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Software robotsand machine learning
Petri KarjalainenTwitter: @PetriKarjLinkedin: www.linkedin.com/in/PetriKarjFacebook: www.facebook.com/PetriKarj
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Intelligent work 2030
“In the new workforce of 2030, the most successful organizations will optimizethe usage of all their resources, both human and machine, for competitiveadvantage. “An increasing portion of your workforce will not be human,” Mr.Prentice said. However, while machines are very good for consistency,performance, predictability, efficiency, and safety; they can’t match humans’skills in ingenuity, novelty, art, creativity, emotion, and to address variabilityand provide context.”
–Gartner Summary of the top news from Gartner Symposium/ITxpo 2015
http://www.gartner.com/smarterwithgartner/technology-and-business-in-2030/?cm_mmc=Eloqua-_-Email-_-LM_EVT%20EMEA%202016%20ESC27%20E10%20-%20Newsflash5%20%2816.11.15%29%20Non%20attendees-_-0000
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Youtube video here
February 25, 2015 | OpusCapita INTERNAL 3
OpusCapita Software Robots
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Megatrends shaping the future
Cloud, IoT and Mobile internet• Economic impact largely dependent
on establishing suitable ecosystemsas growth drivers
Automation of Knowledge Work• One of key disruptive technologies
within next 10 years• Supported by advanced machine
learning and artificial intelligence
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MobileInternet
Automation ofknowledge work
The Internet ofThings
Cloudtechnology
Advancedrobotics 1.7-4.5
1.7-6.2
2.7-6.2
5.2-6.7
3.7-10.8
Range of sized potential economic impacts
Low High Impact from other potentialapplications (not sized)X-Y
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Gartner: Postmodern ERP era is alreadyhere moving applications to Cloud
8 June, 2016 OpusCapita Internal 5
Factors driving Postmodern ERP era• B2B buyers: Buying behaviour is driving cloud services• SAP: moving to cloud reduces core ERP functionality allowing competitors to bid for cloud solutions• Market: By 2018, at least 30 percent of service-centric companies will move the majority of their
ERP applications to the cloud
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Automation of Knowlede work happensas we speak
8 June, 2016 OpusCapita Internal 6
Automation ofknowledge workhas started withRPA automatingclerical tasks
With statistical analysis andmachine learning the impact ofknowledge work automation will growto the next level
With components and technologiessuch as artificial intelligence,machine learning, natural userinterfaces andbig-data technologies
2014-2015
Economicimpact ofknowledgeworkautomation
2016 2-10 years
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OpusCapita Robots in the Cloud
Image source: http://www.brainstormmarketingproductions.com/
• Software robots are using a computer on behalf of aperson or with a person and they can:
• See and interpret text and pictures.• Move and click the mouse.• Write text and numbers.
• Work 24/7 without breaks.• Use several IT systems just like a human would use
them.• Implementation does not require changes to the
existing IT systems.• Are programmed with ”work shadowing” and defined
logic for exception handling.• Work according to preset rules.• Currently operate as rule based, in the future utilize
statistical analysis, machine learning and artificialintelligence.
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AnalyticsEngine
MachineLearning
Software Robots Architecture
Robots, Win Server 2008 R2Virtual Desktops
RDSMaintenance
Transactionlog
VirtualSupervisor
CustomerApplications
BACKENDengine
ROBOTICnetwork
CUSTOMERnetwork
SECUREconnection
InformationSecurity Policiesare based on ISO
27002requirements,
approved by OCSecurity
Committee.
Robots act asremote userswith own user
accountsRDS
Implementation
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0 €
20 000 €
40 000 €
60 000 €
80 000 €
100 000 €
120 000 €
140 000 €
160 000 €
180 000 €
200 000 €
0 1 2 3 4 5
Manualwork
year
RPA Setup
5 000 - 20 000 €per process
The setup time forinstalling RPA robot in
production environmentvaries between two and
six weeks.
RPA Operating
5 000 - 10 000 €per year
RPA production,monitoring,
licenses and IT.
Manual work
40 000 €per year
Typical FTE cost perannum.
How much savings does RPA offer?Cost of 1 FTE mnual work vs. robotized process
RPA
Typical payback time is less than six months
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> 95 % automation
Old manualprocess: 10-20
minutes
Complex rules
Trigger
List of new employee relationships is sent fromcustomer’s ERP system to customer’s payroll
system nightly.
