Elio and Villmer, Franz-Josef}},
  isbn         = {{978-3-941645-11-0}},
  keywords     = {{High gloss, surface measurement, topographic features, quality assessment}},
  location     = {{Trieste, Italy}},
  number       = {{1}},
  pages        = {{99--110}},
  title        = {{{Development of an Algorithm for Measuring the Quality of High Gloss Surfaces Correlated to Human Perception}}},
  year         = {{2015}},
}