[1,4]Finn Klessascheck

[1]School of CIT, Technical University of Munich, Heilbronn, Germany

2]School of CIT, Technical University of Munich, Garching, Germany

2]Fraunhofer Gesellschaft, Munich, Germany

4]Chair for Software and Business Engineering, Technical University Berlin,
Berlin, Germany

# SOPA: A Framework for Sustainability-Oriented Process Analysis and Re-design
in Business Process Management

<EMAIL_ADDRESS>Ingo Weber<EMAIL_ADDRESS>Luise Pufahl
<EMAIL_ADDRESS>*  [  [  [

###### Abstract

Given the continuous global degradation of the Earth’s ecosystem due to
unsustainable human activity, it is increasingly important for enterprises to
evaluate the effects they have on the environment. Consequently, assessing the
impact of business processes on sustainability is becoming an important
consideration in the discipline of Business Process Management (BPM). However,
existing practical approaches that aim at a sustainability-oriented analysis
of business processes provide only a limited perspective on the environmental
impact caused. Further, they provide no clear and practically applicable
mechanism for sustainability-driven process analysis and re-design. Following
a design science methodology, we here propose and study SOPA, a framework for
sustainability-oriented process analysis and re-design. SOPA extends the BPM
life cycle by use of Life Cycle Assessment (LCA) for sustainability analysis
in combination with Activity-based Costing (ABC). We evaluate SOPA and its
usefulnesswith a case study, by means of an implementation to support the
approach, thereby also illustrating the practical applicability of this work.

###### keywords:

sustainable business process management, life cycle assessment, business
process life cycle, business process simulation, business process re-design

##### Statements and Declarations

###### Competing Interests and Funding

The authors have no relevant competing interests to disclose. No funds,
grants, or other support was received.

###### Acknowledgements

The authors acknowledge the work of Niklas Sumalvico towards the evaluation of
SOPA.

###### Ethics approval

Not applicable.

###### Consent to participate

Not applicable.

###### Consent for publication

Not applicable.

###### Availability of data and materials

The datasets generated during and/or analyzed during the current study are
available in the FigShare repository,
https://doi.org/10.6084/M9.FIGSHARE.22591513.

###### Code availability

We provide the software components developed for evaluating SOPA as open-
source software, available online at https://github.com/INSM-
TUM/sustainability-analysis-tool and https://github.com/INSM-
TUM/sustainability-scylla-extension.

###### Authors’ contributions

Conceptualization, F.K. and L.P.; methodology, F.K., I.W. and L.P..; software,
F.K.; validation, I.W. and L.P.; formal analysis, F.K.; investigation, F.K.;
resources, F.K.; data curation, F.K.; writing—original draft preparation,
F.K.; writing—review and editing, I.W. and L.P.; visualization, F.K.;
supervision, L.P.; project administration, L.P.

##  1 Introduction

The concept of sustainability has gained notable importance not only in
general societal debates, but also regarding the activities of businesses.
When considering the impact of business and their operation and processes,
traditionally, the focus is put on performance in a financial or operational
sense [28]. However, the scientific consensus about the relation between human
activity and the existence and acceleration of human-made climate change and
alterations of ecosystems underlines the necessity of considering
environmental impact. The UN’s Sustainable Development Goals, released as a
“blueprint to achieve a better and more sustainable future for all” [71],
state that a decoupling of environmental degradation and resource use from
economic growth and associated production and consumption patterns is
required. According to the report, unsustainable human activities have
significantly degraded the Earth’s ecosystems on a global scale, thereby
endangering the ecological foundations of society. Toxic substances introduced
into the environment as a result of e.g. wasteful manufacturing, play a
significant role in the endangerment of biodiversity and promotion of risks to
human health — a loss of biodiversity is closely linked to threats towards
human life and its surrounding environment [61]. Further, industrial activity
and production of heat and electricity, have been identified as key
contributors to climate change [37]. Moreover, current goals for conserving
and sustainably using nature, and achieving global sustainability, cannot be
met by current trajectories, requiring transformative changes across economic,
social, political, and technological factors [9].

These facts underline the necessity of considering the impact of businesses
and their activities on the environment, and on sustainability — in other
words, the degree to which they contribute to the ongoing deterioration of the
environment and ecosystems. Only when their impact is known, business can take
useful and directed measures in order to reduce that impact [28].

In literature and in practice, various approaches have been discussed and
established to assess the impact of businesses on both very broad and general
scales, and with specific perspectives and narrow measures. Broad approaches
include mechanisms that assess social, economic, and environmental impact of
enterprises in conjunction (see e.g. [12]). In the discipline of Business
Process Management (BPM), used in enterprises for modeling, analyzing,
measuring and improving their business processes [77], mechanisms have emerged
which assess sustainability with a very limited scope on a process-level view.
A well-known example is the work of [59], where mainly carbon emissions are
assessed. While broad analyses are unsuitable for assessing and re-designing
individual business processes in order to reduce their environmental impact,
existing process-focused approaches generally concentrate on a limited set of
measures to assess the environmental impact (e.g., carbon emissions and energy
consumption [20]). This, however, discounts many dimensions along which the
environment is also impacted negatively, such as emission of other greenhouse
gasses that contribute to climate change, land use, aquatic acidification, or
other substances toxic to humans [40]. Nonetheless, existing literature
underlines that integrating sustainability into BPM is a worthwhile research
effort in terms of research opportunity and relevance [68]. For instance,
enabling analysts and managers to consider sustainability on a process level
leads to meaningful insights for process re-design, as the process perspective
relates to where exactly impacts are caused and reductions of impact can be
implemented [60]. Further, it has been shown that enterprises have a need for
techniques that enable modeling, monitoring, and optimizing the environmental
impact of business processes, in order to establish sustainable business
practices [67]. However, a mechanism that operates on a process level while
taking into account a holistic view on environmental sustainability, and that
provides mechanisms for sustainability-oriented process analysis and re-
design, does not yet exist.

In light of this, we pose two research questions to be addressed by this
article that specifically target this gap.

  1. RQ1:

What are mechanisms with which the impact on environmental sustainability of
business processes can be analyzed holistically, without limiting the
dimensions of impact that are being considered to a small subset of
indicators?

  2. RQ2:

How can this mechanism be leveraged in a practical setting to facilitate both
process analysis and re-design?

This work targets these research questions by proposing SOPA, a framework for
sustainability-oriented process analysis, that combines Life Cycle Assessment
(LCA) and activity-based costing (ABC) within the BPM life-cycle. LCA is an
assessment technique from sustainability research, specifically the field of
environmental impact assessment (EIA), which is concerned with systematically
“identifying, predicting, evaluating and mitigating biophysical, social and
other relevant effects of proposed development proposals” [24]. LCA focuses
explicitly on identifying the cradle-to-grave (meaning from materials
procurement until the final disposal, i.e., across the complete life cycle)
environmental impact of products, services, and systems. This is in contrast
to considering solely a particular location or facility, as is the case with
traditional EIA [24]. Notably, the integration of LCA into BPM has been
identified as a key research gap [20]. Activity-based costing is a model of
determining (usually financial) costs in an organization by determining the
involved activities, their respective costs, and thus the cost of the actual
product [7]. This allows a more precise estimation of process costs than
previous accounting methods [42]. In SOPA, we utilize this notion to assess
the environmental impact of individual activities, and hence, the overall
process. Thus, SOPA allows process stakeholders to assess the environmental
impact of a business process holistically, and re-design it accordingly.

Concretely, SOPA contributes methods for assessing the impact of business
processes on environmental sustainability by assigning impact measures based
on LCA to activities with ABC, and proposes concrete measures for
sustainability-oriented process re-design via business process simulation. In
contrast to existing approaches, SOPA aims at a holistic consideration of the
environmental impact through LCA, instead of focusing on a few explicit impact
dimensions, and more explicitly covers mechanisms for re-design. In this work,
we follow a design science research methodology [81, 54] in the design and
evaluation of the artifact, SOPA. The evaluation is based on business process
simulation [55] and a prototypical implementation, and applied to a real-world
case study. We show how SOPA is able to be leveraged for process re-design by
evaluating various re-design scenarios for their environmental impact and
determining the least impactful variant.

The following section presents background information on sustainability, ABC,
LCA, BPM and business process simulation. Then, the methodology of design
science, utilized in this work, is detailed (Section 3). Subsequently, the
resulting framework SOPA is presented and evaluated (Section 4). Finally, the
results, as well as practical and theoretical considerations for BPM, are
discussed (Section 5).

##  2 Background and Related Work

###  2.1 Sustainability

The term of sustainability has been established in discussions about viable
long-term development, be it economic, social, or ecological. In 1987, the
Brundtland Report of the World Commission on Environment and Development from
the United Nations formulated sustainability as ensuring that the needs of the
present are met without compromising the ability of future generations to meet
their own needs [5].

Since then, some estimated three-hundred definitions of sustainability and
sustainable development have emerged in environmental management and other
disciplines [39]. This underlines that, while being a term of everyday use,
defining the concept itself is complex, and a general understanding and
uniform definition is still missing [57]. Some authors, such as [39] argue,
therefore, that an unambiguous definition of sustainability and an
authoritative framework that can be used to assess policies and practice
w.r.t. sustainability is needed.

Nonetheless, a common understanding has emerged in literature, which can be
summarized as the triad model of sustainability. This model, displayed in Fig.
1, illustrates that sustainability can be understood to consist of three
interconnected facets, the economic sustainability, the social sustainability,
and the environmental sustainability. The triad nature of the model underlines
that all three facets are inextricably linked, implying a strong reciprocal
influence [2].