Automated Activities
Robot fetches the list of new employeerelationships. It navigates in payroll application,
runs dozens of different validations and fixesobvious deviations. Some deviations are
reported to payroll specialists.
Case Example:Validating & Correcting Employee Data
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Other Case Examples in Payroll
Constant Salaries Update• Validating new and updated salaries
• Entering salaries and other monthly payments in payrollapplication
Employment Experience Calculation• Fetching relevant data about employee’s employment history
• Calculating employee’s work experience and updating relevantdata in payroll application
Sick Notes Handling• Ensuring that working hour data is in line with sick note
• Entering sick leave information in payroll application• If required, filling in and sending an application to Kela or sick fund
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Youtube video here
February 25, 2015 | OpusCapita INTERNAL 12
OpusCapita Robots I
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Youtube video here
February 25, 2015 | OpusCapita INTERNAL 13
OpusCapita Robots II
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Macrorecorders– Simple, runs on one
computer– Requires refreshing– Not for complex
operations
Application macros– Automates a single
application, such asExcel
– Available for multipleusers in the organization
Evolution from macros toMachine Learning
RoboticProcessAutomation– Front-end level automation– Any set of applications– Centrally monitored
Machine Learning– Learns from what has been
done in past– Uses e.g. neural network to
build a solution model– Can predict a solution
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Machine Learning application areasinvestigated by OpusCapita• Sales forecasting
– Case: sales forecasting using weather data
• Anomality and fraud detection– OpusCapita is piloting this with Haaga-Helia to develop algorithms for payments deviations
• Cash flow forecasting– OpusCapita is planning to start piloting how to build cash flow forecasts using machile learning
• Purchase Invoice preposting automation– OpusCapita is having a proof of concept study for own invoices and with some selected
customers
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Case: Improve invoice posting quality andreduce lead time with Machine Learning• OpusCapita has tested that a Artificial Intelligence and Machine Learning
solution can prebook the invoice data: Inspector, Approver, Cost Center, G/LAccount
• The solution consists of three modules:1. Training the AI: The prefilled information is based on real accepted invoice data. This data is
used to build and improve the Machine Learning algorithm.2. Prediction by AI: When a new purchase invoice comes to the process, the Machine Learning
model prefills the invoice data to the posting view of OpusCapita Invoices. The probability of theprediction is shown as well.
3. Verification reporting: The change log of posting data is used to measure the quality of theMachine Learning algorithm (rate of no correction needed)
• The solution shorten the lead time, boost efficiency and improve quality
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Invoice preposting process with artificialintelligence and machine learning
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Invoiceread-in
Supplierdefinition +
OCI ruleengine
AI/MachineLearning
JSON call forAzure ML Webservice API with
XMLconversion
InvoiceWorkflow
Invoicedata
Logdata
PredictingAccount,
CostCenter,
Inspector,Approver
TrainingMachineLearningAlgorithm
Acceptedposting datais used totrain and
improve thealgorithm
VerificationReporting
Automationquality
measuredby
monitoringthe numberof human
correctionsneeded
Self learning loop
All thecustomer
invoices areread in to
OCI
Known rulesapplied andinvoices with
PurchaseOrders
separated.
Rest of theinvoices
assigned forArtificial
Intelligence
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Invoice workflow approval user interface withMachine Learning based preposting
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Invoice is sent straight forthe best known inspector
Account, Cost Center andApprover are prefilled withthe estimated probability
Inspector verifies thepredictions before
sending the invoice to theApprover
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Prediction accuracy verification
• The quality of the Machine Learning algorithm is tracked by analyzing the change loginformation: prefilled vs. approved invoice data
• % =
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7580
8590
85 8792
0102030405060708090
100
Intelligent invoicing quality (%)
week1 week2 week3 week4 week5 week6 week7
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What does machine learning look like ?
20
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Learninig machine learning:”Read the book and just start using”
Commercial platforms• Microsoft• Microsoft seems to have the best functionality
and also be the easiest to learnand best documented.
Open source platforms• Weka• Not so easy to start using but completely open source
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Summary
• RPA is mainstream and suitable for processes with simpleclearly defined rules
• Machine Learning is available for more complex decisionsand it works already today
• Processes where Machine Learning could be used areeasy to identify with these three rules:1. Humans are processing the data with complex rules, too complex for programmer to encode2. Dataset exists as a result of human processing so that machine learning can be used to build a
model3. Users should still review the data that machinelearning has prefilled but processing time and
quality improves drasticly