Seeing the Forest for the Trees:
Group-Oriented Workforce Analytics
Jing Yang1, Chun Ouyang1, Arthur H.M. ter Hofstede1,
Wil M.P. van der Aalst2,1, and Michael Leyer3,1
1Queensland University of Technology, Brisbane, Australia
2RWTH Aachen University, Aachen, Germany
3University of Rostock, Germany
Abstract. Workforce analytics brings data-driven methods to organi-
zations for deriving insights from employee-related data and supports
decision making. However, it faces an open challenge of lacking the capa-
bility to analyze the behavior of employee groups in order to understand
organizational performance. This paper proposes a novel notion of work
proﬁles of resource groups, informed by the management literature, for
characterizing resource group behavior from multiple aspects relevant to
workforce performance. This notion is central to the design of a new,
systematic approach that supports resource group analysis by exploiting
business process execution data. The approach also provides managers
and business analysts with an intuitive means of group-oriented resource
analysis by applying visual analytics. We demonstrate the applicability
of the approach and usefulness of the proposed notion of resource group
work proﬁles using real datasets from ﬁve Dutch municipalities.
Keywords: Workforce analytics ·resource groups ·process mining ·
event logs ·visual analytics
Achieving excellent business process performance within the management of op-
erations is a demanding and crucial challenge for any organization to maintain
competitive advantage. The prevalence of information systems has led to many
data science applications supporting analyses of organizational performance to
address this challenge. Business processes are often at the core of such analyses as
they describe how resources of an organization (employees, machines, systems)
are connected with each other . The focus of business process analytics is often
on the control-ﬂow perspective and process design. However, the aspect of how
employees work together in processes to achieve eﬃciency is also important .
Employees are the key resources of an organization. Not only their indi-
vidual but also collective performance as diﬀerent units or teams has a direct
impact on the outcomes delivered by the organization . Data science appli-
cations in this regard are termed workforce analytics, which aim at extracting
insights from analyzing employee-related data and thus support evidence-based
2 J. Yang et al.
decisions on human resources . The success of Google’s Project Oxygen and
other leading-edge enterprises illustrates the value of workforce analytics as an
important organizational capability to improve resource planning and perfor-
mance evaluation. However, as workforce analytics receives growing attention,
several challenges have been identiﬁed regarding its current practice . One of
these challenges concerns the absence of group-level analysis pivotal to strategy
execution and organizational eﬀectiveness. For example, current workforce ana-
lytics has not yet enabled consistent comparisons across internal groups within
Our research aims to explore a possible solution to improving organizations’
capability to conduct group-oriented workforce analytics by systematically ex-
ploiting business process execution data. The motivation is two-fold. First, busi-
ness processes often cut across functional boundaries in an organization and col-
lectively involve employees from diﬀerent functional units to deliver outcomes
to customers . The end-to-end nature of processes makes it viable to analyze
and compare diﬀerent resource groups by linking their performance with process
outcomes. Second, data recording actual process execution is readily available in
many organizations in the form of event logs. With time-stamped information
on process instances (e.g., an insurance claim), process activities, and relevant
organizational groups (e.g., a group of claim processors), event logs can serve as
a valuable and objective data source complementary to survey data commonly
used by current workforce analytics in practice .
Process mining is the ﬁeld that studies data-driven process analytics using
event logs. With regard to human resources in organizations, the state-of-the-art
literature focuses on analyzing individual resources or discovering the formation
of resource groups. Studying human resources at the group-level to extract in-
sights on how resources work in groups and how resource groups perform in busi-
ness processes is underexplored. This leads to the following research question for
workforce analytics in process-related digitalization: how to utilize process ex-
ecution data for analyzing the behavior of resource groups working in business
In this paper, we propose a novel notion of work proﬁle of resource groups,
drawing on relevant studies in the management literature. It comprises an ex-
tensible set of quantitative measures for characterizing resource group behav-
ior from multiple aspects, including workload, performance, goal achievement,
participation, distribution, and collaboration. Based on this notion, we develop
an approach to identify and analyze resource groups’ work proﬁles using event
log data. The approach provides managers and business analysts with an in-
tuitive means of group-oriented resource analysis by applying visual analytics.
