PPI Repository

Expected Successful outcome Unit Percentage

\mathit{e}\mathit{SUP}\mathit{^{\times}}(g)

\mathit{\times} \in \{\mathit{sgl}, \mathit{sum}\}

Dimension:

Quality

Granularity:

Group of cases

Description

Expected percentage of outcome units associated with all activity instances of a case belonging to a group of cases that are successfully completed.

Potential use

Process where the outcome of activity instances, as well as whether they are successful or not, can be measured.

Assumptions

Maximizing the percentage of successful outcome units is desirable.

Desired Value:

MAXIMIZE

Required attributes

actinst
case
type
uns
unt

Equations

Formula for practitioners

$\frac{\sum _{c \in g} \mathit{SUP}(c)}{\mathit{C}(g)}$

$\mathit{SUP}(c)$ returns the successful outcome unit percentage of case $c$ .
$\mathit{C}(g)$ returns the number of cases in group $g$ .

Formal Definition

$\frac{\sum _{c \in g} \mathit{SUP}\mathit{^{\times}}(c)}{\mathit{C}(g)}$
The $\mathit{\times}$ indicates that the PPI can take multiple forms, in this case, $\mathit{\times} \in \{\mathit{sgl}, \mathit{sum}\}$ . If $\mathit{\times} = \mathit{sgl}$ , then the function considers single events of activity instances for outcome unit calculations, i.e., it uses $\mathit{SUP}\mathit{^{sgl}}$ ; if $\mathit{\times} = \mathit{sum}$ , then the function considers the sum of all events of activity instances for outcome unit calculations, i.e., it uses $\mathit{SUP}\mathit{^{sum}}$ .

Python implementation

This indicator is implemented in the process_performance_indicators Python library.

process_performance_indicators.indicators.quality.groups.expected_successful_outcome_unit_percentage

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