{"id":72712,"date":"2024-02-19T11:21:38","date_gmt":"2024-02-19T10:21:38","guid":{"rendered":"https:\/\/www.efcom.de\/the-use-of-predictive-analytics-for-supply-chain-finance-part-1\/"},"modified":"2024-03-13T14:49:37","modified_gmt":"2024-03-13T13:49:37","slug":"predictive-analytics-scf-part-1","status":"publish","type":"post","link":"https:\/\/www.efcom.de\/en\/predictive-analytics-scf-part-1\/","title":{"rendered":"The use of predictive analytics for supply chain finance, part 1"},"content":{"rendered":"<div class=\"wpb-content-wrapper\"><p>[vc_row css=&#8221;.vc_custom_1708334309580{margin-top: 45px !important;}&#8221;][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;3\/5&#8243;]<div id=\"ultimate-heading-75969f5eeb3ddd41\" class=\"uvc-heading ult-adjust-bottom-margin ultimate-heading-75969f5eeb3ddd41 uvc-9780 \" data-hspacer=\"no_spacer\"  data-halign=\"center\" style=\"text-align:center\"><div class=\"uvc-heading-spacer no_spacer\" style=\"top\"><\/div><div class=\"uvc-main-heading ult-responsive\"  data-ultimate-target='.uvc-heading.ultimate-heading-75969f5eeb3ddd41 h1'  data-responsive-json-new='{\"font-size\":\"desktop:55px;\",\"line-height\":\"desktop:65px;\"}' ><h1 style=\"font-family:&#039;Roboto Condensed&#039;;font-weight:700;\">The use of predictive analytics for supply chain finance, part 1<\/h1><\/div><\/div><div class=\"ult-content-box-container \" >\t\t<div class=\"ult-content-box\" style=\"box-shadow: px px px px none;-webkit-transition: all 700ms ease;-moz-transition: all 700ms ease;-ms-transition: all 700ms ease;-o-transition: all 700ms ease;transition: all 700ms ease;\"  data-hover_box_shadow=\"none\"    ><style type=\"text\/css\" data-type=\"the7_shortcodes-inline-css\">.dt-shortcode-soc-icons.soc-icons-299b36f54cba27cbb3ac5cc78e508a96 a {\n  margin-right: 10px;\n}\n.dt-shortcode-soc-icons a.soc-icons-299b36f54cba27cbb3ac5cc78e508a96 {\n  margin-right: 10px;\n}\n.dt-shortcode-soc-icons 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!important;}&#8221;]<span style=\"font-size: 15pt; line-height: 2.0;\">What if you could imagine the ideal SCF world? There would be stable material flows across all channels and borders. We would have a fully digitalised workflow at financing level without any loss of time or other efficiency. Decisive criteria with regard to ESG issues would be fully taken into account. Last but not least: SCF players have optimum planning security thanks to the use of AI-based predictive analysis tools. This ensures maximum resilience, even in the event of major upheavals on the global markets.<div class=\"gap\" style=\"line-height: 10px; height: 10px;\"><\/div><\/span><\/p>\n<p><span style=\"font-size: 15pt; line-height: 2.0;\">Back to reality. Global material flows are unstable and will remain so in the future. We are (unfortunately) still relatively far away from comprehensive digitalisation of the entire SCF and consideration of all ESG factors. What is already available to us today, however, are technologies that help us to analyse existing data streams and use them to make predictions about potential financing risks and disruptions, as well as possible opportunities. We are talking here about machine learning (ML).<\/span>[\/vc_column_text][\/vc_column][vc_column width=&#8221;1\/5&#8243;][\/vc_column][\/vc_row][vc_row][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;3\/5&#8243;][vc_column_text css=&#8221;.vc_custom_1708335205249{padding-top: 20px !important;padding-bottom: 10px !important;}&#8221;]<span style=\"font-size: 22pt; line-height: 2.0;\"><strong>1. The basis: data, data, data<\/strong><\/span><\/p>\n<p><span style=\"font-size: 15pt; line-height: 2.0;\">Supply chain relationships can be highly complex as they involve many different players on the supplier and customer side. According to an estimate by the Boston Consulting Group, more than 20 parties are usually involved in a typical trade finance transaction*. Accordingly, there is no shortage of data from all the related (sub-)networks: whether payment behaviour, demand trends, defaults, limit utilisation, etc. &#8211; the historical information is sometimes already available to SCF providers when it comes to long-term customer relationships. However, the availability of data does not automatically make it usable.