Explainable Predictive and Prescriptive Process Analytics of Customizable Business KPIs

1. Explainable Predictive and
Prescriptive Process
Analytics of Customizable
Business KPIS
Riccardo Galanti - IBM & University of Padua
Supervisor: Massimiliano de Leoni
Co-Supervisor: Luciano Gamberini

2. Process Mining
1
Data
ID … …
1 … …
2 … …
Logs
Mining Prep
Transform
Process Mining
Model
& KPIs
Insights
Analyze
Optimize
Monitor &
optimize
Act on
insights
Improve

3. Event log
2

4. Predictive Process Analytics
3
EVENT
LOG
PREDICTIVE
MODEL

5. Contribution
4
ALERT!
CUSTOMER
SATISFACTION
SOME MONTHS
LATER
PREDICTIVE
MODEL

6. Contribution
5
PROCESS-AWARE
RECOMMENDER
SYSTEMS
MONITORING
PREDICTIVE
ANALYTICS
PRESCRIPTIVE
ANALYTICS
1° objective: build an evidence-based recommender system
that maximizes a given reference KPI

7. Contribution
6
BLACK-BOX
MODEL
DELIVERING LATE!
A C B
A B C
WHY NOT
F?
2° objective: convince users to trust predictions and
recommendations provided (Explainable AI)

8. Contribution
7
ORDER 1
RECEIPT 1
ORDER 2
RECEIPT 2
3° objective: enable predictive analytics of object-centric
processes and exploit the information of the complex sub-
processes interaction to increase the prediction accuracy
OBJECT-CENTRIC
PROCESSES

9. Predictive Analytics of
Customizable Business KPIS

10. Starting point: The Prediction Problem
8
(R1)
ELABORATING
REQUEST
REQUEST
CREATED X
REQUESTED
MISSING
INFORMATION
FINALIZING
REQUEST
X
BACK-OFFICE
ADJUSTMENT
REQUESTED
(R2)
PARTIAL TRACE TO
VECTOR ENCODING
FUNCTION
INPUT FEATURE
VECTOR
OUTPUT
THE CASE
DURATION WILL BE
10 DAYS
THE CASE WILL
COST 30 EUROS
A BACK-OFFICE
ADJUSTMENT WILL
BE PERFORMED
PARTIAL
TRACE
PREDICTIVE
MODEL
ACTIVITY RESOURCE ELAPSED TIME
REQUEST
CREATED
R1 0
ELABORATING
REQUEST
R2 3 days

11. Generic KPI
9
What is a KPI (Key Performance Indicator)?
It is a measurable value that demonstrates how effectively
a company is achieving key business objectives
REMAINING TIME - LEADTIME
CUSTOMER SATISFACTION
CASE COST
RESOURCES INVOLVED 👨‍🔧
Order
Blocked
ACTIVITY OCCURRENCE

12. Training predictor
REQUEST
CREATED
TRACE 1
TRACE N
.
.
.
.
10
TRAINING SET
(67% COMPLETED
CASES)
ELABORATING
REQUEST
REQUESTED
MISSING
INFORMATION
REQUEST
CREATED
REQUEST
CREATED
ELABORATING
REQUEST
...
INPUT
PREDICTIVE
MODEL
TRAINED
PREDICTIVE
MODEL
OUTPUT
OUTPUT

13. Testing predictor
11
TEST SET (33%)
REQUEST
CREATED
TRACE 1
TRACE N
.
.
.
.
ELABORATING
REQUEST
REQUESTED
MISSING
INFORMATION
REQUEST
CREATED
REQUEST
CREATED
ELABORATING
REQUEST
...
INPUT TRAINED
PREDICTIVE
MODEL
OUTPUT
MODEL
ACCURACY
STATISTICS
+.1
-.3
+.5
EXPLANATIONS
CALCULATE PREDICTIVE ACCURACY AND EXPLANATIONS FOR COMPLETED CASES

14. Predictive models adopted
Recurrent Neural Network
Long Short Term Memory
Network (LSTM)
Special kind of
WEAK
LEARNER
WEAK
LEARNER
WEAK
LEARNER
The output is given by
combining all the weak
learners output together
12