Figure 1: Triad model of sustainability, adapted from [23]

Following [2], the three facets have been defined as follows: The economic
facet of sustainability is concerned with controlled growth and long-term
gains in a sense of capital preservation through careful use of natural
resources. However, quantitative growth is still seen as a requirement. The
focus of social sustainability lies on intergenerational and intragenerational
justice in terms of access to resources and satisfaction of basic needs.
Besides that, it explicitly touches on issues of gender relations in the sense
of creating fairer living environments for all genders. Environmental
sustainability deals with the preservation of the natural basis of life and
its life cycles, i.e., ecosystems and other natural functions, through the
prudent use of natural resources. This not only includes the economic
necessity of preserving existing natural capital, but also securing the
ecological conditions of human survival in general.

It has been argued that the difference between considering all facets at once,
and only considering one facet with a limited set of indicators is a
difference in perspective, of a “broad” and “narrow” view on sustainability
[34]. This work adapts a narrow perspective on sustainability, focusing on the
environmental facet being impacted by business process. In other words, the
main focus of SOPA lies in assessing the cost incurred by individual business
processes w.r.t. the environment.

###  2.2 Life Cycle Assessment

The concept of sustainability implies that the costs of business activities
and business processes are not just financial costs, but also costs towards
the environment due to, e.g., emissions, energy usage, and waste. In order to
capture these impacts, techniques such as life cycle assessment (LCA) have
emerged. LCA is a technique belonging to the field of environmental impact
assessment (EIA). EIA is concerned with the systematic identification,
prediction, and evaluation and mitigation of biophysical (i.e.,
environmental), social and other impacts of development proposals [24]. In
contrast to other traditional EIA techniques, the focus of LCA lies in
identifying the environmental impact across the entire life cycle of a
product, service, or system, and not only the impact at an individual facility
or location [24]. LCA is further described by a set of ISO norms [38].

Figure 2: Life cycle assessment framework and its four phases, adapted from
[8, 38]

Sustainability analyses with LCA are conducted in four distinct phases, as
illustrated in Fig. 2. During the goal and scope definition phase, the
analysis goal and its boundaries are defined. In terms of goals, LCA is
suitable to answer several types of questions about systems: predictive
scenarios (how will the system behave), explorative scenarios (how can the
system behave), and normative scenarios (how can a specific objective be
reached) [18]. In the life cycle inventory analysis phase (LCI), relevant data
is collected and a pre-determined calculation method is applied, quantifying
all relevant input and output. Relevant units could be, e.g., kg of carbon
dioxide, cubic meters of natural gas, mg of phenol, and others [8]. The LCI
phase is, generally, data, labor, and time-intensive. To alleviate this issue
and aid analysts, LCI databases have been developed that contain data for
products and services needed in many LCAs, such as electricity generation, raw
materials, and transportation processes. They are public national or regional
databases, as well as industry-provided or consultant databases, and are
often-times offered with LCA software, such as the Swiss ecoinvent database
[76, 18]. The life cycle impact assessment phase (LCIA) is where the results
of the LCI are further processed and interpreted with respect to environmental
impacts. A list of impact categories is defined (e.g., degree of ozone layer
depletion, ecotoxicity, human toxicity, climate change) and models for
relating the environmental interventions identified in the LCI phase to
indicators for these categories are selected, after which actual results are
calculated. The perspective of LCIA is holistic in nature, aiming to capture
all possible areas of impact [18]. Further, the results can be normalized, and
the different category indicators can be grouped and weighted to, e.g.,
aggregate them into a single score, expressing in a unit-less manner the
overall environmental impact of the considered system. Some methodologies
(e.g., Eco-indicator 99 [26]) do this per default, for others, it can be done
a posteriori [8]. During the life cycle interpretation phase, the results of
the analysis, as well as the underlying choices and assumptions made in each
phase through the analyst, are evaluated. Overall conclusions are drawn,
including an evaluation of the results w.r.t. their consistency, completeness,
and robustness. Finally, conclusions and recommendations are formulated [8].

LCA has been identified as a useful tool to holistically measure the degree of
impact of products, goods, or services on the environment, and thus, their
impact on environmental sustainability.

###  2.3 Business Process Management

For enabling an analysis and re-design of business process for their
environmental impact, the use of LCA methods needs to be embedded into a
rigorous methodological context. To this end, this section presents the
discipline of business process management (BPM), dealing with analysis,
design, and implementation of business processes. This discipline aims at
improving and running business processes through a combination of
technological and management sciences [72].

Business processes are considered to be sets of activities that are performed,
in a coordinated manner, in an organizational and technical environment, to
achieve a business goal [77]. An example of such a process is the production,
shipping, invoicing and payment of a specific good, e.g., a laptop computer.
BPM comprises concepts, techniques, and methods for supporting design,
administration, configuration, enactment, as well as the analysis of business
processes [77]. These processes can be represented as business process models,
for example via modeling languages such as Business Process Model and Notation
(BPMN) [53]. The models serve as a medium to communicate requirements and
behaviors of the process they capture, and therefore serve as a central
artifact of various disciplines in the area of process science [77]. Business
process management systems can, based on such a process model, execute and
enforce the respective business process in individual process instances [72,
77].

Figure 3: Business process life cycle, adapted from [77]

The business process life cycle can be structured in four main phases, as
depicted in Fig. 3, with which organizations can implement and maintain
business processes. During the design and analysis phase, business processes
are identified, analyzed, and expressed explicitly in the form of process
models. Validation (i.e., ensuring that the model correctly reflects the real-
world execution through, e.g., stakeholder interviews), verification (i.e.,
ensuring formal correctness of the model), and simulation techniques (e.g.,
stochastic technique to simulate process execution, as further explained in
Section 2.3.1) can be utilized during this phase. The resulting process model
and the behavior that it specifies are then used in the configuration and
implementation phase to implement the respective business process. This is
commonly done either on a technical level by, e.g., configuring the involved
information systems that support the execution of that process, by adapting
business process management systems used to enact the process based on its
model, or by formulating a set of rules and policies to be followed by
employees. During the enactment phase, process instances are enacted, or
executed. Here, execution data of process instances is typically gathered in
the form of execution logs, or event logs [77]. These logs form the basis of
the subsequent evaluation phase. In this phase, the process is evaluated for
shortcomings or potential for improvement, based on information captured
during enactment. This phase is also related to simulation techniques of the
design phase. If changes to the process need to be implemented after
evaluation, a new iteration of the business process life cycle can be started,
by re-entering the (re-)design phase [77]. Re-design objectives commonly
include improvement of process performance among the dimensions of time, cost,
quality, and flexibility [10].

####  2.3.1 Business Process Simulation

A method that has shown practical benefit for process analysis and re-design
is business process simulation (BPS). It can be applied to understand the
behavior of a system, in order to assess the implications of alterations made
to the process itself or the surroundings of the process [19, 1]. Further, it
is a quantitative analysis method of process models, grounded in probability-
based simulation of discrete events. BPS can be used, e.g., to consider
process costs of different process alternatives [19]. Moreover, insights into
existing or future scenarios of the execution of that process can be attained
[73]. BPS has been identified as an important tool in the life cycle of BPM
(see Fig. 3), especially the (re-)design phase: it is possible, through
simulation, to create hypothetical scenarios of process re-designs and
evaluate and improve them, before implementing them in the real world [1].
This eliminates cost and risks inherent with testing changes in a practical
setting [19, 63].

To facilitate this simulation and analysis, a business process simulator is
required [55]. This tool aims at simulating the execution of process instances
based on input provided by a process analyst (e.g., resource information,
branch probability, number of processes instances to simulate, and other
parameters [47]), which is usually done in the form of a simulation
configuration. The result of that simulation is artificial information in the
form of, e.g., an event log and a performance report about the process, which
provides, among others, information about probable throughput time, process
costs and resource utilization of the current process design [10, 82]. The
resulting information can then be used to iteratively drive the re-design of
the considered process. However, it should be noted that BPS abstracts from
the real-world behavior of the considered process, and, due to its stochastic
and qualitative nature, depends greatly on the input parameters. Thus, they
should be chosen with care and, if need be, validated externally [10]. An
alternative approach is the use of historic data from logs [63], which can
however only deliver reliable results if the process version from which the
data stems is similar enough to the version that is simulated. In practice,
some frameworks for process re-design explicitly relying on process simulation
have been outlined or implemented (e.g., [46]) – however, none of them include
a notion of sustainability-oriented process re-design, which is a shortcoming
that SOPA aims to address.

####  2.3.2 Activity-based Costing

When considering (financial) costs involved in organizational settings, such
as business processes, the perspective of activity-based costing (ABC) has
been established — it recognizes that all activities taking place in an
organization directly or indirectly support the overall business goal, and
hence should be the places where costs are allocated [25]. Thus, ABC is a
model of determining (financial) costs in an organization by determining the
involved activities, their respective costs, and thus determining the costs of
the product or service based on the costs of the activities [7]. This allows
for a more precise estimate of process costs than traditional accounting
methods [42].

