{"id":2104,"date":"2021-05-24T09:37:00","date_gmt":"2021-05-24T09:37:00","guid":{"rendered":"https:\/\/processminery.com\/?p=2104"},"modified":"2025-06-12T09:55:27","modified_gmt":"2025-06-12T09:55:27","slug":"predict-ap-invoice-payment-day-with-machine-learning-2","status":"publish","type":"post","link":"https:\/\/processminery.com\/index.php\/predict-ap-invoice-payment-day-with-machine-learning-2\/","title":{"rendered":"Predicting your invoice process with Machine Learning and Process Mining!"},"content":{"rendered":"\t\t
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With process mining alone you already gain great insight in your Accounts Receivable (AR) invoice process. All based on the data in your IT systems, like Oracle ERP Cloud. Machine learning adds the capability to predict the future. It adds the remaining activities for an open invoice, including the expected remaining duration until the invoice is closed.<\/p>

\"\"<\/p>

Together with data scientist Jaap van Bruchem<\/a>\u00a0we have built an analysis model that\u00a0predicts the complete process of invoices<\/strong>. The analysis is not only showing the actual process flow but it also predicts the remaining process activities and the duration of these activities.<\/figure>

\u00a0<\/p><\/div>

The data for closed and open invoices is extracted from IT-systems, in this case Oracle ERP. Based on this data set we have built a\u00a0predictive model that forecasts what invoice activities will take place and what the duration of these activities will be<\/strong>. The outcome of the model is analyzed in the #Celonis Intelligent Business Cloud (IBC) process mining application. This results in a graphical representation that shows the as-is process combined with the predicted (remaining) process activities and duration.<\/p>

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<\/figure>
A dashboard with actual information combined with the predicted data helps you to understand your process and gives you great insight in future process steps and duration.<\/figure><\/div>

With these new insights you are able to see upfront:<\/p>

\u00a0<\/p>

  1. which activities are predicted to take place or skipped for open AR invoices<\/li>
  2. which invoices are predicted to cause\u00a0rework, changes or other undesired activities<\/strong><\/li>
  3. which invoices are predicted to\u00a0end in a collections process<\/strong>\u00a0and need more attention<\/li>
  4. which customer profiles need to be updated to match a better collection strategy<\/li>
  5. what will the predicted\u00a0remaining duration<\/strong>\u00a0be until an individual invoice is closed and the total duration for all invoices<\/li>
  6. what the\u00a0underlying general characteristics<\/strong>\u00a0are of the invoices that do (not) follow the correct process flow<\/li><\/ol>

    \u00a0<\/p>

    Based on our client needs we are working on applying the model and ideas onto other processes and use cases. For more information or demonstration do not hesitate to contact us.<\/p>

    \u00a0<\/p>

    We predict that you will be amazed about the outcome!<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

    With process mining alone you already gain great insight in your Accounts Receivable (AR) invoice process. All based on the data in your IT systems, like Oracle ERP Cloud. Machine learning adds the capability to predict the future.<\/p>\n","protected":false},"author":5,"featured_media":2198,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[95,63,90,89],"tags":[49,59,53],"class_list":["post-2104","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-accounts-receivable","category-machine-learning","category-oracle-ebs","category-oracle-fusion","tag-celonis","tag-machine-learning","tag-oracle"],"_links":{"self":[{"href":"https:\/\/processminery.com\/index.php\/wp-json\/wp\/v2\/posts\/2104","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/processminery.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/processminery.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/processminery.com\/index.php\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/processminery.com\/index.php\/wp-json\/wp\/v2\/comments?post=2104"}],"version-history":[{"count":19,"href":"https:\/\/processminery.com\/index.php\/wp-json\/wp\/v2\/posts\/2104\/revisions"}],"predecessor-version":[{"id":3762,"href":"https:\/\/processminery.com\/index.php\/wp-json\/wp\/v2\/posts\/2104\/revisions\/3762"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/processminery.com\/index.php\/wp-json\/wp\/v2\/media\/2198"}],"wp:attachment":[{"href":"https:\/\/processminery.com\/index.php\/wp-json\/wp\/v2\/media?parent=2104"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/processminery.com\/index.php\/wp-json\/wp\/v2\/categories?post=2104"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/processminery.com\/index.php\/wp-json\/wp\/v2\/tags?post=2104"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}