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    <article-meta>
      <title-group>
        <article-title>The Proactive Insights Engine: Process Mining meets Machine Learning and Artificial Intelligence</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <string-name>Fabian Veit</string-name>
          <email>f.veit@celonis.com</email>
          <xref ref-type="aff" rid="aff0">0</xref>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Jerome Geyer-Klingeberg</string-name>
          <xref ref-type="aff" rid="aff0">0</xref>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Julian Madrzak</string-name>
          <email>j.madrzak@celonis.com</email>
          <xref ref-type="aff" rid="aff0">0</xref>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Manuel Haug</string-name>
          <email>m.haug@celonis.com</email>
          <xref ref-type="aff" rid="aff0">0</xref>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Jan Thomson</string-name>
          <email>j.thomson@celonis.com</email>
          <xref ref-type="aff" rid="aff0">0</xref>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
        <aff id="aff0">
          <label>0</label>
          <institution>Celonis SE</institution>
          ,
          <addr-line>Munich</addr-line>
          ,
          <country country="DE">Germany</country>
        </aff>
        <aff id="aff1">
          <label>1</label>
          <institution>From Process Discovery to Process Intelligence</institution>
        </aff>
      </contrib-group>
      <abstract>
        <p>This demo presents the features of the Proactive Insights (PI) engine, which uses machine learning and artificial intelligence capabilities to automatically identify weaknesses in business processes, to reveal their root causes, and to give intelligent advice on how to improve process inefficiencies. We demonstrate the four PI elements covering Conformance, Machine Learning, Social, and Companion. The new insights are especially valuable for process managers and academics interested in BPM and process mining.</p>
      </abstract>
      <kwd-group>
        <kwd>process mining</kwd>
        <kwd>process intelligence</kwd>
        <kwd>machine learning</kwd>
        <kwd>artificial intelligence</kwd>
      </kwd-group>
    </article-meta>
  </front>
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    <sec id="sec-1">
      <title>-</title>
      <p>PI consists of the following four components:</p>
      <p>PI Conformance compares the actual ‘as-is’ process with the documented ‘to-be’
process. It automatically identifies the highest priority issues and their root causes,
which allows users to take immediate action. Therefore, PI Conformance extends the
many existing applications for conformance checking, as it automatically reveals a list
of process violations, drills them down to their root causes, and makes intelligent
suggestions for how to fix them. The software develops these recommendations based on
the process data, adapts and continuously improves these recommendations as more
data is being processed.</p>
      <p>PI Machine Learning integrates advanced statistical analyses and machine learning
algorithms natively into Celonis. The application fully supports R-scripting language.
This allows the user to run advanced prediction techniques directly in Celonis. Historic
process data and the findings of process discovery serve as an input to create predictions
of the future. For example, users can proactively monitor process performance by
evaluating how ongoing cases will flow through the process until their completion</p>
      <p>PI Social adds the social aspect of processes to Celonis. PI Social maps process data
to different teams and organizations to show how they interact with each other. It
identifies critical roles within the process, workload imbalances, and other team
inefficiencies. The visualization of the network of social process interactions uncovers issues in
organizational structures and the interactions among people involved in the process.</p>
      <p>PI Companion integrates Celonis into business management systems. It acts as a
‘process advisor’ and identifies recommendations at the time when critical business
decisions are made. This allows process analysis while the process is being executed,
rather than analyzing processes after their completion. The new add-on interacts with
SAP systems and supports decisions by using relevant data from historical transactions.
For example, users can check which vendor had the fastest delivery record in the last
month or analyze customers’ payment behavior for well-grounded decisions on
payment terms.
2</p>
    </sec>
    <sec id="sec-2">
      <title>Case study</title>
      <p>For the case study, we apply the new PI features on a demo data set for the
Purchaseto-Pay (P2P) process. The demo data covers 279,000 purchase order items.</p>
      <p>After loading the predesigned to-be process model, the conformance checker scans
the actual process, shows conformance history, key statistics about conformance, and a
list of violations sorted by their frequency. Figure 1 illustrates that 57% of the cases are
compliant with the target process model. PI detects 15 process violations. From the
KPIs shown in the middle of Figure 1, we can see that the average throughput time of
non-compliant cases is 31.1, which is higher than the 29 days that compliant cases need
to go through the process. Further user-specific KPIs can be added using the integrated
formula editor. Moreover, the list at the bottom of Figure 1 reveals that in 14% of the
cases, the price of a purchase order is changed, which is a frequent source of manual
rework. In 7% of the cases, the process starts with the scan of the invoice and not with
the creation of a purchase order item/requisition item as defined in the uploaded ‘to-be’
process model. This process deviation is often caused by maverick buying violating
corporate compliance. Acceptable violations can be added to a whitelist.</p>
      <p>More information about each violation can be retrieved by clicking on the items in
the list. Figure 2 refers to a violation, where the procurement process starts with the
scan of the invoice. PI automatically displays possible root causes for this violation.
For example, in 6,000 cases when the process starts with the scan of an invoice, the
vendors Unisono AG, IDES Consumer Products, and six other vendors are involved in
the transaction.</p>
      <p>PI Machine Learning enables users to execute R-statements and to access R-libraries
in Celonis. R-statements can be executed on the process data, for example, to calculate
the 95% quantile of the throughput time for each vendor. Results are displayed in
Celonis and can be re-used for filtering the process data or as input for further analyses.</p>
      <p>In the social overview in Figure 3, information about the people working in the
process and their collaboration is displayed. By clicking on a specific user, PI Social shows
several performance measures, e.g., the number events per day for the Team 2 (35
users) is 226 and their throughput time is 718.8 hours.</p>
      <p>PI Companion embeds Celonis in the front end of the business application, e.g., in
the SAP Business Client as shown in Figure 4. This enables users to include insights
from process mining i nto their daily operational business. While working in SAP, PI
intelligently creates selections based on the input made by the SAP user and shows
realtime insights into the process data. For instance, when purchasing a new material, users
can immediately evaluate the delivery performance of the vendors in the side panel to
choose the best performing vendor.</p>
    </sec>
    <sec id="sec-3">
      <title>Maturity, screencast, and demo license</title>
      <p>
        PI is available in Celonis Process Mining since December 2016. The new
functionalities have already been applied by hundreds of users, including Fortune 1000 leaders
like Siemens, SAP, or Vodafone. Customers and industry experts confirm the power of
Celonis PI as the next generation of process mining:
• Prof. Wil van der Aalst (TU Eindhoven). https://youtu.be/prynsvWJMng
• Romana Engler (Siemens AG). https://youtu.be/HQqHRJfNcag
• Bastian Nominacher (Celonis SE). https://youtu.be/wwh58wObNJo
A full screencast demonstrating Celonis and the new PI features is available as
download [
        <xref ref-type="bibr" rid="ref4">4</xref>
        ]. A free demo license including PI features is offered for academic users
via the Celonis Academic Cloud [
        <xref ref-type="bibr" rid="ref5">5</xref>
        ].
      </p>
    </sec>
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