DATA-DRIVEN ANALYSIS OF SALES PROCESSES DYNAMICS IN THE ORDER-TOCASH CYCLE USING SAP SIGNAVIO: OPERATIONAL INSIGHTS FOR SMES

Tanase Andreea Gabriela, Grigorescu (stanescu) Andreea, Rapan Ivona, Cretu Raluca-Florentina and Banta Viorel Costin
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Tanase Andreea Gabriela: BUCHAREST UNIVERSITY OF ECONOMIC STUDIES
Grigorescu (stanescu) Andreea: ROMANIAN ACADEMY, SCHOOL OF ADVANCED STUDIES OF THE ROMANIAN ACADEMY, DOCTORAL SCHOOL OF ECONOMIC SCIENCES, NATIONAL INSTITUTE FOR ECONOMIC RESEARCH COSTIN C KIRITESCU, INSTITUTE OF NATIONAL ECONOMY, BUCHAREST, ROMANIA
Rapan Ivona: INSTITUTE OF NATIONAL ECONOMY, ROMANIAN ACADEMY
Cretu Raluca-Florentina: BUCHAREST UNIVERSITY OF ECONOMIC STUDIES
Banta Viorel Costin: BUCHAREST UNIVERSITY OF ECONOMIC STUDIES

Annals - Economy Series, 2025, vol. 6, 493-498

Abstract: This paper examines the dynamics of integrated sales processes within a business process (Order-to-cash (O2C) flow), using the SAP SPI (SAP Signavio Process Intelligence) platform as an advanced operational performance analysis tool. The study explores how data extracted from the analyzed business process and correlated using process mining techniques can avoid bottlenecks, variations, and critical inefficiencies in the sales activities of small and medium-sized enterprises. The analysis aims to identify recurring behavioral patterns at the operational and commercial level, as well as to assess their impact on processing times, documentation accuracy, and customer satisfaction. For effective analysis, raw data sets were extracted directly from the ERP system, respecting the actual structure of the O2C operational flow. The collected information was then processed and modeled using advanced data transformation and cleaning tools to accurately reflect the monitored operational activities. After processing, the data was converted into the standardized format required by SAP SPI, ensuring full compatibility with the process mining engine. The resulting files were uploaded to the SAP SPI platform, where they were automatically correlated with the reference model of the analyzed process. This processing flow - from extraction from ERP to integration into SAP SPI - enabled a coherent and analytical view of process performance, providing a solid basis for rigorous interpretations and applicable recommendations. Based on the results generated by SAP SPI, the paper formulates a series of empirically grounded recommendations for optimizing the flows related to the O2C economic process.

Keywords: O2C; ERP – Enterprise Resource Planning; SAP; Industry 4.0.; AI – artificial intelligence; Process mining. (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:cbu:jrnlec:y:2025:v:6:p:493-498

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