15. Prefix Encoding
LSTM CATBOOST
Represented as Represented as
EVENT LOG EVENT LOG
13

16. Predictive quality comparison
14

17. Explainable Predictive Analytics

18. Approaches to generate explanations
SHAP
2 main approaches
POST-HOC INTRINSIC
INPUT
BLACK-BOX
MODEL
OUTPUT
MODEL
SMOKE = YES
SEX = F
AGE = 65
OUTPUT = 0.4
BASE VALUE = 0.1
SMOKE = YES
SEX = F
AGE = 65
OUTPUT = 0.4
BASE VALUE = 0.1
+.1
-.3
+.5
Explanation
1° result: Galanti, R., Coma-Puig, B., de Leoni, M., Carmona, J., Navarin, N., Explainable
Predictive Process Monitoring, the International Conference on Process Mining (ICPM 2020)
15

19. Our Explainable AI approach: Details
WILL BACK-OFFICE
ADJUSTMENT
BE PERFORMED?
ELABORATING
REQUEST
REQUEST
CREATED X
REQUESTED
MISSING
INFORMATION
FINALIZING
REQUEST
X
BACK-OFFICE
ADJUSTMENT
REQUESTED
TEST SET (33%)
REQUEST
CREATED
TRACE 1
TRACE N
.
.
.
.
ELABORATING
REQUEST
REQUESTED
MISSING
INFORMATION
REQUEST
CREATED
REQUEST
CREATED
ELABORATING
REQUEST
...
OFFLINE
PHASE
16

20. Our Explainable AI approach: Details
(R1)
WILL BACK-OFFICE
ADJUSTMENT
BE PERFORMED?
NO RESOURCE=R1
OUTPUT EXPLANATION
ENCODED
AVERAGE PREDICTION: 0.2
CURRENT PREDICTION: 0.1
PAYOUT: -0.1
SHAPLEY VALUES
ELABORATING
REQUEST
REQUEST
CREATED X
REQUESTED
MISSING
INFORMATION
FINALIZING
REQUEST
X
BACK-OFFICE
ADJUSTMENT
REQUESTED
ACTIVITY RESOURCE ROLE
0 -0.10 0
ACTIVITY RESOURCE ROLE
REQUEST
CREATED
R1 SUPPORT
FEATURE VECTOR
17

21. Our Explainable AI approach: Details
(R1)
WILL BACK-OFFICE
ADJUSTMENT
BE PERFORMED?
YES RESOURCE=R2
OUTPUT EXPLANATION
ENCODED
AVERAGE PREDICTION: 0.2
CURRENT PREDICTION: 0.55
PAYOUT: 0.35
SHAPLEY VALUES
ELABORATING
REQUEST
REQUEST
CREATED X
REQUESTED
MISSING
INFORMATION
FINALIZING
REQUEST
X
BACK-OFFICE
ADJUSTMENT
REQUESTED
ACTIVITY RESOURCE ROLE
0 -0.10 0
0 0.45 0
ACTIVITY RESOURCE ROLE
REQUEST
CREATED
R1 SUPPORT
ELABORATING
REQUEST
R2 SUPPORT
FEATURE VECTOR
(R2)
18

22. Our Explainable AI approach: Details
(R1)
WILL BACK-OFFICE
ADJUSTMENT
BE PERFORMED?
YES ACTIVITY=REQUESTED
MISSING INFORMATION
OUTPUT EXPLANATION
ENCODED
AVERAGE PREDICTION: 0.2
CURRENT PREDICTION: 0.95
PAYOUT: 0.75
SHAPLEY VALUES
ELABORATING
REQUEST
REQUEST
CREATED X
REQUESTED
MISSING
INFORMATION
FINALIZING
REQUEST
X
BACK-OFFICE
ADJUSTMENT
REQUESTED
ACTIVITY RESOURCE ROLE
0 -0.10 0
0 0.10 0
0.55 0 0
ACTIVITY RESOURCE ROLE
REQUEST
CREATED
R1 SUPPORT
ELABORATING
REQUEST
R2 SUPPORT
REQUESTED
MISSING
INFORMATION
R3 BACK-
OFFICE
FEATURE VECTOR
(R2)
(R3)
19

23. Our Explainable AI approach: Offline
OFFLINE
PHASE
TEST SET (33%)
REQUEST
CREATED
TRACE 1
TRACE N
.
.
.
.
ELABORATING
REQUEST
REQUESTED
MISSING
INFORMATION
REQUEST
CREATED
REQUEST
CREATED
ELABORATING
REQUEST
...
INPUT
TRAINED
PREDICTIVE
MODEL
80%
PREDICTIVE
ACCURACY
RESOURCE = R2
RESOURCE = R1
ACTIVITY = REQUESTED
MISSING INFORMATION
0 1
-1
GLOBAL AVERAGE
INFLUENCE
20