<\/span><\/p>\n<p>*Boston Consulting Group 2017[\/vc_column_text][\/vc_column][vc_column width=&#8221;1\/5&#8243;][\/vc_column][\/vc_row][vc_row css=&#8221;.vc_custom_1708335425479{padding-top: 20px !important;padding-bottom: 20px !important;}&#8221;][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;3\/5&#8243;]<div class=\"ult-content-box-container \" >\t\t<div class=\"ult-content-box\" style=\"box-shadow: px px px px none;border-style:solid;border-color:#eb9000;padding:15px;-webkit-transition: all 700ms ease;-moz-transition: all 700ms ease;-ms-transition: all 700ms ease;-o-transition: all 700ms ease;transition: all 700ms ease;\"  data-hover_box_shadow=\"none\"     data-border_color=\"#eb9000\" >[vc_column_text]<span style=\"font-size: 15pt; line-height: 2.0;\"><strong>Excursus: of supercomputers and exaflops <\/strong><\/span><\/p>\n<p><span style=\"font-size: 13pt; line-height: 2.0;\">Enormous computing power is required to process huge amounts of data &#8211; especially for training within AI models. It is therefore not surprising that the global demand for powerful computing machines has increased exponentially since the AI boom. Countries around the world are currently investing huge sums of money in the expansion of supercomputers &#8211; knowing full well that access to enormous computing power will be one of the strategic advantages in the future**. To give just one example: China is planning to increase its computing power to a total of 300 exaflops by 2025, with one exaflop comprising the power of two million laptops working in parallel. However, cloud computing and collaborations also enable smaller companies to train and utilise AI models with the necessary computing power (see also &#8220;Critical factors&#8221;).<\/span><\/p>\n<p><span style=\"font-size: 11pt; line-height: 2.0;\">**Guido Appenzeller, Matt Bornstein, and Martin Casado,\u00a0<em>Navigating the High Cost of AI Compute<\/em>, Andreessen Horowitz, April 27, 2023,\u00a0<a href=\"https:\/\/a16z.com\/navigating-the-high-cost-of-ai-compute\">https:\/\/a16z.com\/navigating-the-high-cost-of-ai-compute<\/a><a href=\"https:\/\/www.zotero.org\/google-docs\/?lFtQt5\">.<\/a><\/span>[\/vc_column_text]\t\t<\/div><\/div>[\/vc_column][vc_column width=&#8221;1\/5&#8243;][\/vc_column][\/vc_row][vc_row css=&#8221;.vc_custom_1708336359587{padding-top: 30px !important;}&#8221;][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;3\/5&#8243;][vc_column_text css=&#8221;.vc_custom_1708336322564{padding-top: 20px !important;padding-bottom: 10px !important;}&#8221;]<span style=\"font-size: 22pt; line-height: 2.0;\"><strong>2. Agile systems for dynamic markets<\/strong><\/span><\/p>\n<p><span style=\"font-size: 15pt; line-height: 2.0;\">The next logical step would be to generate knowledge from the existing databases that can help us make decisions. The aim here is to train a data model in such a way that it is able to output relevant labels such as &#8220;low risk&#8221;, &#8220;medium risk&#8221; or &#8220;high risk&#8221; (supervised learning) with the help of certain parameters (analogous to the evaluation of a customer by an employee). In a further development<br \/>\nstage, the system continues to learn independently and attempts to find and categorise relationships between the data (reinforcement learning). It can access not only &#8220;internal&#8221; available data from the individual management systems (such as payment behaviour and financial reports, etc.), but also &#8220;external&#8221; data such as general market trends, data from partners and information sources as well as information from the web (articles, social media). The ultimate aim is to map the dynamics of the markets in an agile, self-learning system.<\/span>[\/vc_column_text][\/vc_column][vc_column width=&#8221;1\/5&#8243;][\/vc_column][\/vc_row][vc_row css=&#8221;.vc_custom_1708336706652{padding-top: 25px !important;padding-bottom: 20px !important;}&#8221;][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;3\/5&#8243;][vc_single_image image=&#8221;72715&#8243; img_size=&#8221;large&#8221; style=&#8221;vc_box_border&#8221;][vc_column_text]<em>Quelle:<\/em> Relevant advanced technologies for trade and supply chain finance, Whitepaper by Commerzbank and Fraunhofer IML, 2022[\/vc_column_text][\/vc_column][vc_column width=&#8221;1\/5&#8243;][\/vc_column][\/vc_row][vc_row][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;3\/5&#8243;][vc_column_text css=&#8221;.vc_custom_1710337774316{padding-top: 20px !important;padding-bottom: 10px !important;}&#8221;]<span style=\"font-size: 22pt; line-height: 2.0;\"><strong>3. I see what you don&#8217;t see!<\/strong><\/span><\/p>\n<p><span style=\"font-size: 15pt; line-height: 2.0;\">In this case, mapping means, in particular, being able to recognise patterns that would not be visible to humans and\/or previous methods. These patterns can then be interpreted and serve as the basis for further recommendations, warnings or decisions &#8211; for example, indications of possible risks that could arise from fraud. As already mentioned, the system&#8217;s ability to learn improves over time, leading to increasingly accurate results.