ABC contains several key concepts, which are illustrated in Fig. 4. Cost
objects represent the overall cost of the system under investigation, such as
a product or a process. Activities that contribute to that product or process
consume resources, which, through resource drivers, incur resource costs. In
the context of BPM, resources are usually understood to be, among others,
human resources, machines, vehicles, or materials, which greatly contribute to
the execution of individual activities and process instances [36] — this is a
perspective similar to that in ABC [25]. Resource drivers represent the demand
placed on resources by an activity [42], and could be, e.g., the time an
employee has spent on a specific activity, depreciation of the equipment
involved in that activity, the supplies used, etc. Thus, by considering what
resources contribute to an activity, and how the activity places demands on
them, the resource cost of that activity can be determined. Cost drivers
determine the cost of the operational activities to which they are linked.
They be, e.g., the number of products ordered, number of parts required, etc.
[42]. By considering cost drivers of activities and resource costs imposed on
resources by an activity, the activity costs can be determined. Activity
drivers represent the demand placed on activities, e.g., the number of times
an activity needs to be executed, or the duration of it. With these activity
drivers, the actual activity cost can be allocated to the overall cost object
[25, 42]. Activity drivers could be, for example, the number of deliveries
made to a customer, thus determining the overall cost for the customer in
question [25]. When all activity costs have been considered and allocated to
the cost object, the overall cost of the system has been determined.

Figure 4: The structure of an activity-based costing model, adapted from [42]

In summary, ABC outlines a method in which, by considering the costs of single
activities and how they relate to, e.g., a process being considered by the ABC
analysis, the overall cost of that process can be determined. SOPA utilizes
that notion for the assessment of environmental impact: by determining the
individual environmental cost of activities with LCA, the overall
environmental cost of the process is determined.

###  2.4 Related Work

####  2.4.1 Sustainability and BPM

The integration of sustainability considerations into the framework of BPM has
been investigated at various levels in the literature.

In [3], various contributions consider sustainability and its integration into
BPM. [65] provide a framework for BPM research and practice with a focus on
sustainability and outline challenges for sustainable considerations along the
key capabilities of BPM. For example, methods for the analysis, design, and
implementation of sustainable business processes are seen as a key challenge.
[4] investigates existing literature on IS research with a focus on
sustainability, and identifies the motivation for and adoption of
sustainability initiatives as major research gaps. [32] critiques existing
sustainability analyses that focus on individual organizations, noting that
they should also include other environmental factors such as biodiversity and
others, and argues for challenging notions of productivity, cost reduction and
profitability. [31] propose a semi-automated approach for reducing the
environmental impact of business processes. They present a method where
process activities can be annotated with ratios for e.g, consumption of energy
or fuel, and production of carbon dioxide with are not to be exceeded during
process execution, and which can be monitored. For process re-design, they
outline a mechanism that uses a library of best-practice fragments to replace
“unsustainable” parts of a business process, to reduce e.g., carbon dioxide
emissions and to not exceed the annotated ratios. [60] propose an extended
BPMN notation to capture the consumption of fuel and paper (which both produce
carbon dioxide), as well as the flow and emission of carbon dioxide in process
models. Based on an ABC-like analysis of greenhouse gas emissions, the
environmental impact of the process and of activities in terms of greenhouse
gas emissions can be assessed, and consequently improved. [79, 80] outline a
mechanism that similarly follows an ABC-based approach, focussing on
greenhouse gas emissions and labor cost. Further, they propose to utilize
simulation-based techniques to determine how activity timings change the
overall greenhouse gas emissions and financial cost of the process. The work
of [51] describes how environmental impact in terms of greenhouse gas
emissions, water usage and energy consumption can be measured or estimated
during process execution on an activity level and highlighted in a process
model, so that potential process alternatives can be derived. [6] argue that
techniques for measuring and managing process performance can be used to
assess and improve the sustainability of enterprises. Subsequently, [83]
investigates performance measurement systems for their potential to be used
for assessing the sustainability of companies. While the set of ISO norms that
describe LCA [38], see Section 2.2, is noted for its focus on environmental
aspect and applicability for business processes, other measurement systems are
also considered suitable. They propose a combination of reporting and
management frameworks, and conclude that concrete obligatory requirements
towards enterprises are needed to be able to establish concrete sustainability
objectives. Additionally, [44, 45] describe how business process models of
certain domains (e.g., public administration) can be analyzed for pre-
determined patterns that hint at a high environmental impact, and provide
means for re-designing the model accordingly. However, no quantification of
impact, resp. reduced impact, is done.

Generally, in a structured literature review of “green BPM”, [28] note that,
most commonly, sustainability in a BPM context is assessed (either during the
design or the evaluation phase) in terms of greenhouse gas emissions, energy
consumption, and utilization of materials and water (e.g., [79, 80], [51],
[30] and [59, 60]). These impacts are usually elicited either on a process
level, or on an activity basis with variations of ABC. A literature review by
[62] confirms the prevalence of carbon emissions, energy consumption, and
water use as indicators for environmental impact. While important categories
of impact, they arguably do not sufficiently cover the impact that business
process have on the environment. As [74] argues, reducing “environmental
performance” to a limited set of indicators such as carbon dioxide emissions
and water footprint brings the danger of leading to improvement of these
indicators only, while ignoring the impact a process might have on
environmental dimensions that are not considered by these indicators. This
highlights the need for a more holistic manner of assessing the environmental
impact of business processes, taking into account a broader set of impact
dimensions. Finally, in a systematic tertiary literature review [20] identify
a structured integration of LCA and BPM in order to incorporate sustainability
considerations into BPM in a practical manner as a research gap worth
investigating, and share the observation of a narrow focus on carbon emissions
and energy use found in related work.

By considering related work, found during a literature search, and systematic
literature reviews in the area of sustainability and BPM, we observe that
notably, none of the approaches provide explicit mechanisms that
_holistically_ assess the environmental impact and provide a concrete
methodology to re-design the process in a sustainability-oriented manner. SOPA
aims to close this gap found in the literature by explicitly including the
technique of business process simulation during the analysis and re-design
phase, which is explained in the subsequent section, and by considering a more
encompassing set of impact factors through the mechanism of LCA. Instead of
static analysis based on just a process model, SOPA includes dynamic analysis
based on process executions.

####  2.4.2 Process Optimization

In the area of BPM, related work has investigated manual and algorithmic
approaches for optimizing business processes towards certain goals. For
example, [11] describe various heuristics (i.e., general “rules of thumb”)
with which improved process designs can be determined, and their impact on
time, cost, quality, and flexibility of the process. More algorithmically,
[21] propose a method to derive process designs that are optimal in terms of
costs and durations based on activities and resources and their attributes,
such as task costs and resource in- and outputs. Finally, [43] present a
framework to identify more optimal process executions based on cost functions
for cases, activities, and resources, to identify trade-offs between time,
cost, and resource allocation. However, all of the algorithmic approaches aim
at automatically identifying improved process configurations that are optimal
based on some parameters, whereas SOPA aims to allow process experts to
evaluate and deliberate different improvement scenarios derived from domain
knowledge. Therefore, SOPA is more closely aligned to the manual heuristics of
[11], where a reduction in environmental impact through changes of activities
and process implementation is the main objective, although based on a
quantitative evaluation of the environmental impact and its reduction.

##  3 Methods

In disciplines such as information systems (IS) research or BPM research,
theoretical and technical, i.e. practical, aspects interlink. Here, research
is often conducted in an applied manner, in order to solve certain problems. A
widely accepted paradigm that aims at enabling explicitly applicable research
results is design science research (DSR) [54].

###  3.1 Design Science Research

DSR, as outlined by [54] and [81], is a methodology in IS research that is
primarily concerned with solving problems through the creation and evaluation
of artifacts. The fundamental principle is the acquisition of knowledge and
understanding of a problem through these artifacts [58]. The notion of
artifact in DSR contains anything that is constructed by humans (i.e.,
artificial) — be it a methodology, a conceptual framework, or a specific
software. DSR describes a process, with which observed problems can be
addressed, research contributions can be provided, designed solutions can be
evaluated, and the results can be communicated [54]. Figure 5 outlines the
process of DSR and how it is applied in this work. Following [81], SOPA is
evaluated in a specific context to investigate its implications, through a
single-case mechanism experiment.

Figure 5: Methodology of design science and its application in this work,
adapted from [54]

As the first step of DSR, where the problem domain is investigated and the
research itself is motivated, this work argues that, given the pressing matter
of climate change and a need for environmentally sustainable business
processes, a need for SOPA is underlined. Sections 1 and 2.4 provide a
thorough discussion of this. Solution objectives are further considered in
Section 2, where a need to holistically integrate environmental sustainability
impact assessment into the BPM life cycle is identified through a literature
search and related literature reviews. Based on these objectives, the
framework SOPA, which utilizes LCA and ABC, is designed through a formal-
deductive approach [49] and described in detail in Section 4.1. In a first
iteration, the concepts of ABC and LCA were formalized in line with BPM
notions, to derive a theoretical formal framework. This was implemented
prototypically and applied to a small set of synthetic use cases, after which
the formalisms of the framework and their prototypical implementation were
improved. After presenting the result to a number of research and
practitioners in the areas of BPM and sustainability, the formalisms, the
framework, and the prototypical implementation were finalized in a third
iteration. Finally, corresponding to the 4th and 5th step of DSR, SOPA is
demonstrated and evaluated by means of an implementation (described in Sec.
4.2) on a real-world case study in Section 4.3. The entire research article
represents the 6th step of DSR, where the problem, its importance, and the
resulting artifact are presented to transmit the knowledge gained during the
DSR process. How data was elicited for this case study is further illustrated
in Section 3.3. The methodology used for designing this implementation is
described in more detail in Section 3.2.

