---
_id: '9356'
abstract:
- lang: eng
  text: 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:
- first_name: Jan-Phillip
  full_name: Herrmann, Jan-Phillip
  id: '75846'
  last_name: Herrmann
- first_name: Sven
  full_name: Tackenberg, Sven
  id: '71470'
  last_name: Tackenberg
- first_name: Elio
  full_name: Padoano, Elio
  last_name: Padoano
- first_name: Jörg
  full_name: Hartlief, Jörg
  last_name: Hartlief
- first_name: Jens
  full_name: ' Rautenstengel, Jens'
  last_name: ' Rautenstengel'
- first_name: Christine
  full_name: Loeser, Christine
  last_name: Loeser
- first_name: 'Jörg '
  full_name: 'Böhme, Jörg '
  last_name: Böhme
citation:
  ama: Herrmann JP, Tackenberg S, Padoano E, et al. An ERP Data Quality Assessment
    Framework for the Implementation of an APS system using Bayesian Networks. <i>Procedia
    Computer Science</i>. 2022;200:194-204. doi:<a href="https://doi.org/10.1016/j.procs.2022.01.218">https://doi.org/10.1016/j.procs.2022.01.218</a>
  apa: Herrmann, J.-P., Tackenberg, S., Padoano, E., Hartlief, J.,  Rautenstengel,
    J., Loeser, C., &#38; Böhme, J. (2022). An ERP Data Quality Assessment Framework
    for the Implementation of an APS system using Bayesian Networks. <i>Procedia Computer
    Science</i>, <i>200</i>, 194–204. <a href="https://doi.org/10.1016/j.procs.2022.01.218">https://doi.org/10.1016/j.procs.2022.01.218</a>
  bjps: <b>Herrmann J-P <i>et al.</i></b> (2022) An ERP Data Quality Assessment Framework
    for the Implementation of an APS System Using Bayesian Networks. <i>Procedia Computer
    Science</i> <b>200</b>, 194–204.
  chicago: 'Herrmann, Jan-Phillip, Sven Tackenberg, Elio Padoano, Jörg Hartlief, Jens  Rautenstengel,
    Christine Loeser, and Jörg  Böhme. “An ERP Data Quality Assessment Framework for
    the Implementation of an APS System Using Bayesian Networks.” <i>Procedia Computer
    Science</i> 200 (2022): 194–204. <a href="https://doi.org/10.1016/j.procs.2022.01.218">https://doi.org/10.1016/j.procs.2022.01.218</a>.'
  chicago-de: 'Herrmann, Jan-Phillip, Sven Tackenberg, Elio Padoano, Jörg Hartlief,
    Jens  Rautenstengel, Christine Loeser und Jörg  Böhme. 2022. An ERP Data Quality
    Assessment Framework for the Implementation of an APS system using Bayesian Networks.
    <i>Procedia Computer Science</i> 200: 194–204. doi:<a href="https://doi.org/10.1016/j.procs.2022.01.218">https://doi.org/10.1016/j.procs.2022.01.218</a>,
    .'
  din1505-2-1: '<span style="font-variant:small-caps;">Herrmann, Jan-Phillip</span>
    ; <span style="font-variant:small-caps;">Tackenberg, Sven</span> ; <span style="font-variant:small-caps;">Padoano,
    Elio</span> ; <span style="font-variant:small-caps;">Hartlief, Jörg</span> ; <span
    style="font-variant:small-caps;"> Rautenstengel, Jens</span> ; <span style="font-variant:small-caps;">Loeser,
    Christine</span> ; <span style="font-variant:small-caps;">Böhme, Jörg </span>:
    An ERP Data Quality Assessment Framework for the Implementation of an APS system
    using Bayesian Networks. In: <i>Procedia Computer Science</i> Bd. 200, Elsevier
    (2022), S. 194–204'
  havard: J.-P. Herrmann, S. Tackenberg, E. Padoano, J. Hartlief, J.  Rautenstengel,
    C. Loeser, J. Böhme, An ERP Data Quality Assessment Framework for the Implementation
    of an APS system using Bayesian Networks, Procedia Computer Science. 200 (2022)
    194–204.
  ieee: 'J.-P. Herrmann <i>et al.</i>, “An ERP Data Quality Assessment Framework for
    the Implementation of an APS system using Bayesian Networks,” <i>Procedia Computer
    Science</i>, vol. 200, pp. 194–204, 2022, doi: <a href="https://doi.org/10.1016/j.procs.2022.01.218">https://doi.org/10.1016/j.procs.2022.01.218</a>.'
  mla: Herrmann, Jan-Phillip, et al. “An ERP Data Quality Assessment Framework for
    the Implementation of an APS System Using Bayesian Networks.” <i>Procedia Computer
    Science</i>, vol. 200, 2022, pp. 194–204, <a href="https://doi.org/10.1016/j.procs.2022.01.218">https://doi.org/10.1016/j.procs.2022.01.218</a>.
  short: J.-P. Herrmann, S. Tackenberg, E. Padoano, J. Hartlief, J.  Rautenstengel,
    C. Loeser, J. Böhme, Procedia Computer Science 200 (2022) 194–204.
  ufg: '<b>Herrmann, Jan-Phillip u. a.</b>: An ERP Data Quality Assessment Framework
    for the Implementation of an APS system using Bayesian Networks, in: <i>Procedia
    Computer Science</i> 200 (2022),  S. 194–204.'
  van: Herrmann JP, Tackenberg S, Padoano E, Hartlief J,  Rautenstengel J, Loeser
    C, et al. An ERP Data Quality Assessment Framework for the Implementation of an
    APS system using Bayesian Networks. Procedia Computer Science. 2022;200:194–204.
date_created: 2023-01-25T12:00:32Z
date_updated: 2024-08-06T11:45:41Z
department:
- _id: DEP7020
doi: https://doi.org/10.1016/j.procs.2022.01.218
intvolume: '       200'
keyword:
- Data Quality Assessment
- Advanced Planning
- Scheduling
- Bayesian Network
- Enterprise Resource Planning
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://www.sciencedirect.com/science/article/pii/S1877050922002277
oa: '1'
page: 194-204
publication: Procedia Computer Science
publication_identifier:
  issn:
  - '1877-0509 '
publication_status: published
publisher: Elsevier
quality_controlled: '1'
status: public
title: An ERP Data Quality Assessment Framework for the Implementation of an APS system
  using Bayesian Networks
type: journal_article
user_id: '83781'
volume: 200
year: '2022'
...