Machine Learning Get Ready to Measure the Value for Supply Chain Management

Ute Riemann () and Thomas Ochs
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Ute Riemann: BT&A EMEA, SAP
Thomas Ochs: CIO Villeroy & Boch

Chapter Chapter 7 in Building Cloud Software Products, 2025, pp 111-128 from Springer

Abstract: Abstract Machine learning (ML) isn’t a solitary endeavour of IT but is having a dramatic impact on the way we can perform business processes. It is one of the quickest expanding areas within the area of artificial intelligence (AI) (Jordan & Mitchell, Science, 349(6245), 255–260, 2015) justified by the high productivity growth promised by these technologies, coupled with the explosive increase of data amounts and the growing availability of low-cost computing power and data storage required to use ML (Court et al., Big data, analytics, and the future of marketing & sales. McKinsey & Company Marketing & Sales Paper, March 2015; Jordan & Mitchell, Science, 349(6245), 255–260, 2015; Wess, Mit Künstlicher Intelligenz immer die richtigen Entscheidungen treffen. In P. Buxmann & H. Schmidt (Eds.), Künstliche Intelligenz: Mit Algorithmen zum wirtschaftlichen Erfolg (pp. 143–159). Springer, 2019). ML is so important because it helps using data to a greater value to drive business rule and logic. Already in the 1950s and 1960s the terms AI, ML, pattern recognition and game playing were used in connection with intelligent computers. Methods such as neural networks were also already well known (Minsky, Proceedings of the IRE, 49(1), 8–30, 1961; 20; Samuel, IBM Journal of Research and Development, 3(3), 210–229, 1959). Due to the rapid progress of technology at lower cost, this interest is renewed as big data can now be processed (Jordan & Mitchell, Science, 349(6245), 255–260, 2015; Wess, Mit Künstlicher Intelligenz immer die richtigen Entscheidungen treffen. In P. Buxmann & H. Schmidt (Eds.), Künstliche Intelligenz: Mit Algorithmen zum wirtschaftlichen Erfolg (pp. 143–159). Springer, 2019) and algorithms concerning neural networks and deep learning evolved (Streibich & Zeller, Offene Plattformen als Erfolgsfaktoren für Künstliche Intelligenz. In P. Buxmann & H. Schmidt (Eds.), Künstliche Intelligenz: Mit Algorithmen zum wirtschaftlichen Erfolg (pp. 107–117). Springer, 2019). The initial value of machine learning is that it allows you to continually learn from data and predict the future. With the emergence of digital tools and communications and thus the increase of data volumes, the benefit of ML further enhances—this is of relevance as well in the production and product development where intelligent production machines and smart products constantly produce relevant data (Buxmann & Schmidt, Künstliche Intelligenz: Mit Algorithmen zum wirtschaftlichen Erfolg. Springer Gabler, 2019a; Cheatham et al., McKinsey on Risk - Transforming Risk Efficiency and Effectiveness, 7, 27–34, 2019; Fink, Quick Guide KI-Projekte – einfach machen: Künstliche Intelligenz in Service, Marketing und Sales erfolgreich einführen, Springer Fachmedien Wiesbaden, 2020, S. VI, Mainzer, Künstliche Intelligenz—Wann übernehmen die Maschinen? Springer, 2016; Leukert et al., Das intelligente Unternehmen: Maschinelles Lernen mit SAP zielgerichtet einsetzen. In P. Buxmann & H. Schmidt (Eds.), Künstliche Intelligenz: Mit Algorithmen zum wirtschaftlichen Erfolg (pp. 41–62). Springer, 2019). From the business perspective it is not just the matter of generating huge quantities of data, but also of converting this data into new knowledge and gaining new insights into business processes, which in turn can lead to better decisions (Mainzer, Künstliche Intelligenz—Wann übernehmen die Maschinen? Springer, 2016; Streibich & Zeller, Offene Plattformen als Erfolgsfaktoren für Künstliche Intelligenz. In P. Buxmann & H. Schmidt (Eds.), Künstliche Intelligenz: Mit Algorithmen zum wirtschaftlichen Erfolg (pp. 107–117). Springer, 2019) and led to fundamental changes due to technological innovations (Leukert et al., Das intelligente Unternehmen: Maschinelles Lernen mit SAP zielgerichtet einsetzen. In P. Buxmann & H. Schmidt (Eds.), Künstliche Intelligenz: Mit Algorithmen zum wirtschaftlichen Erfolg (pp. 41–62). Springer, 2019; Preuss, In-Memory-Datenbank SAP HANA. Springer Fachmedien Wiesbaden, 2017; Wuest et al., Production and Manufacturing Research, 4(1), 23–45, 2016) leading to less repetitive and more innovative activities, e.g. within product management (Wellers et al., Why machine learning and why now? [White Paper]. SAP SE, 2017). Machine learning is dominating conversations about how emerging advanced analytics can provide businesses with a competitive advantage to the business. The following article now aims to answer the question what value these powerful set of algorithms and models can be delivered to the process of product management and how the value can be measured to justify the usage of ML technology. Coming from the end-to-end process view and the relevant KPIs, the following article outlines a methodology to quantify and measure the effects of ML while gain insights into patterns and anomalies within data and thus improving processes has.

Keywords: Machine learning; Supply chain; Application of machine learning; Business case; Key performance indicators; End-to-end processes; Value management (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-031-92184-1_7

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