We demonstrate the applicability of the implemented approach and usefulness
of the proposed notion of resource group work proﬁles by analyzing real event
logs from ﬁve Dutch municipalities.
Our research contributes to addressing the gap of resource-group level anal-
ysis in business process management research on a conceptual and also method-
ological level. From a practical perspective, our research provides the possibility
Seeing the Forest for the Trees: Group-Oriented Workforce Analytics 3
of strengthening an organization’s process-oriented capability in terms of coor-
dinating groups to increase eﬃciency.
The paper is organized as follows. Sect. 2 reviews existing literature related to
resource group analysis. Sect. 3 proposes the notion of work proﬁles of resource
groups. Sect. 4 presents the design of an approach for identifying and analyzing
work proﬁles. Sect. 5 discusses results and ﬁndings from evaluating the approach
over real-life datasets. Sect. 6 concludes the paper and outlines future work.
2 Resource Group Analysis: Theory and Related Work
The organization of employees in terms of teams or groups and the comparison
of their collective performance constitute important topics in management .
A team is formed by engaging individuals in collective work with joint eﬀort,
whereas a group only represents individuals tied together by certain criteria,
not necessarily working jointly . For example, there can be employees from
a function-oriented group who work together with those from other function-
oriented groups in a team performing a particular process, but having limited
interaction with their own group members. In this work, we focus on groups
rather than teams.
Groups of employees can be characterized by the interaction between group
members. This is addressed by the interactionist theory of behavior, which states
that the interaction between individuals in a group determines the performance
of the group . Malinowski et al.  provide a comprehensive overview of
the challenges regarding decision support to identify inﬂuencing factors and the
related concepts. Next to a person-job ﬁt and a person-vocation ﬁt, individu-
als interact with their group members, and thus a person-group ﬁt has to be
ensured. Whether all group members have an adequate person-group ﬁt can be
determined from their interaction and performance . Hence, there are two
levels of workforce analysis — group performance and interaction within a group,
i.e., the way a group is organized internally.
Within management research, much work has focused on deﬁning general
practices while neglecting individual interactionist ﬁts in a group context .
An example of such a general practice would be the grouping of employees around
the processes they are involved in rather than around the types of tasks they per-
form. Other general practices state that high performance groups should have,
e.g., clearly deﬁned goals, aligned values, and adequate collaboration . In par-
ticular, the collaboration aspect remains opaque in such work. The problem with
such practices is that they are based on generic assumptions and may not be
the best for a speciﬁc organization or parts of it. Research in the ﬁeld of psy-
chology includes individual diﬀerences, perceived psychological states regarding
various dimensions and aspects, e.g., group cohesion . However, such psycho-
logical aspects are often subjectively measured through questioning, conducted
sporadically. Hence, these aspects are not considered in this article as focus is
on objective measures using process data. Moreover, organizations nowadays are
required to be ﬂexible as they are faced with dynamic and ongoing changes of
4 J. Yang et al.
the environment . Organizational structures need to reﬂect this by being able
to evaluate groups on an ongoing basis — providing data on group comparisons.
Group performance is typically described from a measurement perspective
without specifying how to gather and analyze data. Brignall and Ballantine 
review diﬀerent performance management models and point out that the utiliza-
tion of human resources is an essential aspect. The literature reviews conducted
by Haynes  and Bortoluzzi  discuss the measurement of productivity in or-
ganizations and identify certain productivity indicators, e.g., working hours, time
to completion, and amount of satisfactory outcomes vs. errors. Gibson et al. 
review the existing measures of group eﬀectiveness in the literature and conduct
interviews in several multinational organizations, summarizing ﬁve dimensions
for measuring the “outcome eﬀectiveness” of groups. Charlwood  reports the
results from a literature review that identiﬁes theory and evidence on the use
of Human-Capital metrics by organizations. The review extracts more than 600
Human-Capital metrics from the literature describing workforce characteristics
and the evaluation of workforce eﬃciency.