###  3.2 System Development

While DSR is used to design SOPA, with SOPA being the resulting artifact of
the DSR methodology, a concrete software system is also required to facilitate
the evaluation and demonstration of SOPA, and to support the overall approach
in a practical setting. Here, a system development methodology is followed,
where, based on SOPA as a conceptual framework, a system architecture is
developed, the system is analyzed and designed, and finally built and
evaluated [52]. Figure 6 shows the system development process and its steps.

Figure 6: System development research methodology, adapted from [52]

The system functionalities and requirements to be developed, are implicitly
part of the SOPA framework designed with DSR (see Section 3.1), which the
system aims to implement on a technical level. Based on these requirements, a
concrete system architecture with specific components that have specific
functionalities is outlined in Section 4.2. This includes components such as a
concrete business process simulation engine, and a tool for analyzing and
calculating impact measures. Further, the process used to carry out the
respective system functions (i.e., to conduct sustainability-oriented process
analysis and re-design), is illustrated in Section 4.2. Finally, the system is
built and evaluated with software components developed specifically for this
purpose in Section 4.3. These resulting components are available as open-
source software111See https://github.com/INSM-TUM/sustainability-analysis-tool
and https://github.com/INSM-TUM/sustainability-scylla-extension [Accessed: May
26, 2023]. Thus, the evaluation of the concrete system also serves as a
general evaluation of SOPA, following the penultimate step of DSR, by
demonstrating its applicability and utility in a practical setting.

###  3.3 Case Study — Data Collection

In this work, SOPA is evaluated through application with a case study. For
this, a hiring process in a university setting is assessed, analyzed, and
investigated for potential to reduce its environmental impact. The relevant
data has been elicited via unstructured, qualitative stakeholder interviews
and document analysis. Two chairs of a department and their assistant were
interviewed, firstly to elicit a process model of the hiring process, and
second, to identify factors relevant to both the LCA analysis and the process
simulation. Further, the interviews and other relevant documentation (such as
a comprehensive guide on required documents) were utilized to reach reasonable
estimates or assumptions, in case no sufficient information could be elicited.

The LCA analysis was conducted with mobius222See https://mobius.ecochain.com/
[Accessed: Feb. 24, 2023] a web-based tool for LCA analyses. The concrete LCA
method used was EF 3.0 (Ecoinvent v 3.8 Cut-Off) [15], with normalization and
weighting (see Section 2.2). Multiple impact scores were calculated in
reliance on the Ecoinvent database v3.8333See https://ecoinvent.org/the-
ecoinvent-database/data-releases/ecoinvent-3-8/ [Accessed Feb. 27, 2023].

The data elicited for this case study is available online444See
https://doi.org/10.6084/M9.FIGSHARE.22591513 [Accessed: October 10, 2023 ].
Figure 7 displays that process, which will be used subsequently as a running
example to explain the application of SOPA. A higher resolution of the process
model is available online at the same location.

##### Hiring process.

The process starts with the need for hiring of an employee in a department. An
official request for a job advertisement is posted and checked content-wise
and formally by various stakeholders (such as the faculty or the diversity
officer). Should one of the assessments fail, a new request can be submitted,
or the hiring is cancelled. If all checks have been successful, the job
advertisement is submitted to the HR department. Afterward, once the
application deadline has elapsed, the candidates are sifted and selected.
Subsequently, the selected candidates are interviewed. Due to local gender
equality laws, at least two candidates of differing gender are interviewed. In
practice, however, we find that usually no more than five candidates are
considered potential applicants. After the interviews, if a suitable candidate
is found, their employment is requested. Once more, various stakeholders check
the request. Should no suitable candidate exist after the interviews, or
should the final checks fail, the hiring process has failed. If the final
checks are passed, the contract is finalized with the HR department and the
hiring is completed.

Figure 7: BPMN diagram displaying the hiring process elicited through
stakeholder interviews

##  4 Results: SOPA Artifact Design and Evaluation

Following the DSR methodology as described above, the results of this study
are the design, implementation, and evaluation of our artifact, the SOPA
framework. Accordingly, in this section we first present SOPA, including its
application procedure and the resulting metamodel. Then, we describe our
implementation of the framework in a prototype system, which enables the
evaluation based on a case study-based, covered in the last part of this
section.

###  4.1 Framework and Metamodel

SOPA proposes a three-step approach for analyzing business processes for their
environmental impact and their sustainability-oriented re-design. When
applying SOPA to analyze business processes for their environmental impact and
to re-design them accordingly, two possible scenarios exist. Either an
existing event log, which contains execution data of real-world process
instances, is analyzed and used for reasoning about improvement potentials
(log-based analysis), or a process model is, through utilization of business
process simulation, assessed (model-based analysis). This leads to the
following three central steps of SOPA:

  1. 1.

Either, a process model is created and annotated, and simulated to generate
synthetic execution data for further assessment. Alternatively, an event log
containing execution data of real-world process instances, needs to be
extracted from the corresponding information system and provided.

  2. 2.

The execution data is used in conjunction with LCA data to calculate the
environmental costs of the process and its activities.

  3. 3.

Should a need for process re-design become apparent, process simulation can be
used further to investigate potential improvements.

Figure 8 displays the overall application procedure, with the boxes
representing the concrete tasks, separated along the three central steps of
SOPA as described above. Following the arrows, after deciding to begin the
analysis, either the model-based analysis on the upper path, or the model-
based analysis on the lower path is conducted. Re-design iterations, which are
conceptually model-based, lead to another iteration of the model-based path.
Otherwise, the analysis is complete, and process experts can deliberate the
findings. Notably, SOPA works without simulation, given that the required LCA
data is present in the event log. This is a strong requirement for the
evaluation, however, therefore this work applies process simulation to
illustrate the application in terms of analysis and re-design.

Figure 8: Application procedure of SOPA

In the following, Section 4.1.1 starts with presenting the modeling- and
simulation-related activities and concepts of SOPA, and requirements towards
extracted event logs. Section 4.1.2 details the environmental cost calculation
and concepts, while Section 4.1.3 illustrates the steps for sustainability-
oriented process re-design of SOPA.

####  4.1.1 Event Log Simulation and Extraction

In the following, the model-based variant of the first step of SOPA is
explained because it illustrates all modeling concepts required by SOPA.
However, requirements towards extracted event logs to be used with SOPA are
provided at the end of this section. Figure 9 displays a metamodel of these
modeling concepts and their relations.

Figure 9: Metamodel of modeling-related concepts of SOPA. Extensions
conceptualized in this work are shown with dashed borders

First, as the basis for a model-based analysis, the process needs to be
modelled, e.g. by using process modeling languages such as BPMN [53]555While
we use BPMN for process modeling due to its industry-wide adoption as a
standard, prevalent tool support, and extensibility [56], SOPA conceptually
allows different modeling languages to be used, as long as all concepts
formalized in this work can sufficiently be expressed.. Activities form the
basis of processes and process models, as shown in Fig. 9. Definition 1
expresses this formally.

######  Definition 1.

(Activity) Let $A$ be the universe of all possible activities. Then, $a\in A$
is a single activity that can be enacted during process execution.

Afterward, abstract environmental cost drivers are annotated to the individual
activities. They describe, on an abstract level, what objects, resources and
products, i.e., everything that is generally assessable through LCA, are
involved in the execution of specific activities.

In the case of the running example (see Fig. 7), this includes printing
documents relating to the hiring procedure, or the transportation of documents
between different departments using the in-house mail. Figure 10 displays
these abstract environmental cost drivers annotated to the activities of a
process model.

Figure 10: BPMN diagram of the hiring process, with annotated abstract
environmental cost driver

On the other hand, concrete environmental cost drivers describe the
environmental impact of concrete instantiations of their abstract
counterparts, based on LCA analyses through individual LCA scores. For
example, the specific mode of transport used by the in-house mail could be
either a petrol-powered car or an electric bicycle, leading to different
impact scores for the two possible variants. During modeling, abstract and
concrete environmental cost drivers are expected to be identified in
conjunction (either, abstract or concrete environmental cost drivers are known
at first), and continuously refined through abstraction, respective
concretization.

The relation between abstract and concrete environmental cost drivers can be
organized into a strict hierarchy. This environmental cost driver hierarchy,
characterized in Definition 2, organizes abstract environmental cost drivers
and concrete environmental cost drivers, and describes how abstract
environmental cost drivers can be concretized during process execution. This
represents the materialized relation between abstract and concrete
environmental cost drivers shown in the metamodel of Fig. 9.

######  Definition 2.

(Environmental Cost Driver Hierarchy) Let $D$ be a set of abstract
environmental cost drivers, where $d\in D$ is a single abstract environmental
cost driver. Let $C$ be a set of concrete environmental cost drivers, where
$c\in C$ is a single concrete environmental cost driver. Finally, let $H$ be a
hierarchy of abstract and concrete environmental cost drivers, so that
$H=D\times C$. This means that each abstract environmental cost driver $d\in
D$ has one or more concrete environmental cost drivers $c\in C$ associated
with it.

For each concrete environmental cost driver, a concrete, LCA-defined impact
score, or cost, can be determined — either, through manual LCA analyses, or
through an information system used for process execution, that already
supplies this information. This is realized through a cost function described
in Definition 3, where for each concrete environmental cost driver, the impact
score is returned.

######  Definition 3.

(Cost Function) Let $C$ be a set of concrete environmental cost drivers. Cost
functions assign concrete environmental costs in terms of impact on
sustainability to concrete environmental cost drivers. More specifically, let
$cost$ be a cost function of concrete environmental cost drivers so that
$cost:C\rightarrow\mathbb{R}$ assigns environmental cost values to concrete
environmental cost drivers.

