Traditionally, we design processes without any specific variations due to key user’s personality traits. We optimize process activities using LEAN and other techniques to reduce waste and increase efficiency. We all focus on a defined (successful) outcome for our customer needs. However, we had an opportunity within some of the recent projects to see and measure the influence of PersonalityTraits (typically from employees and customers) on optimization results. Customer Behaviour Prediction Analyses was main focus in optimization of cross and up-sell procedures in a Bank. Based on more than 40 variables and thousands of business rules we predictCustomer Behaviour (for every individual customer and its contract) and “on–line” optimize process activities to achieve the best results for Bank. Eye-tracking as a UX technics was used in HealthCare project to increase the quality and reliability of doctor’s decisions, measuring the time to perform the activity and corresponding proportion of incorrect or incomplete decisions. Based on the findings, personalized styles of UX components were proposed in key activities. Anxieties have enormous influence on employee behaviour, usually resulting in strong defences and looking for opportunities to protect their positions. During the step-by-step process reengineering (inUtility Management Company) employee performance deviations (average, expected) were followed and corresponding level of automation was incorporated into processes at each step. With increasing competition to retain the market position companies can achieve next level of providing services only with the respect to Personality Traits of Both, the Employees and Customers.

1. ANDREJ GUŠTIN
Do we all react in the same way?
Influence of people's personality traits on process optimization

2. Andrej Guštin is a cofounder and CEO at CREApro, a
leading Slovenian consulting company focused
comprehensively on business process management
and innovation.
Vice president of IIBA CHAPTER SLOVENIA since 2009

3. Overlooked information(eye tracking)
Case I.

4. Case background - story
• In 2009, a young boy died in a hospital, due to a (potentially) operational mistake.
• It was assumed, that the doctors overlooked some critical indicators in a Blood Lab Test
(BLT) and did not react promptly.
• Processes in hospitals were digitalized with deployment of EHR (electronic healthcare
record) and HIS (hospital information system) some years ago and it seemed that GUI and
UX might also be part of the operational risk.

5. Diagnostic process – From need to value
• Need: how to read the document and get the information 100% correct.
• Stakeholder: doctor, patient.
• Context: dynamic and stressful working environment in the emergency
department at hospital clinics.
• Change: design is important for humans.
• Solution: improved user experience with better graphical design.
• Value: decrease the average time needed to extract the information from the
document and increase the reliability of human activities.

6. Blood Test Results– EHR and Paper copy example
Original paper based BTRDigital presentaton of BTR

7. Why we used Eye tracking?
• How we really see things?
• Do we see them equaly?
• What are the natural patterns of reading?
• How can we take those facts into consideration ?

8. The experiment
• In the first (top) scenario information was
presented with a tabular view (like on the BLT),
• In the second (bottom) scenario we redesigned
the appearance to a more graphical, judicious
view.
• All test users got the same „problem
description“ and performed the same
procedure.
• During the test they were isolated, not to
communicate with each other.
• 24 people were included in the experimental
workflow.

9. The results
• Gaze plots shows a significant difference in both cases.

10. The results – average time and distribution curve
30s
Source: https://books.google.si/books/about/Uporaba_interaktivne_ve%C4%8Dpredstavnosti_v.html?id=zM4GmwEACAAJ&redir_esc=y

11. Customer behavior(predictive analytics)
Case II.

12. Case background – the story
• Since economic crises in 2008, Slovenian banks
have been deeply involved in the collection
process due to the increased quantity and
volume of overdue outstanding receivables.
• Operational efficiency optimization led them to
decrease the number of employees, so
collectors were overloaded with tasks and
documents.
Growth of non-performing loans
Decline in the number of employees

13. Recovery process – From need to value
• Need: how to optimize collection process and increase the volume and amount of
collected payments.
• Stakeholder: back-office, customer service, call center, clerk, middle management
• Context: economic situation, as described
• Change: from human to machine decision making.
• Solution: predictive model (R) for probability calculations. Selectively targeting the
right debtors with the right collection strategies at the right time was proposed by the
Solution and integrated processes.
• Value: optimal allocation of resources to maximize the amount collected while
minimizing collection costs.