With regard to the internal organization of the group, ﬁrst there are ap-
proaches which consider the interaction between groups referring to handovers
in processes , role descriptions and expertise , or communication and con-
trol structures . Second, approaches using business process execution data to
study human resource groups can be categorized into two topics. One concerns
using event logs for analyzing the formation of resource groups, e.g., Sch¨onig
et al.  propose an approach that uncovers the composition rules of human
resource groups in process executions, and Appice  proposes a method that
reveals the construction and destruction of organizational groups over time us-
ing event logs. The other topic concerns the discovery of organizational groups
(e.g., ), which aims at extracting the grouping structures around resources.
Third, there exists research (e.g., [17,25]) focusing on analysis of individual re-
source behavior by building resource “proﬁles” from event logs, which repre-
sent objective descriptions of how individual resources were involved in process
execution. However, it still remains an open question as to what and how to
characterize the behavior of resource groups working in processes.
3 Work Proﬁle of Resource Groups
Drawing on the theoretical and conceptual background in the prior section, this
section presents the notion of work proﬁle of resource groups. A work proﬁle of
a resource group can be deﬁned as a collection of indicators used to measure
diﬀerent aspects of that group of resources in terms of their interaction with
relevant work. As with any indicators related to performance, the measurement
of indicators includes a connection to time, i.e. a time interval (between t1and
t2) in which the respective performance of a group is measured . By specifying
the relevant interval, work proﬁles can reﬂect the fact that the performance of
resource groups is often dynamic due to having shifts and turnover. Hence, the
deﬁnition of a work proﬁle is as follows:
Seeing the Forest for the Trees: Group-Oriented Workforce Analytics 5
Deﬁnition 1 (Work Proﬁle of a Resource Group). Let RG be a set of
resource group identiﬁers, Tthe universe of timestamps, and [t1, t2)a half-open
time interval with t1, t2∈ T and t1< t2. Let Ibe a set of names for possible
indicators. Given a resource group rg ∈RG, WP = (rg, t1, t2,I, σ )is a work
proﬁle for the resource group during time period [t1, t2)where σ:I → Rspeciﬁes
the quantiﬁed measures of the indicators.
The deﬁnition provides a general representation of indicators measuring dif-
ferent aspects of a resource group over a speciﬁc time-frame. By reviewing the
management literature, we identiﬁed a number of relevant studies [4,5,6,10,13]
which can inform the proposal of a resource group’s work proﬁle useful for work-
force analytics. The indicators refer to the input-throughput-output view on
processes . Performance regarding input-output can be measured with indica-
tors related to productivity and eﬃciency. Whether a speciﬁc output is achieved
is referred to as goal achievement. Finally, the throughput is reﬂected by the
summation of employee workload in a group. As a result, we present a collection
of three general aspects and associated indicators, focusing on a resource group
in its entirety.
•Workload : What and how much work is a resource group involved in?
-allocation – overall amount of work allocated to the group
-assignment – amount of the group’s workload assigned to speciﬁc work
-relative focus – % of the group’s workload assigned to speciﬁc work
-relative stake – amount of contribution by the group to speciﬁc work
•Performance [4,6,10,13]: How does a group perform?
-amount-related productivity – amount of work completed by the group
-time-related productivity – time needed by the group to complete work
-eﬃciency – amount of satisfactory work produced by the group
•Goal achievement [4,10]: To what extent does a group adhere to goals?
-eﬀectiveness – % of established goals accomplished by the group
In this research, we also consider how resource groups interact with work
in terms of their involvement in business process execution captured by process
event logs. This theoretical focus is reﬂected in the following aspects and in-
dicators, which measure how group members interact with relevant work in a
process, and with each other.
•Participation [4,6]: How do group members commit to work?
-attendance – number or % of members in the group committing to work
•Distribution : How is work distributed over group members?
-member load – amount of work allocated to individual group members
-member assignments – amount of members’ workload allocated to speciﬁc
•Collaboration : How is the collaboration among group members?