24. Our Explainable AI approach: Online
TRACE 1
TRACE N
.
.
.
ONLINE
PHASE
ELABORATING
REQUEST
REQUEST
CREATED
RUNNING CASES INPUT
PREDICTION:
10 DAYS
RESOURCE = R2
RESOURCE = R1
ACTIVITY = REQUESTED
MISSING INFORMATION
0 1
-1
TRAINED
PREDICTIVE
MODEL
LOCAL INFLUENCE
21

25. Integration into IBM Process Mining Suite
2° result: Galanti, R., de Leoni, M., Marazzi, A., Bottazzi, G., Delsante, M., Folli, A., Integration of
an Explainable Predictive Process Monitoring System into IBM Process Mining Suite,
the International Conference on Process Mining (ICPM 2021)
ANALYTICS
DASHBOARD
DATABASE
SERVER
ROUTER
REQUEST
DATA
VM
RESULT
DELIVERY
22

26. IBM Analytics dashboard – Global view
23

27. IBM Analytics dashboard – Local view
24

28. User Evaluation - Setting
25
Most of the tasks (questions) were related to prediction’s explanations
Question examples:
• What is the most frequent influencer affecting cost prediction?
• How much is the total cost prediction affected by a ”Network
Adjustment Requested”?
18 TASKS
20 PARTICIPANTS
10 WOMAN 10 MAN
15 BUSINESS
ANALYSTS
5 MASTER/PHD
STUDENTS
3° result: Galanti, R., de Leoni, M., Monaro, M., Navarin, N., Marazzi, A., Di Stasi, B., Maldera, S.,
An Explainable Decision Support System for Predictive Process Analytics, Engineering
Applications of Artificial Intelligence, vol. 120, p. 105904, 2023

29. User Evaluation - Task Accuracy/Difficulty
TASK ACCURACY: 89% (STD 11)
TASK DIFFICULTY
26
Explanations are generally comprehensible to correctly carry out
analysis’ tasks
Users have found most of the tasks reasonably easy

30. User Evaluation - PSSUQ
Post-Study System Usability Questionnaire (PSSUQ)
27
19-items questionnaire that assesses user satisfaction with system usability
No issues regarding the intelligibility of the explanations were found

31. User Evaluation - UEQ
28
User Experience Questionnaire (UEQ)
26-items testing user’s experience
Scores compared with benchmark data (20190 users from 452 studies
concerning different products (business software, web pages/shops, social
networks)
Attractiveness (overall impression of the product)
Perspicuity (is it easy to get familiar with the product?)
Efficiency (can users solve their tasks without unnecessary effort?)
Dependability (does the user feel in control of the interaction)
Stimulation (is it exciting and motivating to use the product?)
Novelty (is the product innovative and creative?)

32. User Evaluation - UEQ
29
Users were overall satisfied with the explainable predictive framework
10% BEST
PRODUCTS
10% BETTER
75% WORSE
25% BETTER
50% WORSE
50% BETTER
25% WORSE
25% WORST
PRODUCTS

33. Explainable Prescriptive Analytics

34. Process-Aware Recommender System
Predictive
Monitoring
Block EVENT LOG
(COMPLETED TRACES)
PREDICTIVE
MODEL
EXPECTED
KPI
Prescriptive
Block
PROCESS
MODEL
POSSIBLE NEXT ACTIVITIES TO
IMPROVE PROCESS EXECUTION
30
4° result: Padella, A., de Leoni, M., Dogan, O., Galanti, R., Explainable Process Prescriptive
Analytics, the International Conference on Process Mining (ICPM 2022).

35. Prescriptive Block - Transition System
31
Running trace
< a, b, c>
POSSIBLE NEXT
ACTIVITY
d
e
f
g
EVENT LOG
(COMPLETED TRACES)

36. Prescriptive Block – Predict Outcomes
32
Running trace
< a, b, c>
POSSIBLE NEXT
TRACES
< a, b, c, d >
< a, b, c, e >
< a, b, c, f >
< a, b, c, g >
POSSIBLE NEXT
TRACES
ASSOCIATED
EXPECTED KPI
< a, b, c, d > 161h 23min
< a, b, c, e > 803h 41min
< a, b, c, f > 398h 22min
< a, b, c, g > 874h 51min
The Prescriptive-Analytics block will suggest
to perform the activity “d” as next