For example, the abstract cost driver $d\in D$ could have various
concretizations defined in the cost driver hierarchy $H$, such as
$c_{1d},c_{2d}\in C$. Both represent different concretizations of the abstract
delivery of in-house mail. Their difference in environmental impact is
reflected by the cost function $cost$, which, in this example, might assign a
cost of $3.91\times 10^{-5}$ to the first and $4.22\times 10^{-6}$ to the
second cost driver.

The LCA scores assigned to concrete environmental cost drivers need to have
either been part of the recorded event data, or be elicited through LCA
analyses of process experts with an LCA method that aggregates the LCA results
into a single score. Notably, the quality of the SOPA-led analysis is
determined by how precisely the event log has captured the process executions,
respective represents them accurately. Further, the LCA analyses and their
exactness determine how exact the impact of concrete environmental cost
drivers, and thus, the overall process, can be assessed. Here, making use of
LCA databases (see Section 2.2) which could be shared industry-wide to
disseminate accurate data for certain materials, products, or services, might
increase the analysis’ accuracy and reduce the time needed for conducting the
LCA analyses.

Cost variants govern what specific combinations of concretizations can occur
during individual process instances based on the environmental cost driver
hierarchy — in other words, what sets of concrete environmental cost drivers
can, during process execution, take the place of the abstract environmental
cost drivers during activity execution. For example, one concrete mode of
transport (i.e., via car or bike) requires the documents to be printed
(represented with one concrete environmental cost driver), while another mode
(i.e., sending them via email instead) requires them to exist as digital
documents (represented with a different concrete environmental cost driver).
In Fig. 9, this is shown by the mediation of concrete and abstract
environmental cost drivers through the cost variant.

Further, cost variants can be used to emulate cost distributions: when, e.g.,
one abstract environmental cost driver is expected to occur with a continuous
distribution, this distribution can be divided into several ranges, for which
concrete LCA scores and thus specific cost variants can be determined. Based
on the expected frequency of the respective ranges, their cost variants can be
simulated, and thus, the distribution be approximated.

Finally, the overall process simulation can be configured through a simulation
configuration (see Section 2.3.1), by not only selecting cost variants and
their frequency of occurrence, but also by determining conventional parameters
of process simulation, such as gateway probabilities and event arrival rates.

The concepts outlined in this section constitute a basis for process
simulation, through which a simulated event log can be generated, that can
serve as the basis for the second step of the framework (i.e., the calculation
of the environmental costs, see Fig. 8).

Listing LABEL:lst:xes-excerpt displays an example event log excerpt for the
hiring process, which could either be extracted from an information system, or
created synthetically using process simulation. In both cases, activity
instances (i.e., events) contain information about environmental cost drivers,
and process instances (i.e., traces), information about the respective cost
variant. Either, by being recorded in the information system, or by being
modelled during process simulation, knowledge of the environmental cost driver
and cost variants can be used to compute the overall environmental impact of
the business process for which the log is available.

Listing 1: Excerpt of an XES [35] event log belonging to the hiring process
showing additional cost variant and environmental cost driver information.

⬇

<trace>

<string key=”concept:name” value=”410”/>

<string key=”cost:variant” value=”standard procedure”/>

<event>

<string key=”concept:name” value=”Hiring required”/>

<string key=”lifecycle:transition” value=”start”/>

<date key=”time:timestamp” value=”2026-07-17T15:35:28+02:00”/>

</event>

<event>

<string key=”concept:name” value=”Hiring required”/>

<string key=”lifecycle:transition” value=”complete”/>

<date key=”time:timestamp” value=”2026-07-17T15:35:28+02:00”/>

</event>

<event>

<string key=”concept:name” value=”Submit request for job advertisement
(Department)”/>

<string key=”lifecycle:transition” value=”start”/>

<date key=”time:timestamp” value=”2026-07-17T15:35:28+02:00”/>

</event>

<event>

<string key=”cost:driver” value=”Request for job advertisement”/>

<string key=”concept:name” value=”Submit request for job advertisement
(Department)”/>

<string key=”lifecycle:transition” value=”complete”/>

<date key=”time:timestamp” value=”2026-07-17T16:12:16+02:00”/>

</event>

…

</trace>

The individual cost of concrete environmental cost drivers in relation to cost
variant can be modelled or provided with a cost variant config, such as the
one shown in Listing LABEL:lst:cost-variant-excerpt. As described above, they
identify the specific combinations of concretizations that can occur during
process instances. Further, the environmental cost for each concretization of
the environmental cost drivers is provided here, representing the cost
function described above in Definition 3. Notably, such a configuration file
can also be used to concretize environmental cost drivers in event logs that
have been extracted out of an IS and not just simulated, provided information
about the variants and abstract environmental cost drivers to be concretized
is present.

Listing 2: Excerpt of a cost variant configuration file describing how
abstract environmental cost drivers are concretized depending on the
respective cost variant of a process instance.

⬇

<?xml version=”1.0” encoding=”UTF-8”?>

<costVariantConfig count=”100”>

<variant id=”standard procedure” frequency=”0.5”>

<driver id=”Request for job advertisement” cost=”0.0000289”/>

<driver id=”In-house mail” cost=”0.0000391”/>

<driver id=”Advertisement” cost=”0.0000291”/>

…

</variant>

<variant id=”transport with e-bike” frequency=”0.2”>

<driver id=”Request for job advertisement” cost=”0.0000289”/>

<driver id=”In-house mail” cost=”0.00000422”/>

<driver id=”Advertisement” cost=”0.0000291”/>

…

</variant>

<variant id=”digital only” frequency=”0.3”>

<driver id=”Request for job advertisement” cost=”0.0000195”/>

<driver id=”In-house mail” cost=”0.000000151”/>

<driver id=”Advertisement” cost=”0.0000195”/>

…

</variant>

</costVariantConfig>

The first variant, representing the standard procedure of the running example,
will be simulated 50 times out of the configured 100 process instances. The
environmental cost drivers all provide concrete impact scores, that will be
assigned to all relevant activity instances that are simulated with the first
cost variant. The second cost variant, representing in this example a
different mode of transport being used, will be simulated 20 times. Notably,
the environmental cost driver In-house mail differs from the standard
procedure, illustrating a different concretization in the respective process
instances. All other environmental cost drivers stay the same. Finally, the
digital-only variant completely differs in the concrete scores of the
environmental cost drivers, representing process instances where only digital
documents are used, which can also be seen in the cost of all environmental
cost drivers being different.

####  4.1.2 Environmental Cost Calculation

In the second step of SOPA (see Fig. 8), the environmental cost of business
processes is calculated based on event logs, which represent recorded
executions through information systems, in a manner that is similar to
existing ABC approaches. The objective here is, to gain insights into the
environmental impact of a process by considering its recorded process and
activity instances.

Figure 11 outlines how environmental costs of activity and process instances
and concrete environmental cost drivers relate. Their interrelation is the
basis for the environmental cost calculation of SOPA.

Figure 11: Metamodel of instance-related concepts of SOPA. Extensions
conceptualized in this work are shown with dashed borders

As discussed above, in the case of a model-based analysis, concrete LCA scores
for the concrete environmental cost drivers assigned to activity instances
during simulation configuration need to be provided. In the case of a log-
based analysis, sufficient information needs to be present in the log,
providing either concrete LCA scores for the activities, or information about
abstract environmental cost drivers and how they are concretized.

Having an event log that captures process executions, and LCA scores
expressing the environmental impact of individual environmental cost drivers
involved in activity execution, an ABC analysis can be conducted, to assess
the overall environmental impact of the process. For each instance of an
activity or a process, the respective cost (i.e., its environmental impact)
can be calculated. The following provides a formal definition of the concepts,
and how impact scores can be calculated.

As illustrated above in Listing LABEL:lst:xes-excerpt, event logs contain
recorded instances of processes, consisting of recorded activity executions.
In the case of SOPA, activities are instantiated and executed during process
execution, and each activity instance is potentially associated with certain
concrete environmental cost drivers. The overall possible set of activity
instances is determined by the combination of activities and sets of concrete
environmental cost drivers, as expressed in Definition 4.

######  Definition 4.

(Activity Instance) Let the set of activity instances $I$ be a set $I\subset
A\times\mathcal{P}(C)$, where each activity is associated with a set of
concrete environmental cost drivers. A single activity $i\in I$ therefore is a
pair $(a,Q)$, with $a\in A,\ Q\in\mathcal{P}(C)$.

For example, the environmental cost of an activity $a\in A$ could be
represented by the abstract cost driver $d\in D$, symbolizing the delivery of
a document via the in-house mail. If the cost driver hierarchy $H$ described
that $d$ could either be concretized into $c_{1}$ or $c_{2}$ $\in C$,
instances of activity $a$ would either occur with $c_{1}$ as
$(a,\\{c_{1}\\})$, or $(a,\\{c_{2}\\})$, representing the execution of $a$ at
runtime that incurs different costs based on the involved concrete cost
drivers $c_{1}$ and $c_{2}$. For instance, $c_{1}$ could represent the
delivery via a petrol-powered car over a distance of 750m with a parcel of 2
kg, while $c_{2}$ could represent the delivery of the same parcel over the
same distance with an electric bike. This, notably, also works for sets of
abstract respective concrete cost drivers associated with activities.

