This paper addresses overstock issues in a real-life purchase-to-pay process in cooperation with the industry leader in pet retail in Europe. It highlights the development of solutions for more efficient inventory man...
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ISBN:
(数字)9783031610035
ISBN:
(纸本)9783031610028;9783031610035
This paper addresses overstock issues in a real-life purchase-to-pay process in cooperation with the industry leader in pet retail in Europe. It highlights the development of solutions for more efficient inventory management, thereby reducing overstock. Our approach involves identifying patterns leading to overstock and proposing specific improvement measures within the existing logistics systems. This includes technical modifications in the order suggestion and purchase order processes using Logomate and SAP systems. The research utilizes object-centric process mining techniques as a crucial tool to uncover these patterns, with a focus on the practical solutions derived for overstock reduction. The case study conducted with the PM2 methodology demonstrates potential benefits in optimizing inventory structure and suggests a path for future research in generalizing these findings across various sectors and automating overstock pattern detection.
Small and medium-sized enterprises increasingly adopt electronic invoices and digitized purchase-to-pay processes. A purchase-to-pay process begins with making a purchase order and ends with completing the payment pro...
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ISBN:
(纸本)9783030773854;9783030773847
Small and medium-sized enterprises increasingly adopt electronic invoices and digitized purchase-to-pay processes. A purchase-to-pay process begins with making a purchase order and ends with completing the payment process. Even when organizations adopt electronic invoices, knowledge work in such processes is characterized by assimilating information distributed over heterogeneous sources among different stages in the process. By integrating such information and enabling a shared understanding of stakeholders in such processes, ontologies and knowledge graphs can serve as an appropriate infrastructure for enabling knowledge services. However, no suitable ontology is available for current electronic invoices and digitized purchase-to-pay processes. Therefore, this paper presents P2P-O, a dedicated purchase-to-pay ontology developed in cooperation with industry domain experts. P2P-O enables organizations to create semantic invoices, which are invoices following linked data principles. The European Standard EN 16931-1:2017 for electronic invoices was the main non-ontological resource for developing P2P-O. The evaluation approach is threefold: (1) to follow ontology engineering best practices, we applied OOPS! (OntOlogy Pitfall Scanner!) and OntoDebug;(2) to evaluate competency questions, we constructed a purchase-to-pay knowledge graph with RML technologies and executed corresponding SPARQL queries;(3) to illustrate a P2P-O-based knowledge service and use case, we implemented an invoicing dashboard within a corporate memory system and thus enabled an entity-centric view on invoice data. Organizations can immediately start experimenting with P2P-O by generating semantic invoices with provided RML mappings.
Fraud is a widespread international problem for enterprises. Organizations increasingly use self-learning classifiers to detect fraud. Such classifiers need training data to successfully distinguish normal from fraudu...
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ISBN:
(数字)9783319394268
ISBN:
(纸本)9783319394268;9783319394251
Fraud is a widespread international problem for enterprises. Organizations increasingly use self-learning classifiers to detect fraud. Such classifiers need training data to successfully distinguish normal from fraudulent behavior. However, data containing authentic fraud scenarios is often not available for researchers. Therefore, we have implemented a data generation tool, which simulates fraudulent and non-fraudulent user behavior within the purchase-to-pay business process of an ERP system. We identified fraud scenarios from literature and implemented them as automated routines using SAP's programming language ABAP. The data generated can be used to train fraud detection classifiers as well as to benchmark existing ones.
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