---
_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'
...
---
_id: '12803'
abstract:
- lang: eng
  text: The increasing amount of alarms and information for an operator in a modern
    plant becomes a significant safety risk. Although the notifications are a valuable
    support, they also lead to the curse of overloading with information for the operator.
    Due to the huge amount of alarms it is almost impossible to separate the crucial
    information from the insignificant ones. Therefore, new procedures are required
    to reduce these alarm floods and support the operator to minimize the safety risk.
    One approach is based on learning a causal model that represents the relationships
    between the alarms. This allows alarm sequences that are causally implied to be
    reduced to the root cause alarm. Fundamental element of this approach is the causal
    model. Therefore in this work, different probabilistic graphical models are considered
    and evaluated on the basis of appropriate criteria. A real use case of a bottle
    filling module serves as a benchmark for how well they are suitable as a causal
    model for the application in alarm flood reduction.
author:
- first_name: Paul
  full_name: Wunderlich, Paul
  id: '52317'
  last_name: Wunderlich
- first_name: Nemanja
  full_name: Hranisavljevic, Nemanja
  id: '62376'
  last_name: Hranisavljevic
citation:
  ama: Wunderlich P, Hranisavljevic N. <i>Comparison of Different Probabilistic Graphical
    Models as Causal Models in Alarm Flood Reduction</i>. (Aalto University, Finland,
    Tampere University, Finland, Finnish Society of Automation, Finland, eds.). IEEE;
    2019:1285-1290. doi:<a href="https://doi.org/10.1109/indin41052.2019.8972251">10.1109/indin41052.2019.8972251</a>
  apa: Wunderlich, P., &#38; Hranisavljevic, N. (2019). Comparison of Different Probabilistic
    Graphical Models as Causal Models in Alarm Flood Reduction. In Aalto University,
    Finland, Tampere University, Finland, &#38; Finnish Society of Automation, Finland
    (Eds.), <i>2019 IEEE 17th International Conference on Industrial Informatics (INDIN)</i>
    (pp. 1285–1290). IEEE. <a href="https://doi.org/10.1109/indin41052.2019.8972251">https://doi.org/10.1109/indin41052.2019.8972251</a>
  bjps: '<b>Wunderlich P and Hranisavljevic N</b> (2019) <i>Comparison of Different
    Probabilistic Graphical Models as Causal Models in Alarm Flood Reduction</i>,
    Aalto University, Finland, Tampere University, Finland, and Finnish Society of
    Automation, Finland (eds). [Piscataway, NJ]: IEEE.'
  chicago: 'Wunderlich, Paul, and Nemanja Hranisavljevic. <i>Comparison of Different
    Probabilistic Graphical Models as Causal Models in Alarm Flood Reduction</i>.
    Edited by Aalto University, Finland, Tampere University, Finland, and Finnish
    Society of Automation, Finland. <i>2019 IEEE 17th International Conference on
    Industrial Informatics (INDIN)</i>. [Piscataway, NJ]: IEEE, 2019. <a href="https://doi.org/10.1109/indin41052.2019.8972251">https://doi.org/10.1109/indin41052.2019.8972251</a>.'
  chicago-de: 'Wunderlich, Paul und Nemanja Hranisavljevic. 2019. <i>Comparison of
    Different Probabilistic Graphical Models as Causal Models in Alarm Flood Reduction</i>.
    Hg. von Aalto University, Finland, Tampere University, Finland, und Finnish Society
    of Automation, Finland. <i>2019 IEEE 17th International Conference on Industrial
    Informatics (INDIN)</i>. [Piscataway, NJ]: IEEE. doi:<a href="https://doi.org/10.1109/indin41052.2019.8972251">10.1109/indin41052.2019.8972251</a>,
    .'
  din1505-2-1: '<span style="font-variant:small-caps;">Wunderlich, Paul</span> ; <span
    style="font-variant:small-caps;">Hranisavljevic, Nemanja</span> ; <span style="font-variant:small-caps;">Aalto
    University, Finland</span> ; <span style="font-variant:small-caps;">Tampere University,
    Finland</span> ; <span style="font-variant:small-caps;">Finnish Society of Automation,
    Finland</span> (Hrsg.): <i>Comparison of Different Probabilistic Graphical Models
    as Causal Models in Alarm Flood Reduction</i>. [Piscataway, NJ] : IEEE, 2019'
  havard: P. Wunderlich, N. Hranisavljevic, Comparison of Different Probabilistic
    Graphical Models as Causal Models in Alarm Flood Reduction, IEEE, [Piscataway,
    NJ], 2019.
  ieee: 'P. Wunderlich and N. Hranisavljevic, <i>Comparison of Different Probabilistic
    Graphical Models as Causal Models in Alarm Flood Reduction</i>. [Piscataway, NJ]:
    IEEE, 2019, pp. 1285–1290. doi: <a href="https://doi.org/10.1109/indin41052.2019.8972251">10.1109/indin41052.2019.8972251</a>.'
  mla: Wunderlich, Paul, and Nemanja Hranisavljevic. “Comparison of Different Probabilistic
    Graphical Models as Causal Models in Alarm Flood Reduction.” <i>2019 IEEE 17th
    International Conference on Industrial Informatics (INDIN)</i>, edited by Aalto
    University, Finland et al., IEEE, 2019, pp. 1285–90, <a href="https://doi.org/10.1109/indin41052.2019.8972251">https://doi.org/10.1109/indin41052.2019.8972251</a>.
  short: P. Wunderlich, N. Hranisavljevic, Comparison of Different Probabilistic Graphical
    Models as Causal Models in Alarm Flood Reduction, IEEE, [Piscataway, NJ], 2019.
  ufg: '<b>Wunderlich, Paul/Hranisavljevic, Nemanja</b>: Comparison of Different Probabilistic
    Graphical Models as Causal Models in Alarm Flood Reduction, hg. von Aalto University,
    Finland/Tampere University, Finland, Finnish Society of Automation, Finland, [Piscataway,
    NJ] 2019.'
  van: 'Wunderlich P, Hranisavljevic N. Comparison of Different Probabilistic Graphical
    Models as Causal Models in Alarm Flood Reduction. Aalto University, Finland, Tampere
    University, Finland, Finnish Society of Automation, Finland, editors. 2019 IEEE
    17th International Conference on Industrial Informatics (INDIN). [Piscataway,
    NJ]: IEEE; 2019.'
conference:
  end_date: 2019-07-25
  location: 'Helsinki, Finland '
  name: 17th IEEE International Conference on Industrial Informatics (INDIN)
  start_date: 2019-07-22
corporate_editor:
- Aalto University, Finland
- Tampere University, Finland
- Finnish Society of Automation, Finland
date_created: 2025-04-16T06:29:04Z
date_updated: 2025-06-26T13:38:06Z
department:
- _id: DEP5023
doi: 10.1109/indin41052.2019.8972251
keyword:
- probabilistic graphical model
- causal model
- alarm flood reduction
- Bayesian network
- Markov chain
- restricted boltzmann machine
- automata
language:
- iso: eng
page: 1285-1290
place: '[Piscataway, NJ]'
publication: 2019 IEEE 17th International Conference on Industrial Informatics (INDIN)
publication_identifier:
  eisbn:
  - 978-1-7281-2927-3
  isbn:
  - 978-1-7281-2928-0
publication_status: published
publisher: IEEE
status: public
title: Comparison of Different Probabilistic Graphical Models as Causal Models in
  Alarm Flood Reduction
type: conference_editor_article
user_id: '83781'
year: '2019'
...