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A Framework Supporting Supply Chain Complexity and Confidentiality Using Process Mining and Auto Identification Technology

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Innovation in Information Systems and Technologies to Support Learning Research (EMENA-ISTL 2019)

Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 7))

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Abstract

Supply chain is a network between a company and its suppliers to produce and distribute a product to the final customer. This network includes different activities, people, entities, etc. The interaction between these elements provides a cross-organization Business Process. It has mainly treated with process mining techniques, to handle the resulted process instances as event logs. These events are obtained by implementing the auto identification technology within the supply chain related to materials or personal. By doing so, the provided events are not simply presented, they emerged more challenges like complexity and data confidentiality. Therefore, in this work we develop a descriptive framework, based on a literature study, to answer supply chain challenges related to the process mining field. This is done, by implementing process mining within the supply chain, using auto identification technology and taking into consideration recent challenges related to cross-organization Business Process.

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Acknowledgement

This work was supported by the National Center for Scientific and Technical Research (CNRST) in Rabat, Morocco.

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Authors and Affiliations

  1. LRIT Associated Unit to CNRST (URAC 29), Rabat IT Center, Faculty of Sciences, Mohammed V University, Rabat, Morocco

    Zineb Lamghari, Maryam Radgui, Rajaa Saidi & Moulay Driss Rahmani

  2. SI2M Laboratory, National Institute of Statistics and Applied Economics, Rabat, Morocco

    Maryam Radgui & Rajaa Saidi

Authors
  1. Zineb Lamghari
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  2. Maryam Radgui
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  3. Rajaa Saidi
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  4. Moulay Driss Rahmani
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Corresponding author

Correspondence to Zineb Lamghari .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Faculty of Sciences, Mohammed Premier University, Oujda, Morocco

    Mohammed Serrhini

  2. Department of Computer Science, School of Management Sciences, Engineering and Technology, Atlantica University, Lisbon, Portugal

    Carla Silva

  3. Department of Computer Science, Taif University, College of Computers and Information Technology, Al Huwaya, Saudi Arabia

    Sultan Aljahdali

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Lamghari, Z., Radgui, M., Saidi, R., Rahmani, M.D. (2020). A Framework Supporting Supply Chain Complexity and Confidentiality Using Process Mining and Auto Identification Technology. In: Serrhini, M., Silva, C., Aljahdali, S. (eds) Innovation in Information Systems and Technologies to Support Learning Research. EMENA-ISTL 2019. Learning and Analytics in Intelligent Systems, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-030-36778-7_39

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