<\/span><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-size: 15pt; line-height: 2.0;\">Click here to <a href=\"https:\/\/www.efcom.de\/en\/the-use-of-predictive-analytics-for-supply-chain-finance-part-2\/\">continue<\/a><\/span>[\/vc_column_text][\/vc_column][vc_column width=&#8221;1\/5&#8243;][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text][\/vc_column_text][\/vc_column][\/vc_row]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>[vc_row css=&#8221;.vc_custom_1708334309580{margin-top: 45px !important;}&#8221;][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;3\/5&#8243;][vc_single_image image=&#8221;72688&#8243; img_size=&#8221;large&#8221; alignment=&#8221;center&#8221; css=&#8221;.vc_custom_1708334702010{padding-top: 15px !important;}&#8221;][\/vc_column][vc_column width=&#8221;1\/5&#8243;][\/vc_column][\/vc_row][vc_row][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;3\/5&#8243;][vc_column_text css=&#8221;.vc_custom_1708333786238{padding-top: 20px !important;padding-bottom: 10px !important;}&#8221;]What if you could imagine the ideal SCF world? There would be stable material flows across all channels and borders. We would have a fully digitalised workflow at financing level without any loss of time or&hellip;<\/p>\n","protected":false},"author":5,"featured_media":72689,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"yasr_overall_rating":0,"yasr_post_is_review":"","yasr_auto_insert_disabled":"","yasr_review_type":"","footnotes":""},"categories":[145],"tags":[181,140,183,182],"class_list":["post-72712","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artikel-en","tag-efcom-en","tag-factoring-en","tag-future","tag-innovation-en","category-145","description-off"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>The use of predictive analytics for supply chain finance, part 1 - efcom gmbh | Proven and efficient standard software for factoring institutes<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.efcom.de\/en\/predictive-analytics-scf-part-1\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The use of predictive analytics for supply chain finance, part 1 - efcom gmbh | Proven and efficient standard software for factoring institutes\" \/>\n<meta property=\"og:description\" content=\"[vc_row css=&#8221;.vc_custom_1708334309580{margin-top: 45px !important;}&#8221;][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;3\/5&#8243;][vc_single_image image=&#8221;72688&#8243; img_size=&#8221;large&#8221; alignment=&#8221;center&#8221; css=&#8221;.vc_custom_1708334702010{padding-top: 15px !important;}&#8221;][\/vc_column][vc_column width=&#8221;1\/5&#8243;][\/vc_column][\/vc_row][vc_row][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;3\/5&#8243;][vc_column_text css=&#8221;.vc_custom_1708333786238{padding-top: 20px !important;padding-bottom: 10px !important;}&#8221;]What if you could imagine the ideal SCF world? 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We would have a fully digitalised workflow at financing level without any loss of time or&hellip;\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.efcom.de\/en\/predictive-analytics-scf-part-1\/\" \/>\n<meta property=\"og:site_name\" content=\"efcom gmbh | Proven and efficient standard software for factoring institutes\" \/>\n<meta property=\"article:published_time\" content=\"2024-02-19T10:21:38+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-03-13T13:49:37+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.efcom.de\/wp-content\/uploads\/2024\/02\/AdobeStock_575701260-scaled.jpeg\" \/>\n\t<meta property=\"og:image:width\" content=\"2560\" \/>\n\t<meta property=\"og:image:height\" content=\"1707\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"rasko.peric\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@EfcomG\" \/>\n<meta name=\"twitter:site\" content=\"@EfcomG\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"rasko.peric\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"6 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/www.efcom.de\/en\/predictive-analytics-scf-part-1\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.efcom.de\/en\/predictive-analytics-scf-part-1\/\"},\"author\":{\"name\":\"rasko.peric\",\"@id\":\"https:\/\/www.efcom.de\/en\/#\/schema\/person\/a4d840c4773991aff51a17dcfcbe509d\"},\"headline\":\"The use of predictive analytics for supply chain finance, part 1\",\"datePublished\":\"2024-02-19T10:21:38+00:00\",\"dateModified\":\"2024-03-13T13:49:37+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.efcom.de\/en\/predictive-analytics-scf-part-1\/\"},\"wordCount\":1297,\"image\":{\"@id\":\"https:\/\/www.efcom.de\/en\/predictive-analytics-scf-part-1\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.efcom.de\/wp-content\/uploads\/2024\/02\/AdobeStock_575701260-scaled.jpeg\",\"keywords\":[\"efcom\",\"factoring\",\"future\",\"innovation\"],\"articleSection\":[\"Article\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.efcom.de\/en\/predictive-analytics-scf-part-1\/\",\"url\":\"https:\/\/www.efcom.de\/en\/predictive-analytics-scf-part-1\/\",\"name\":\"The use of predictive analytics for supply chain finance, part 1 - 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