-cooperation – extent of collaboration between group members
6 J. Yang et al.
The above collection of six aspects and associated indicators can be used
to form the structure (or template) of a group’s work proﬁle for group-oriented
analysis. Note that the term “work” here refers to either activities (tasks) or cases
of a business process. Analysts can build their own sets of indicators according
to diﬀerent problems and contexts. However, to enumerate a comprehensive,
universal set of aspects and indicators would be unrealistic  and is beyond
this paper’s scope.
4 Identifying and Analyzing Work Proﬁles
We introduce the design of an approach for identifying and analyzing work pro-
ﬁles of resource groups using process event log data. Fig. 1 depicts an overview
of the proposed approach consisting of two main phases.
4.1 Identifying Work Proﬁles
Identify resource groups. The starting point is an event log. As a minimum
requirement, the input event log should provide information on cases (instances
of process execution), activities, resources, and time. These are satisﬁed by many
event logs as they often record case identiﬁers, activity labels, resource identiﬁers,
and timestamps as the basic event attributes. Additionally, the input event log
may also carry an attribute indicating the group identities of resources.
Given such an event log, the ﬁrst task is to identify diﬀerent resource groups.
This is straightforward when a group identity attribute is present in the log.
Otherwise, organizational group discovery (e.g., ) can be applied to extract
group identities of resources. In either situation, one can determine the number
of resource groups, their members, and thus the associated event data in the log.
Map events onto multiple dimensions. Event logs contain complex and multi-
dimensional data capturing the information on various perspectives of process
execution. Consider an example of an insurance claim process: a manager (or-
ganizational dimension) is in charge of the ﬁnal review of a claim (activity di-
mension), and several groups are formed to work on diﬀerent weekdays (time
Map events onto
Group-level analysis Within-Group analysis
Fig. 1: Overview of the approach to identifying and analyzing work proﬁles
Seeing the Forest for the Trees: Group-Oriented Workforce Analytics 7
dimension) to serve customers lodging diﬀerent types of claims (case dimension).
Moreover, from the perspective of resource groups, members in the same group
are likely to share common characteristics, e.g., all managers conduct reviews,
despite that they may be specialized in handling certain types of claims.
To study the work behavior of resource groups in process execution, we or-
ganize events into classes of events based on diﬀerent dimensions (such as case,
activity, time dimensions). Depending on the purpose of an analysis and available
information in the input log, analysts can specify diﬀerent case types, activity
types, and time types. For example, to compare the performance of employee
groups on diﬀerent weekdays, seven time types may be deﬁned (e.g., “Monday”,
“Tuesday”). Consequently, (“car insurance claims”, “contact”, “Friday”) refers
to all events on Fridays concerning the work behavior of employees when they
contacted customers that had lodged car insurance claims.
Based on the classiﬁcation of events according to various process dimensions,
indicators of work proﬁles can be calculated respectively. It therefore enables
more targeted analyses on the work behavior of groups. For instance, given case
type “gold customer” and activity type “contact”, (“CityS.”, 2020-09-27, 2020-
10-25, attendance, 60%) indicates to HR analysts that 60% of members in em-
ployee group “CityS.” worked on contacting gold customers between September
and October 2020.
Extracting work proﬁles. We describe the pre-deﬁned work proﬁle indicators
(Section 3) that can be directly extracted given a typical event log with essential
information recorded. Note that all indicators are measured given a resource
group and a time interval (see Deﬁnition 1).
Workload: The indicators of group workload capture the amount of diﬀerent
types of work carried out by a resource group. With respect to an event log, the
amount of work can be quantiﬁed by considering either the number of activities
(which can be inferred from the event number) or the number of cases (which
can be inferred from the case identiﬁers).
–allocation is measured by the total number of activities conducted by a group, or
the total number of cases involving the group;
–assignment is measured by the number of activities conducted by a group that are
speciﬁc to some case type, activity type, and time type, or the number of cases
involving a group that are speciﬁc to some case type;
–rel focus measures the assignment of speciﬁc activities or cases to a group, com-
pared with the total allocation to the group;
–rel stake measures the assignment of speciﬁc activities or cases to a group, compared
with the total number of activities or cases of the speciﬁc types.