The assignment of concrete environmental cost drivers to an activity instance
is either done during process execution and recorded in an event log, or done
during simulation time — here, as described in the previous section, the
simulation configuration, cost variant, and environmental cost driver
hierarchy govern how activities in the model and the annotated abstract
environmental cost drivers are translated to activity instances and concrete
environmental cost drivers. For the simulation of a single process instance,
the cost variant is determined by the simulation configuration, which in turn
describes for each activity, how the annotated abstract environmental cost
drivers are turned into concrete ones. Since this is more of a technical
challenge than a conceptual problem, this translation is not formalized
further. Conversely, during actual execution, the concrete environmental cost
drivers can directly be recorded for all activity instances of the process
instance. This means that, following Definition 5, process instances are
sequences of activity instances (see also the relation between activity
instances and process instances in Fig. 11), and they either emerge during
execution or simulation of the process.

######  Definition 5.

(Process Instance) A process instance is a finite non-empty sequence of
activity instances, containing totally ordered pairs of activity instances and
sets of concrete environmental cost drivers. Thus, let $t\in I^{*}$ be a
single process instance, which contains one or more pairs $(a,Q)$.

Similar to the example above, process instances consist of sequences of
activity instances. Assume a process consisting of two activities $a,b\in A$,
where $a$ is associated with abstract cost drivers $d_{1},d_{2}\in D$, and $b$
with $d_{3}\in D$. If $d_{1},d_{2},d_{3}$ can be concretized into
$c_{1d_{1}},c_{2d_{1}},c_{1d_{2}},c_{2d_{2}},c_{3d_{2}},c_{1d_{3}}\in C$, then
$t_{1}=\langle(a,\\{c_{1d_{1}},c_{3d_{2}}\\}),(b,\\{c_{1d_{3}}\\})\rangle$ and
$t_{2}=\langle(a,\\{c_{2_{1}},c_{1d_{2}}\\}),(b,\\{c_{1d_{3}}\\})\rangle$ with
$t_{1},t_{2}\in I^{*}$ would be process instances that represent the concrete
environmental impact incurred at runtime by the various activities and hence,
the process instances.

Note that we rely on a formalization of sequences provided by [72] that
assumes that the sequence’s length can be known, elements can be retrieved,
and that sequences can contain the same element multiple times with an
explicit order, which we omit here for clarity. Moreover, we assume a total
order of all activity instances belonging to a process instance in line with
established formalizations of process instances. This allows i.a., process
mining algorithms to be applied to a set of process instances, which commonly
rely on totally ordered activity instances, see e.g. [72].

For each activity instance, the overall environmental impact can be
calculated, by considering all concrete environmental cost drivers of that
instance. Definition 6 describes this calculation.

######  Definition 6.

(Environmental Activity Instance Cost) Let $(a,Q)\in I$, consisting of an
activity $a\in A$ and a set of concrete environmental cost drivers
$Q\subset\mathcal{P}(C)$, be an activity instance. For each instance, the
environmental cost can be calculated with a function
$\mathit{activity\\_instance\\_cost}:I\rightarrow\mathbb{R}$ so that:

$\mathit{activity\\_instance\\_cost}(a,Q)=(\sum\limits_{q\in Q}cost(q))\ $

More concretely, consider an activity instance $(a,\\{c_{1},c_{2},c_{3}\\})$,
with $a\in A$, $c_{1},c_{2},c_{3}\in C$. If $cost(c_{1})=u$, $cost(c_{2})=v$,
and $cost(c_{3})=w$ with $u,v,w\in\mathbb{R}$, the overall environmental cost
of that activity instance $\mathit{activity\\_instance\\_cost}$ is equal to
the sum of all costs of the involved concrete cost drivers, being $u+v+w$.

Based on activity instance costs, the cost of a process instance can similarly
be calculated by summing up the costs of the involved activity instances,
following Definition 7.

######  Definition 7.

(Environmental Process Instance Cost) Let $T\subset I^{*}$ be a set of process
instances. Then, for a single process instance $t\in T$, the environmental
cost can be calculated with a function
$\mathit{process\\_instance\\_cost}:T\rightarrow\mathbb{R}$ so that:

$\mathit{process\\_instance\\_cost(t)}=\sum\limits_{(a,Q)\in
t}\mathit{activity\\_instance\\_cost}(a,Q)$

This provides a concrete numerical score of how impactful a single process
instance has been. However, not just individual instances of activities and
the overall process can thus be considered, but also the average environmental
instance cost of specific activities, respective the process itself, can be
calculated. This is further described in Definitions 8 and 9. Notably, a
collection of process instances is formalized as a multiset of process
instances – while traditionally (see e.g. [72]), they are formalized
differently, to be able to differentiate between instances based on e.g., the
case they represent, this differentiation is not needed here, and multiple
instances can be, considering just the involved activities and the concrete
environmental cost drivers, the same.

######  Definition 8.

(Average Environmental Activity Cost) Let $T\in\mathcal{B}(I^{*})$ be a
multiset of process instances, and $a\in A$ be an activity. Let
$\mathit{occurrence\\_count}:(A\times T)\rightarrow\mathbb{N}$ be a function
that counts the occurrences of $a$ in a sequence of activity instances $t\in
T$. Let $\mathit{specific\\_count}:(I\times T)\rightarrow\mathbb{N}$ be a
function that counts the occurrences of activity instances $a$ with a specific
set of concrete environmental cost drivers $Q$ in a sequence of activity
instances $t\in T$. Then, the average environmental cost of a specific
activity across all process instances can be calculated, and
$\mathit{average\\_activity\\_cost}:A\times\mathcal{P}(T)\rightarrow\mathbb{R}$
can be defined so that:

$\mathit{average\\_activity\\_cost}(a,T)=\frac{\sum\limits_{Q\in\mathcal{P}(C)}\sum\limits_{t\in
T}\mathit{specific\\_count}(a,Q,t)\cdot\mathit{activity\\_instance\\_cost}(a,Q)}{\sum\limits_{t\in
T}\mathit{occurrence\\_count}(a,t)}$

Described more concretely, by counting the activity instances of an activity
$a\in A$ with all combinations of possible concrete cost drivers, multiplying
this with the respective $\mathit{activity\\_instance\\_cost}$ and dividing by
the total number of occurrences of $a$, we can calculate the average cost of
$a$ across all instances, taking into account all possible concretizations of
the abstract cost drivers that have occurred.

The average environmental cost of all process instances can be calculated
similarly:

######  Definition 9.

(Average Environmental Process Instance Cost) Let $T\in\mathcal{B}(I^{*})$ be
a multiset of process instances. For this $T$, the average environmental cost
can be calculated with
$\mathit{average\\_process\\_instance\\_cost}:\mathcal{P}(T)\rightarrow\mathbb{R}$
so that:

$\mathit{average\\_process\\_instance\\_cost}(T)=\frac{\sum\limits_{t\in
T}\mathit{process\\_instance\\_cost}(t)}{|T|}$

These calculated costs, whose conceptual relation is outlined in Fig. 11, then
provide a notion of how costly activity instances, respective process
instances, can be expected to be, based on all recorded process executions and
the cost information they provide.

Using these costs, potential for improvement can be uncovered, and the
activity of process re-design as further described in the subsequent Section
4.1.3 can be conducted.

####  4.1.3 Process Re-Design

The third and final step of SOPA, as shown in Fig. 8, concerns the
sustainability-oriented process re-design. When a consideration of the
calculated environmental casts uncovers potential for improvement (e.g.,
disproportionally high average environmental costs of process instances,
specific average environmental costs of activities, or a reduction not being
as high as expected), the process can be re-designed accordingly and simulated
to test hypotheses on whether the re-design could actually improve, i.e.,
reduce, the environmental impact of the process under consideration.

Figure 12 shows the interplay between instance- and model-related concepts in
its entirety, underlining how activity and environmental process costs can be
calculated, based both on instance and modeled data.

Figure 12: Class diagram showing the entire metamodel of SOPA. Extensions
conceptualized in this work are shown with dashed borders. Entities in green
are provided during design time, entities in purple either emerge based on
probabilistic distribution during simulation time, or are instantiated during
process execution. Since concrete environmental cost drivers can both be
modeled and be part of activity instances, they are shown in both colors

Using these model- and instance-related concepts, SOPA can be used to conduct
business process re-design across three facets:

  1. 1.

Concrete environmental cost drivers can be changed, which represents an
adaption of the respective activities to use, for example, less impactful
materials.

  2. 2.

The simulated behavior can be altered (e.g., gateway probabilities),
representing a change in process execution, facilitated i.a. through changes
in the processes operational support.

  3. 3.

Both environmental cost drivers and simulated behavior can be changed
simultaneously, implying a causal relation between a new concrete
environmental cost driver and the behavior. This could, e.g., be the case when
the adaption of a process to use less impactful materials in one activity
leads to a higher probability of having to execute another activity. An
example could be a logistics process, where using structurally weaker
packaging material that is less impactful can lead to a higher probability of
needing to handle return shipments due to breakage, in turn leading to an
overall increase in impact.

After configuring a new simulation run, the resulting event log can be
analyzed once again, and the cost analyses can be compared. Either, a new re-
design iteration may be necessary, or the theoretical re-design can be
implemented in practice. Further, the process analyst could consider whether
the potential reduction in environmental impact may be too costly to
implement, and further tweak the re-design scenario to take economic
considerations into account.

###  4.2 System Development

To support the approach of SOPA, and to facilitate the case study-based
evaluation, a prototypical software system has been developed. This software
system realizes the overall metamodel of SOPA, as displayed in Fig. 12, and
allows for a practical application of SOPA’s application procedure of Fig. 8.
Note that the software system is not the central artifact, but rather a means
for evaluating it. The following briefly presents the respective components,
which combine modeling- and instance-related concepts.