37. Evaluating recommendations
33

38. Shap for explaining recommendations
34
Running trace
<a, b, c>
POSSIBLE NEXT
TRACES
< a, b, c, d >
< a, b, c, e >
< a, b, c, f >
< a, b, c, g >
Comparison of Shapley values following
or not a recommendation.
Shapley Values of <a, b, c>
Shapley Values of <a, b, c, d>
vs

39. Explaining recommendations
35
The fact that the Closure
Reason is “Client Recess”,
following our suggestion (i.e.
Requesting the Adjustment to
the Back-Office), modifies its
effect on KPI from 0.15 to -0.10
CLOSURE_REASON =
CLIENT RECESS
CLIENT_TYPE = 1
ACTIVITY = NETWORK
ADJUSTMENT
ACTIVITY =
REQUEST CREATED
How executing “Back-Office Request Adjustment”
changes the Shapley values that each feature assumes
Bank-Account Closure
KPI
Pending Liquidation Request

40. Explaining recommendations
36
The fact that the system is
Waiting for an assignment,
following our suggestion (i.e.
calling a customer) modifies its
effect on KPI from 110h to 70h
PRODUCT =
PROD424
ACTIVITY = WAIT
IMPLEMENTATION
ACTIVITY = WAITING
ASSIGNMENT
COUNTRY = PL
How executing “Call Customer”
changes the Shapley values that each feature assumes
VINST Volvo system
KPI
Total Execution Time

41. Object-centric Predictive Process
Analytics

42. Object-centric processes
37

43. Object-centric processes
38
MANUFACTURING
EVENT LOG

44. From Event Logs to Event Graphs
ID Time Req Order Invoice
e1 1 R1
e2 2 R2
e3 3 R3
e4 4 R1, R3 O1
e5 5 R2 O2
e6 6 R2 O3
e7 7 O1 I1
e8 8 O1 I2
e9 9 O2,O3 I3
e1
e2
e3
e4
e6
e7
e5
e9
e8
Graph
Instance
1
Graph
Instance
2
39

45. Building a predictor
INPUT
OUTPUT
e1
e2
e3
e4
e6
e7
e5
e9
e8
GRAPH
INSTANCE
1
.
.
.
.
GRAPH
INSTANCE
N
PREFIXES
e2 e6
e5
e2
e2 e5
Predictive
model
Trained
Predictive
model
40

46. Predictor based on Graph Neural Networks
ID Activity Time Req Order
Order
Price
Order
Country
e2 A 2 R2
e5 B 5 R2 O2 50 IT
e6 C 6 R2 O3 100 ES
e2 e6
e5
REPRESENTED
AS MATRIXES
ADJACENCY
MATRIX
NODE
MATRIX
EDGE
MATRIX
41

47. Aggregation attributes (LSTM and Catboost)
Activity Time Req Order
Order
Price
Order
Country
#
Req
#
Order
Avg
Order
Price
%
Country=
IT
%
Country=
ES
A 2 1 1 0 0 0 0
B 5 1 1 50 IT 1 1 50 1 0
C 6 1 1 100 ES 1 2 75 0.5 0.5
ID Activity Time Req Order
Order
Price
Order
Country
e2 A 2 R2
e5 B 5 R2 O2 50 IT
e6 C 6 R2 O3 100 ES
e2 e6
e5
REPRESENTED
AS
42

48. KPIS considered
All KPIS were considered between the first occurrence of the
considered object and the last occurrence of a selected target activity
ELAPSED TIME
PAY DELAY
OCCURRENCE OF ACTIVITY / ATTRIBUTE WITH PARTICULAR VALUE
Examples:
• from Requisition to the last Goods Line Registered
• from Order to the last Invoice Cleared
43

49. Experiments and Case Studies
44

50. Experiments and Case Studies
KPI GCN GNN LSTM Catboost
8 34.39 (41m) 45.62 (33m) 41.36 (56m 31.08 (5m)
9 35.33 (16m) 67.04 (27m) 40.96 (2h 15m) 36.71 (17m)
10 32.23 (5m) 75.21 (20m) 55.4 (49m 26.96 (3m)
11 31.52 (5m) 81.62 (14m) 48.97 (48m) 31.2 (6m)
23 4.31 (50m) 1.93 (2h 39m) 2.49 (1h 49m) 1.06 (4m)
24 18.37 (20m) 19.14 (1h 28m) 16.59 (1h 8m) 17.32 (3m)
GCN can occasionally have slightly better
performances in the presence of the limited amount of data
45

