  As data-driven intelligent systems advance, the need for reliable and
transparent decision-making mechanisms has become increasingly important.
Therefore, it is essential to integrate uncertainty quantification and model
explainability approaches to foster trustworthy business and operational
process analytics. This study explores how model uncertainty can be effectively
communicated in global and local post-hoc explanation approaches, such as
Partial Dependence Plots (PDP) and Individual Conditional Expectation (ICE)
plots. In addition, this study examines appropriate visualization analytics
approaches to facilitate such methodological integration. By combining these
two research directions, decision-makers can not only justify the plausibility
of explanation-driven actionable insights but also validate their reliability.
Finally, the study includes expert interviews to assess the suitability of the
proposed approach and designed interface for a real-world predictive process
monitoring problem in the manufacturing domain.