14. Predictive Model Development
15
Model
Algorithems
Cursors
Rules
Historical data Machine learning Result
New data for processing The calculation of probability for
delayed payment
Result
Model
DevelopmentDailyusage
What is the probability, that this Customer will
be late with this payment? Probability!

15. Behaviour cursors for predictions
Some cursors, used in the model:
x2: The amount of the credit approved
x9: The total amount of remaining part of the credit
x10: The number of days from credit approval
x11: The number of days to payment maturity
x13: were the delayed receivables in the previous year paid
x14: The date of the first delay
x15: The amount of the first delay
x16: Late payments in the past year
x19: The maximum number of days of delay in payments in previous year
Main decision tree and key cursors with their weights

16. Results – graphical presentation
The graphs below present a distribution of 2 cursors from 192 observed cases.
The left graph presents the result of the predicted model. Black dots are payments that
won‘t be paid.
The middle graph presents the same sample after the invoices were actually paid (or late).
The right graph presents the difference.
The model incorrectly predicted 3 cases out of 192, that is 1.5%.
This is much better than the collectors can do, even knowing their customers well.

17. ## Confusion Matrix and Statistics
##
## Reference
## Prediction default no-default
## default 9 1
## no-default 2 180
##
## Accuracy : 0.984
## 95% CI : (0.955, 0.997)
## No Information Rate : 0.943
## P-Value [Acc > NIR] : 0.0041
##
##
## 'Positive' Class : default
##
98,4%
Behaviour prediction index
18
Results – statistics

18. How we see the results?
• We used survival curve to present
the results.
• We improve the calculation of the
profitability of the client
(controlling profitability per
customer).
• Cost calculation of collection and
recovery proceedings (against
potentially recovered value).
• Assessment of future debt
servicing capabilities.
• The calculation of the probability of
default of existing and new assets.
90 days
 9% in number

19. Personal fear to change (process monitoring)
Case III.

20. Case background - story
• Back in 2010, a utility management service company started a process-reengineering
project with the main goal to increase efficiency and reorganize back-office services
as part of digital transformation.
• The head of the back-office was also a managing director and partner in the
company.
• After some successful pilot processes optimization, we redefined their main core
process.

21. Billing process – From need to value
• Need: increase efficiency and refocus on customer.
• Stakeholder: back-office, accounting and finance department, IT, customer
service, call center, middle management, senior management
• Context: economic situation, digital transformation, internal change of
workplaces and job positions.
• Change: 100% automatization of core process, focus on customer service.
• Solution: deployment of BPMS solution with tight integration with ERP and DMS.
• Value: reorganization of the work, customer centric approach.
Source: http://www.dlib.si/stream/URN:NBN:SI:doc-CPFDANEE/23ff0ac4-1c72-4398-a037-833bdff2c573/PDF
http://dsi2011.dsi-konferenca.si/upload/predstavitve/Mened%C5%BEment%20poslovnih%20procesov/Gustin_Andrej.pdf
http://dsi2010.dsi-konferenca.si/upload/predstavitve/mened%C5%BEment%20poslovnih%20procesov/gustin_andrej_upravljanje%20poslovnih%20procesov%20kot%20odgovor%20na%20sedanjo%20krizo.pdf

22. The Change Curve (developed by Elisabeth Kubler-Ross)
Source:https://www.linkedin.com/pulse/change-curve-tim-crocker

23. Process 1: processing of incoming document
• The first steps in process
optimization went smoothly.
• In a time period of four months
(1) we were nearly halfway to
achieving our goal.
• Normal deviation in the declining
time trend (moment at A) – some
ideas doesn‘t work .
• Prompt reaction and process
change led to expected results
(2).
• Size of the bubble presents the number of
documents
1
2
Real BPMS data from 2010-2013

24. Process 2: processing of contracts and invoices
• A small process change resulted in a
high deviation in employees’
performance (moment “C”).
• The primary cause of this was
employees’ anxieties of losing internal
business “power.”
• Top management and HR started an
internal campaign and promotion for a
retraining program.
• Step-by-step automation that finally led
to a nearly complete computer-
automated process (a final level of 98%
automatization).
• Size of the bubble presents the number of
documents
Real BPMS data from 2012-2015
Re-check
Re-work
Exceptions
Irrational
Incorrect
Senseless

25. Conclusions
Who will build the highest stone stack?