Performance: The indicators of group performance can be quantiﬁed by con-
sidering activities and cases completed in a given time interval. Note that they
are diﬀerent from the workload indicators which do not consider completion.
–amount-related productivity is measured by the total number of completed activities
or completed cases by a group;
8 J. Yang et al.
–time-related productivity is measured by the average time taken by a group to
complete an activity or a case;
–eﬃciency extends amount-related productivity by including some normative pre-
deﬁned criteria. For example, an analyst can specify that only cases completed
within 10 days are considered “satisfactory”, and therefore eﬃciency will be cal-
culated based on the number of satisfactory cases by the group only.
Goal achievement: The eﬀectiveness indicator measuring the goal achievement
of a resource group can be quantiﬁed based on other aspects and their indicators.
For example, when two goals are established in terms of the maximum amount
of allocation (measuring workload) and the minimum level of eﬃciency (measur-
ing performance), the eﬀectiveness of a group can be measured by considering
whether the group accomplishes these goals, respectively.
Participation: The indicator attendance can be quantiﬁed by considering the
occurrences of group members carrying out activities or cases. Note that the
measure may only be a rough estimate since an event log may not accurately
capture the time when employees start working on a process.
–attendance is measured by the number of member resources in a group who origi-
nated at least one event (for a relevant activity or case).
Distribution: The indicators for distribution are deﬁned over group members
by calculating the portion of workload of the group. Thus, the following indica-
tors consider a given resource in a group.
–member load is measured by the number of activities conducted by a resource.
Therefore, the sum of member load across all members of a group should be equal
to the allocation of the group (measured by activities);
–member assignment is measured similarly to member load, but using case types,
activity types, and time types to characterize the work by diﬀerent dimensions.
Collaboration: Quantifying the extent of collaboration among employees using
event logs can be challenging since (1) event logs usually do not capture the
communication between employees and (2) the way how collaboration happens
in diﬀerent processes and organizations may diﬀer. In the following, we discuss a
possible estimate of cooperation based on how frequently group members transfer
work between each other in process execution (known as handovers).
–cooperation within group members can be estimated by the density of handovers
of work between group members .
Seeing the Forest for the Trees: Group-Oriented Workforce Analytics 9
4.2 Analyzing Work Proﬁles
Building on work proﬁles extracted from event logs, diﬀerent data analytics tech-
niques can be applied to discover patterns from the measurement of indicators.
In our approach, we discuss the use of visual analytics as an intuitive and proven
means  for analyzing work proﬁles.
Following the deﬁnition of work proﬁles and the relevant aspects and indica-
tors, we consider the following requirements for visually analyzing work proﬁles:
–Users should be able to interactively extract work proﬁles related to diﬀer-
ent time intervals in an event log and at diﬀerent granularity (e.g., daily,
monthly), thus be able to track changes of work proﬁles over time;
–Users should be able to have an integrated view of interrelated indicators
(e.g., allocation and assignments) to derive ﬁndings on interactions between
diﬀerent aspects (or dimensions);
–Users should be able to compare indicators measured among diﬀerent groups
at diﬀerent times; and
–Users should be able to correlate indicators of group-level analysis with those
of within-group analysis to obtain a holistic view on groups’ work behavior.
Based on these requirements and guided by the general principles in visual
analytics , we developed a design composed of several types of charts com-
bined with interactive ﬁlters. The design aims at providing an integrated and
purposeful visualization on multiple aspects of a resource group’s work proﬁles.
The design includes the following. (1) A stacked area chart and a line chart
are chosen for analyzing workload and performance, considering their advantages
in capturing indicator values as time-series and showing the evolution patterns.
For these two charts, interactive ﬁlters are embedded to allow users to explore
the workload and performance indicators at diﬀerent times and at diﬀerent levels
of granularity. (2) A heatmap is used for supporting the analysis on workload
and distribution with regard to diﬀerent case, activity, and time types, for its
usefulness in simultaneously presenting values related to two-dimensional data
attributes. (3) A stacked bar chart is used for intuitively presenting the atten-
dance of group members with respect to group size. By connecting diﬀerent
charts using the same set of interactive ﬁlters, users are provided with an overall
picture of work proﬁles of resource groups in a selected time interval of interest.
