Predictive Process Monitoring for Lead-to-Contract Process Optimization

Name
Madhu Tipirishetty
Abstract
Business processes today are supported by enterprise systems such as Enter-
prise Resource Planning systems. These systems store large amounts of process execution
log data that can be used to improve business processes across the organization. The
process mining methods have been developed to analyze such logs, which are capable of
extracting process models. These methods, in turn, have been applied in conjunctions
with predictive monitoring methods for early differentiation of desired and undesired
outcomes. Although predictive monitoring approach has recently caught attention and
found application in recommendation engines, which suggest cases to improve business
process outcomes, there is no much research on how contextual data, such as clients fi-
nancial indicators and other external data, may improve the quality of recommendations.
This thesis examines whether including the external data with the event data affects the
accuracy of predictive monitoring for early predictions positively. More specifically, this
thesis reveals usage of context data had the adverse effect on the performance of learned
models. Furthermore, the study indicated that the usage of first three events from the
event logs with internal data is sufficient to predict the label of an opportunity in the
sales funnel.
Graduation Thesis language
English
Graduation Thesis type
Master - Software Engineering
Supervisor(s)
Peep K√ľngas
Defence year
2016
 
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