The system’s architecture consists of multiple components used in concert by a
process analyst to elicit environmental impact information, consisting of
average process instance and activity environmental costs, of a business
process under investigation. Figure 13 illustrates the different components
that are involved. First, the process analyst can provide a process model
annotated with abstract environmental cost driver information and a simulation
configuration with cost variants to a fork of the process simulation engine
Scylla666See https://github.com/INSM-TUM/sustainability-scylla-extension
[Accessed: May 26, 2023] (see [55]), that has been modified to accommodate the
SOPA-specific concepts. The resulting (simulated) event log is used by the
Analysis Tool777See https://github.com/INSM-TUM/sustainability-analysis-tool
[Accessed: May 26, 2023], together with the simulation configuration
(containing cost variants and the corresponding concrete environmental cost
drivers) and process model provided to Scylla, to calculate the relevant
impact information. Alternatively, an event log containing the relevant
information that has been extracted out of a real-world information system
could be used as well. The impact information can then be used by the process
analyst to either put the hypothesized changes into practice, or to drive a
new re-design iteration.

Figure 13: Fundamental Modeling Concepts (FMC) [41] diagram displaying the
concrete architecture of the implementation supporting SOPA. FMC
differentiates between active and passive components. Active components such
as the Analysis Tool or the Process Analyst, shown in square, perform
activities in the system. Passive components such as the Process Model,
displayed in oval, store, contain, or transmit information

###  4.3 Case Study Evaluation

In the remainder of this section, SOPA is evaluated by being applied to the
university hiring process case study that is depicted in Fig. 7 through the
system outlined above. This constitutes a single-case mechanism experiment
[81]. Concretely, we aim to address the following evaluation goals:

  1. 1.

Show that the SOPA implementation allows a quantification of the environmental
impact of activities and process instances

  2. 2.

Show that SOPA can be used to assess the hypothetical reduction of
environmental impact incurred by re-designing a business process in various
ways

  3. 3.

Illustrate the holistic nature with which SOPA captures environmental impact

Since the hiring process is largely executed manually with neither an IT
system nor an IT system that logs environmental cost driver information, the
model-based variant of SOPA is being used here. The process model itself, as
well as the environmental cost drivers and the relevant LCA data have been
elicited through stakeholder interviews and utilization of LCA tooling and
databases, as described in Section 3.3.

##### Environmental Cost Drivers and Impact Data.

The following abstract environmental cost drivers, listed in Table 1, have
been identified through the stakeholder interviews:

Table 1: Abstract environmental cost drivers identified for the hiring process Abstract environmental cost driver |  Description
---|---
Request for job advertisement |  The cost of creating the documents required for requesting a job advertisement.
In-house mail |  The cost of transporting the documents between offices.
Advertisement |  The cost of creating the documents representing the job advertisement.
Sifting |  The cost of sifting through all applications.
Interview |  The cost of conducting a single job interview.
Application for employment |  The cost of creating the documents required for requesting the employment of a specific applicant.
Contract documents |  The cost of creating the documents of the final employment contract.

##### Simulation Parameters.

The following parameters, listed in Table 2, have been used for the
simulation. Further details can be found in the respective simulation
configuration files available online888See
https://doi.org/10.6084/M9.FIGSHARE.22591513 [Accessed: October 10, 2023].
Generally, based on stakeholder interviews, the content-and form related
assessments of all requests are assumed to be 95% successful, while only a
probability of 5% is assumed for cancellation of the entire process. With a
probability of 2%, the process fails due to a lack of suitable candidates.
With a probability of 50% each, three additional interviews may be conducted.
Further, the simulation is configured to simulate 500 process instances.
Notably, since inter-instance interaction is irrelevant in SOPA, no further
configuration of, e.g., instance arrival rates or activity durations is
necessary.

Table 2: Parameters used for process simulation Parameter | Probability
---|---
Probability of successful assessment in terms of content and form | 95%
Probability of overall cancellation | 5%
Probability of each additional interview | 50%
Probability of failure due to lack of candidates | 2%

##### Scenarios and Results.

Three scenarios have been identified and are evaluated SOPA in the following,
one representing the as-is process, and two representing potential re-designs
for environmental impact reduction. Table 3 describes how the different
environmental cost drivers are concretized in the scenario, and provides their
respective impact scores elicited with LCA, as described in Section 3.3.
Scenario A represents the as-is procedure: the required documents are created
digitally (assuming 2h of computer work per document), are printed on paper
(assuming 5g per page, the request for advertisement is known to average 8
pages, the advertisement 2 pages, the application for employment 20 pages, and
the contract documents 5 pages, printed with a non-specific black-and-white
laser printer) and transported between the different offices via in-house mail
by car (i.e., a 3.5 ton diesel lorry, emission class EUROIII). For all
delivery trips, an average distance of 750 m is assumed. The job interviews
are assumed to be conducted via video conference, for one hour. The sifting of
documents is assumed to take 6 hours and to be done with the aid of a
computer. Scenario B represents a hypothetical re-design scenario, in which
the in-house mail instead would be transported via an electric bicycle using
renewable energy and weighing 20 kg, averaging, as in scenario A, 750 m per
trip. Finally, scenario C also represents a hypothetical re-designed variant
of the hiring process where no documents would be printed and transported, but
instead would be transmitted digitally, discounting potential digital long-
term storage. File transmission is assumed to take, on average, one minute.
Further, the sifting of candidates would be less rigorous, taking 3h of
computer time instead of 6h. However, due to this less precise sifting, at
least two of the three additional interviews will be conducted.

Table 3: Scenarios and respective LCA sores of the concrete environmental cost drivers for evaluation with SOPA Environmental cost driver | Scenario A | Scenario B | Scenario C
---|---|---|---
Request for job advertisement | $2.89\times 10^{-5}$ | See A. | $1.95\times 10^{-5}$
In-house mail | $3.91\times 10^{-5}$ | $4.22\times 10^{-6}$ | $1.51\times 10^{-8}$
Advertisement | $2.19\times 10^{-5}$ | See A. | $1.95\times 10^{-5}$
Sifting | $5.85\times 10^{-5}$ | See A. | $2.93\times 10^{-5}$
Interview | $3.5\times 10^{-5}$ | See A. | See A.
Application for employment | $4.31\times 10^{-5}$ | See A. | $1.95\times 10^{-5}$
Contract documents | $2.54\times 10^{-5}$ | See A. | $1.95\times 10^{-5}$

Applying SOPA to the hiring process and the three identified scenarios,
results in impact information, consisting of average environmental activity
costs and average environmental process instance costs, which give a notion of
the impact of activity and process executions depending on the scenario. Table
4 provides a complete overview of the resulting impact information. Notably,
the average environmental cost per process instance decreases drastically in
the hypothetical re-design scenarios B and C, as shown in Figure 14.

Figure 14: Average environmental process instance costs of the hiring process
per scenario, elicited through SOPA

Switching the mode of transport of the in-house mail would incur an overall
impact reduction of $50.18\%$. Additionally, removing all paper-based
documents from the process, while reducing the time spent sifting applicants
at the cost of having to conduct at least four instead of two interviews,
would reduce the environmental impact by $65.08\%$ per process instance,
compared to the as-is impact. Further, Fig. 15 visualizes the average activity
scores per scenario, which are also provided in Table 4 and compared in Table
5, all to be found in Appendix A. Interestingly, we see that the reduction
across almost all cost drivers from Scenario B to C is somewhat compensated by
the fact that more interviews will need to be conducted. With this
information, process experts and decision makers now can reason about further
potential for process impact reduction, by either implementing scenario B or
C, or by further reducing the cost of individual activities, e.g., reducing
the maximum duration of the interviews, which constitutes one of the most
impactful activity across all two hypothetical re-design scenarios.
Additionally, Scenario C could be further revised, since the (economic) cost
of having to conduct more interviews and having to implement the
infrastructure for paperless applications might not warrant the increase in
environmental saving in comparison to Scenario B. Similarly, a combination of
Scenarios B and C, so that some process instances are done-paper less and
others not, could be explored, to further deliberate the feasibility of the
various scenarios.

Notably, this addresses evaluation goals 1 and 2, since we demonstrably are
able to quantify the impacts of process instances and activities, and
determine how they change across re-design scenarios. We have also illustrated
how a consideration of trade-offs between concrete cost drivers and process
behavior is possible with SOPA.

Given the various environmental cost drivers we identified in the case study,
and the resulting LCA scores, we have arguably also demonstrated the holistic
nature of SOPA: The environmental impact captured here, expressed in a unit-
less way, is multi-faceted and not just a set of indicators such as carbon
emissions or water use, used in other approaches. Thus, evaluation goal 3 is
addressed.

##  5 Discussion

After presenting SOPA and applying it to a case study, the following section
discusses the contribution of this paper and relevant threats to validity.
Afterward, the discussion is opened further, and the general context of SOPA
is discussed.

###  5.1 Results

Since the results of this work are twofold, the case study used for evaluating
this contribution will be discussed first, followed by the framework of SOPA
itself.

####  5.1.1 Case Study

The application of SOPA to a real-world case as a means of evaluating the
contribution and demonstrating its usefulness has indeed shown that SOPA is
applicable to assess the environmental impact of business processes and
subsequently re-design them. Hence, the practical demonstration addresses RQ2.
However, some threats to validity warrant a discussion: Firstly, the LCA
analysis has not been conducted by LCA experts, relies on existing LCA
databases and inventory databases, and has been supplemented with reasonable
assumptions due to limitations of the conducted interviews. Nonetheless, the
quality of the analysis is not highly substantial for the purpose of this case
study, which aims at demonstrating the general utility of SOPA — more exact
LCA data would only allow for more precise reasoning about re-design
potential, but does not impact the overall analysis procedure.