The design shows a possible way of applying visual analytics to analyze work
proﬁles. While the aspects and indicators of a work proﬁle may be further ex-
tended, other visualization techniques can also be adopted accordingly.
The purpose of our evaluation is to demonstrate how the proposed approach
can be used for resource group-oriented analysis. To this end, we have developed
a prototype with interactive visualization, built upon Vega-Lite , as a real-
ization of the design of the approach in Sect. 4. The tool is publicly available
(https://royjy.me/to/gwp-demo). Fig. 2 and Fig. 3 illustrate the prototype’s
interactive visualization interface.
10 J. Yang et al.
Fig. 2: Annotated screenshots of the prototype’s interactive interface for analyz-
ing work proﬁles regarding workload, participation and distribution. The num-
bers mark diﬀerent views: (1) workload by allocation; (2) workload by assignment
measuring either activities or cases; (3) workload by rel focus measuring either
activities or cases; (4) distribution by member assignment; (5) participation by
attendance. The views respond to user interactions simultaneously: (A) selecting
a time interval and zoom-in; (B) highlighting speciﬁc groups; (C) focusing on a
speciﬁc time period (week); and (D) showing speciﬁc numbers via a tooltip
5.1 Design of Experiments
We conducted an evaluation by experimenting on a real-life dataset4with ﬁve
event logs. The event logs record a process of handling building permit applica-
tions in an approximate four-year period, and contain typical event attributes
satisfying the minimum requirements on an input event log (Sect. 4.1). Note
that the event logs only record the end timestamp for each activity conducted
4BPIC 2015: https://data.4tu.nl/collections/BPI_Challenge_2015/5065424/1
Seeing the Forest for the Trees: Group-Oriented Workforce Analytics 11
Fig. 3: Annotated screenshots of the prototype’s interface for analyzing work
proﬁles regarding performance. Views of (6) amount-related productivity and (7)
time-related productivity respond simultaneously to user interactions (A–D)
in the process. Therefore, only activity occurrences can be considered in the
subsequent analysis, not the activity duration time.
Still, this dataset can serve as a representative example of how our approach
can contribute to workforce analytics centered around resource groups. This is
because the dataset captures how an identical process was performed in ﬁve
diﬀerent municipalities, and thus representing scenarios where diﬀerent resource
groups participate in executing the same process. Moreover, the process owners
raised a few questions originally, with a particular focus on the diﬀerences be-
tween the municipalities’ performance and the roles of their employees. Given
this context, we consider each municipality as a separate resource group in our
experiments5, and apply the approach to extract and analyze their work proﬁles.
5.2 Group-level Analysis
We ﬁrst conduct the group-level analysis and focus on the workload and per-
formance aspects, motivated by one of the process owner’s original questions:
Where are diﬀerences in throughput times between the municipalities and how
can these be explained? For simplicity, we refer to the ﬁve municipalities (i.e., the
resource groups) by short names, e.g., “muni-1” denotes the ﬁrst municipality.
Workload analysis. We organize cases and events by three process dimensions
(activity, time, case) to compare the workload of resource groups. Fig. 4 shows
the visualization of group workload in regard to diﬀerent activity, time, and case
types. The ﬁve groups exhibit very similar patterns in terms of assigning their
group workload according to diﬀerent types of activities (Fig. 4a). Slight diﬀer-
ences can be observed as neither of muni-4 nor muni-5 has worked on activities
of type 6. Also, employees from muni-2 and muni-5 seem to have committed to
5Experiment details: https://git.io/Jq9uC
12 J. Yang et al.
more workload in executing activities of type 8. These groups also show sim-
ilarities regarding the types of cases they processed (Fig. 4c), as the majority
leaned towards handling the construction-related applications (‘Bouw’), espe-
cially muni-1 and muni-5. An interesting observation can be made regarding the
weekday pattern shown in Fig. 4b. Muni-1 diﬀers from others as it had only 12%
of its total workload assigned on Wednesdays. In the meantime, muni-2, muni-3
and muni-5 seem to form another cohort as Fridays were their least busy day.