51. Experiments and Case Studies
No clear winner between Graph
Neural Networks and LSTM
46

52. Experiments and Case Studies
KPI PERCENTAGE GNN GCN LSTM Catboost
25 27% 0.33 (4h 45m) 0.37 (9h 12m) 0.52 (9h) 0.60 (20m)
26 26% 0.38 (3h 35m) 0.50 (13h) 0.66 (12h 39m) 0.74 (20m)
27 61% 0.73 (1h 6m) 0.75 (5h 38m) 0.82 (14h) 0.82 (19m)
28 60% 0.76 (1d) 0.78 (1d 2h) 0.76 (18h 50m) 0.83 (42m)
29 20% 0.34 (1d 9h) 0.47 (1d 4h) 0.50 (17h 29m) 0.60 (41m)
30 3% 0.06 (4d) 0.12 (1d 12h) 0.28 (18h 27m) 0.63 (1h 17m)
When the KPI is related to the (non) occurrence of a process’ activity that
is seldom observed, we observed that Catboost and LSTM networks
significantly surpass graph-based neural networks
47

53. Explanations of Object-centric predictions
% ORDER_PLANNED_DELIVERY_MONTH=7 > 0.18
% ORDER_PLANNED_DELIVERY_MONTH=7 < 0.05
0.26 < % ORDER_PURCH_GROUP=100_L50 < 0.66
-20 -10 +10 0 +10 +20
ORDER, % GOODS_LINE_REGISTERED > 0.97
% ORDER_PLANNED_DELIVERY_MONTH=7 < 0.02
% ORDER_PURCH_ORG=100_LTH > 0.49
INVOICE, % INVOICE CLARED > 0.70
% ORDER_PURCH_ORG=100_L50 > 0.66
0.26 < % ORDER_PURCH_ORG=100_L50 < 0.66
318 < # ORDERS < 966
PAY_DUE_MONTH_ADJ = 3
% ORDER_PURCH_ORG=100_205 > 0.49
PAY_DUE_MONTH_ADJ = 5
48
5° result: Galanti, R., de Leoni, M., Navarin, N., Marazzi, A., Object-centric Process Predictive
Analytics, Expert Systems with Applications, vol. 213, p. 119173, 2023
6° result: Galanti, R., de Leoni, M., Predictive Analytics for Object-centric Processes: Do Graph
Neural Networks Really Help?, Business Process Management (BPM), 2023

54. Conclusions & Future Works

55. Conclusions
• Increased attention towards data-driven predictive process monitoring
in order to detect potential deviations from the expected process
behaviour
• According to the state of the art, Long Short-Term Memory networks
generally outperform other methods
• The experiments conducted on 6 datasets and 17 different KPIs
highlighted that Catboost not only can be trained in a shorter amount of
time, but it can also generally outperform LSTM models
• Prescriptive Analytics has often been overlooked, assuming that the
users, after being alerted of a potential failure, are able to autonomously
find the proper corrective actions
• We proposed a PAR system able to recommend the best actions that
could be performed for improving a generic, user-customizable KPI
49

56. Conclusions
• Explainable AI techniques have been overlooked, assuming that a good
level of prediction’s accuracy is sufficient for the process’ stakeholders
to trust the recommender system (as well as the prediction system)
• We proposed an explainable predictive-monitoring framework based on
the Shapley Values game theory approach, and we validated it against
several publicly-available datasets
• In order to demonstrate the practical application of the research
conducted, we integrated our explainable predictive framework inside a
commercial software, IBM Process Mining
• We conducted a user evaluation, which confirmed that the proposed
explainable framework was considered intelligible by the users
• We extended our proposed PAR system with explainable capabilities
50

57. Conclusions
• The large share of research in predictive analytics focused on predicting
the outcome of process instances that run in isolation
• We proposed an approach to enable predictive analytics in object-
centric processes
• We enriched our predictive framework by exploiting the information of
the complex processes interaction
• The experiments conducted on three real object-centric processes and
30 different KPIs showed that the technique based on gradient boosting
generally has the highest accuracy and lowest training time
• We leveraged our explainable framework to confirm the importance of
the features that encode the objects interaction in order to increase the
predictive accuracy
51

58. Future works
• Evaluate alternative explainable approaches that have been emerging
• Investigate a clear quality criteria for Explainable AI techniques to
objectively verify the validity of the explanations against it
• In object-centric processes, test different encoding functions for LSTM
and graph-based neural networks
• Integrate Recommender system inside IBM Process Mining and assess
what would be an optimal user experience to increase its adoption
• Validate if the provided recommendations would be understandable
and easy to use for the end users
52

59. Thank you for your attention!