This observation may link to diﬀerent arrangements of oﬃce hours in the groups.
(a) activity types (b) time types (c) case types
Fig. 4: Workload of the groups measured by rel focus in 2011–2014
Performance analysis. Fig. 5 presents an overview of group performance by
calculating indicator amount-related productivity and time-related productivity for
diﬀerent year-quarters. For analyses in this part, we base our observations on
work proﬁles starting from 2012 Q1, since we only included cases started after
2010-12-31 in our evaluation, and hence the numbers related to case completion
in 2011 may not reﬂect the actual performance6.
From Fig. 5a we can see that ﬁve groups follow a highly similar pattern in
terms of amount-related productivity (as the number of completed cases) — most
of the cases were completed in Q1, followed by that in Q4 and Q3, while the
least throughput happened in Q2. Compared across years, 2012 saw the most
completed cases. The groups’ performance decreased in 2013 and went slightly
higher in 2014. An observation worth mentioning is that muni-4 had a sudden
increase of performance after 2013 Q1 until 2014 Q2, and later dropped to the
same level as the other groups.
Fig. 5b provides another perspective on group performance visualizing time-
related productivity. Note that it is calculated by the average cycle time of com-
pleted cases, hence the performance is high when the value is low, and vice versa.
We can see that muni-3 delivered steadily high performance in terms of shorter
cycle time. Muni-5 also had a relatively consistent level of performance, which
slightly improved during the year 2013. The performance of muni-2 changed
across the four quarters, while within each year it follows a pattern: starting low
in Q1, improving in Q2, and gradually decreasing towards the end of a year (Q3
6The mean case cycle time in the dataset is 91.1 days (std. 105.8 days).
Seeing the Forest for the Trees: Group-Oriented Workforce Analytics 13
(a) amount-related productivity (cases) (b) time-related productivity (cases)
Fig. 5: Performance of the groups in 2011–2014
and Q4). This highlighted pattern of muni-2 would be interesting to investigate,
as this group is not unique in terms of amount-related productivity.
Meanwhile, the spike of case cycle time in muni-1 and muni-4 in 2013 also
deserves further attention. With our previous observation on the increase of
throughput of muni-4 in the same period, we selected the interval of 2013 and
used the detailed view to drill down the performance values of muni-4.
Fig. 6 depicts the visualization. The upper view clearly shows four sharp
increases of amount-related productivity. In each of the four weeks, muni-4 com-
pleted signiﬁcantly more cases (more than 30) compared to all other groups
(less than 10). This explains the spike in the overview (Fig. 5a) and may link
to the existence of batching behavior of muni-4. Interestingly, the increase of
amount-related productivity seems unrelated to the group’s time-related produc-
tivity as shown in the lower view. Cross-checking the same weeks in the two
charts, we can see that the potential batching completion did not directly link
to a signiﬁcantly longer case cycle time of muni-4.
5.3 Within-Group Analysis
We proceed to analysis at the group-member level, motivated by another ques-
tion raised by the process owner: What are the roles of the people involved in the
various stages of the process and how do these roles diﬀer across municipalities?
Fig. 6: Muni-4’s performance by amount-related and time-related productivity
14 J. Yang et al.
Following the question, we analyze the distribution within each group and focus
on the most active members.
Distribution analysis. Fig. 7 presents how individual resources within each group
handled diﬀerent types of activities distributed to them, which reﬂects the in-
volvement of resources at diﬀerent phases in the process. Comparing across the
columns in the heatmaps, we noticed two major patterns in all ﬁve groups, which
are more signiﬁcant in muni-4 and muni-5. There exists a cohort of resources
focusing primarily on the executions of activities of type 0, 4, and 5, while they
seldom carry out activities in the middle of the process (type 1, 2, and 3). Also,
there is another cohort of resources that exhibits a diﬀerent pattern as their
workload was mostly on executing activities from phases in the middle (type
1, 2, 3, and 4) in a balanced manner. This second cohort of resources was less
involved in executing activities of type 0 and 5. The two diﬀerent yet possibly
complementary patterns may relate to two business roles in the process.
The heatmaps also highlight patterns unique to some municipalities. For
example, resource ‘560925’ in muni-1 carried over 89% of its total workload in
executing activities of type 0, and 8% in conducting activities of type 1. The
resource was rarely involved in activities during the later phases in the process.
While such a pattern is not observed in the other groups, it implies that in muni-
(a) muni-1 (b) muni-2 (c) muni-3
(d) muni-4 (e) muni-5
Fig. 7: Distribution within each of the ﬁve groups (2011–2014) measured by
member assignment in terms of activity types. The values have been normalized
by member load of each individual for role analysis
Seeing the Forest for the Trees: Group-Oriented Workforce Analytics 15
1 there was a speciﬁc role for dealing with the initial processing of the received
applications. As another example, resource ‘8492512’ in muni-5 only executed
activities of type 0, 4, and 5 in the four-year period, and may have acted as a
specialist supporting the ﬁrst major role identiﬁed before (i.e., focused mainly
on activities of type 0, 4, and 5).
Summary. The above analyses on group work proﬁles using visual analytics
reveal interesting patterns regarding how ﬁve diﬀerent resource groups worked
on the same process, and identify areas that require further investigation. While
we do not aim at a thorough case study on these municipalities, we demonstrate
how the proposed notion of work proﬁles and the approach to identifying and
analyzing them can contribute to answering questions related to group-oriented
workforce analytics, through utilizing event logs.
6 Discussion, Implications, and Conclusion
Our study is inspired by research on analyzing resource group characteristics
and on mining individual resource behavior. The results of demonstrating the
proposed approach show that it can be applied successfully and provides inter-
esting insights with regard to workforce analytics. Compared to prior work, we
provide an approach that is based on theory and a subsequent conceptualization
on the group level. It allows the use of a minimum of information from event
logs to enable relevant workforce analysis on the group level and describes how
visual analytics can be used to support the analysis.
Our research has several theoretical implications. First, we contribute to
the discussion of connecting human resource management to the domain of
BPM . We introduce the interactionist theory to the domain of analyzing
groups in a BPM context and demonstrate how it is relevant for workforce an-
alytics for group performance and organization. We show how performance in-
dicators can be connected with interactionist-related parameters, using process
data to extract knowledge about how interaction leads to performance. Second,
our research provides insights on workforce analytics in the context of business
processes by conceptualizing the notion of work proﬁles of resource groups. As
such, we provide a better understanding of how such an organizational capabil-
ity can be fostered to enable high performance. The conceptualization of work
proﬁles allows the characterization and comparison among diﬀerent groupings
of employees over time. Such information is important to continually evaluate
existing organizational structures which might not reﬂect optimal interaction
between employees and have to be adapted. Hence, measuring and managing
resource groups is an important organizational capability as organizations con-
tinuously have to decide how to group employees to adapt to changing require-
ments. Third, we provide an analytical approach using actual process execution
data that can be used to determine the performance of groups over time and
identify possible root causes related to the internal group interaction for the
performance observed. Fourth, we show how the analytical results on the group
level can be visualized.
16 J. Yang et al.
From a practical perspective, process managers and analysts can beneﬁt from
the research outcomes which enable them to use event log data to objectively
evaluate work behavior of their resource groups. The analysis can pinpoint the
areas of interest across diﬀerent periods, diﬀerent levels of resources, and diﬀerent
process dimensions. The use of visualizations facilitates the interpretation of
analysis results in daily operations.
As with any research, our work is subject to limitations. First, the dataset
used in the evaluation only records end timestamps. Richer insights can be de-
rived if both start and end timestamps are recorded. Second, the proposed indi-
cators are based on standard event log information. While this allows for broad
applicability, other attributes, e.g., capturing the collaboration aspect of hu-
man resource groups, can be deﬁned and exploited to derive additional insights.
Third, factors related to other aspects of the interactionist theory, e.g., psycho-
logical factors, can be taken into consideration. For this, however, data sources
beyond event logs need to be included.
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