Secondly, the evaluation has been done based on simulated event logs, without
a real information system behind its execution. While the use of process
simulation instead of real execution data arguably reduces the accuracy of the
results, the use of process simulation nonetheless enables the analysis of
(potentially fully manual) processes for which no data has been recorded at
all, and thus still allows insights to be gained, as has been demonstrated in
the case study.

Moreover, while the use of process expert interviews and the conducted
analyses and subsequent simulation have limitations due to their scope, the
case study demonstrates that value could be added. While the results should be
interpreted carefully, investigating the process even more thoroughly and in
conjunction with LCA experts, the confidence in the results can only be
increased. Consequently, the case study shows that, generally, a better
elicitation of and reasoning about environmental impact is possible through
the application of SOPA to a business process.

####  5.1.2 SOPA

After considering the case study, the following discusses SOPA in general.

Generally, the case study has underlined the capabilities of SOPA to quantify
different process re-design measures and to help organizations to identify
those which have the greatest impact in reducing environmental costs. Further,
it has illustrated how SOPA can enable organizations to model, measure, and
optimize their environmental impact in a holistic manner. Thus, with proposing
the SOPA framework, we have answered RQ1. Nonetheless, some limitations need
to be considered here as well.

First, it should be noted that the quality of the results elicited with SOPA
depends on the completeness of the input, be it a simulated event log or an
extracted one — the more complete and close-to-real-world the captured
behavior and the impact information is, the more accurate the assessment with
SOPA is. To increase the exactness of simulation, stochastic information about
the process can either be estimated by process experts or identified based on
the analysis of process execution logs [82] to increase accuracy. The more in-
depth an analyst models the process (e.g., across multiple participants), the
more the impact can be attributed to different participants, attributing
concrete responsibilities.

Second, the use of LCA scores to compare the environmental impact of
activities and processes warrants a discussion. Notably, LCA results can
contain a lot of uncertainty, meaning that discrepancies between the actual
and the calculated impact may exist — this needs to be considered during the
interpretation phase. The discrepancies could stem, e.g., from erroneous,
misspecified, incomplete, or inconsistently elicited data. Relevant literature
has identified three main ways of dealing with that uncertainty, which could
be further integrated into SOPA: either by improving the quality of the data
and models involved, by finding a consensus on data and choices with
stakeholders and determining authoritative standards, or by applying
statistical methods to incorporate that uncertainty [18].

Third, the notion of process re-design that SOPA aims to enable is focused
primarily on changing either operational support or improving activity impacts
by identifying cost drivers and their impact on the overall process. This does
not cover structural re-design in which activities would be replaced or
removed, substantially altering the process. However, the authors maintain
that a SOPA-led analysis can be combined with other forms of process
performance analysis and re-design, allowing further re-design in practice.

Fourth, SOPA does not provide concrete actionable mechanisms to alleviate
conflicts between environmental, social, and economic impacts of the analyzed
business processes. These may emerge, e.g., when a potential reduction in
environmental impact analyzed with SOPA would come at substantial cost to the
economic viability of a business, or could harbor undesired social
consequences. As argued by [57], there is currently no consensus on how to,
formally, characterize sustainability and how its three facets interact.
Deciding on how to alleviate conflicts in a business setting (i.e., w.r.t the
economic facet of sustainability) requires value judgements [57], which cannot
be covered by a technical framework like SOPA. Further, some voices argue for
a general incommensurability (i.e., a conceptual difference that makes
calculating trade-offs complex or impossible), which would require non-
algorithmic multi-criteria frameworks beyond the scope of this work, see [48].
However, a data-driven approach such as SOPA may allow for a more evidence-
based reconciliation of conflicts and assessment of adherence to
sustainability-related regulations (e.g., [14]), which may continue to emerge.

Fifth, SOPA differs from other approaches for process optimization (especially
algorithmic approaches such as [43], [21] and [75]), where optimal process
designs or alternative execution scenarios are identified based on i.a. costs
of cases, activities, resources, the input-output relation of activities and
resources, and the runtime of activities. There, 1) commensurability between
various costs through a common cost unit (such as monetary costs in terms of
US-$) is assumed, and 2) a uni-directional relation between process parameters
and costs is assumed — changing process parameters leads to a change in costs,
which are to be optimized by way of changing the parameters. SOPA however aims
at measuring the environmental impact holistically, so that, as discussed
above, a comparison with e.g., other cost units is potentially infeasible, and
assumes a bidirectional relation between concrete cost drivers of activity
instances and process behavior, as illustrated in the third facet of process
re-design in Section 4.1.3. More concretely, the way in which cost drivers can
be changed is heavily constrained by expert knowledge and dependent upon LCA
analyses. Only after identifying potential for improvement, the process
experts can identify other concretizations of abstract cost drivers that could
lead to a reduction in impact and calculate their LCA values. This is due to
the fact that there might exist an interaction between specific environmental
cost drivers and the process behavior. This means that they cannot be
arbitrarily combined during actual process execution, but that instead expert
knowledge is needed to determine whether changes can or cannot be implemented,
and to what effect w.r.t. the behavior of the process. Further, changing the
probability of cost variants represents changing the implementation of the
business process, and therefore the feasibility of such an optimization needs
to be considered by the process expert. Consequently, reducing the
environmental impact itself is not just an algorithmic problem to which an
optimal solution w.r.t. the process domain can be derived, but one that also
requires an in-depth understanding and deliberation of the domain.

And finally, when considering other methods that aim at capturing impacts on
environmental sustainability (e.g., [12, 13]), the advantages and limitations
of SOPA need to be stressed: The scope of SOPA is bound by the scope of LCA –
other approaches focus only on individual enterprises or processes and are, as
opposed to SOPA and LCA, not intended to cross organizational boundaries.
Further, other approaches are focused on particular indicators that are, as
discussed before, unable to capture the entire environmental impact of
processes or organizations, as opposed to LCA. Notably, the inherent
capabilities for sustainability-oriented process re-design by leveraging
process simulation is a differentiator to other approaches, which assume a
static perspective, and do not provide explicit mechanisms for re-design.

###  5.2 General Discussion

Subsequently, after discussing SOPA, this section further opens the discussion
and considers the context of this work.

####  5.2.1 Sustainability through Optimization?

Firstly, while this work itself offers a tool for optimizing (i.e., by
reducing) the environmental impact of business processes, the idea behind
process optimization could be considered critically. When optimizing towards a
specific goal, in this case the reduction of environmental impact, the measure
with which this optimization is tracked should be discussed — any optimization
should always serve towards the end goal of impact reduction, and not just an
optimization for the metric’s sake [74]. The actual prevention therefore
depends on the context of application of SOPA, not just the application of
SOPA itself, in other words, in which manner SOPA is applied in practice: is
it just applied to reduce the impact scores as much as possible, or is it
applied to actually understand the environmental impact and reduce it
accordingly? This closely relates to rebound effects (see [29]), where,
through greater efficiency of e.g., a service, greater use is encouraged, and
consequently practically all benefits are absorbed. Also of relevance is the
discussion of energy efficiency provided by [66], where efficiency measures
need to be critically examined for what they measure, and how they perpetuate
the status quo, leading to path dependencies and preventing long-term
reduction of environmental impact.

####  5.2.2 Sustainability through BPM?

Moreover, the authors of this work would like to open a discussion on the
extent with which the tools and techniques of BPM are even applicable for
improving the sustainability of business processes.

As argued by [27], an increasing integration of environmental, social and
economic concerns led to the dispersion of an “economic mind-set” into the
governance of an increasing number of areas of life, thereby solidifying path
dependencies towards unsustainability. Therefore, a critical view might be
necessary: How much does the application of BPM in order to address problems
of environmental sustainability contribute to that solidification? This
warrants further research and discussion. Other areas, such as sustainability
transitions research (STR) [16] move towards critically examining the role of
capitalism (i.e., a specific form of social and economic organization going
beyond the economic sphere [16]) so that truly sustainable transitions can be
enabled. As a recent study about a large oil company has shown, accurate
scientific insights into climate change (e.g., accurate predictions of carbon
budget) were systematically withheld and miscommunicated to the outside [69],
thus hinting at how economic incentives might actively hinder sustainability
transformations.

Following [27], the governance of nature would require, instead of the
prevalent monetized cost-benefit analyses, multidimensional evidence.
Unlocking path dependencies towards sustainability arguably is hindered by
inequalities inherent to market logic, laws, and institutions, that are being
created, overlooked, or disguised through undifferentiated cost–benefit and
growth analyses [27]. Further, sustainability solutions may not lie in
increasing the efficiency of technologies and the effectiveness of economic
incentives, but instead may need to be facilitated by organizational setups
and sociocultural frameworks defining the purpose of technologies and economic
instruments [27]. Thus, while SOPA is a contribution within BPM to enable
further considerations of sustainability with it, the overall role of BPM
itself might need to be investigated closely for how it has internalized
mechanisms that might hinder adequately addressing unsustainable path
dependencies, and how it might be leveraged for sustainability solutions
within institutions.

One interesting avenue of research might be applying the research paradigm of
critical research (i.e., a paradigm aiming at critically examining a subject
and its context, and investigating its hidden limitations to change, imposed
by economic, political, and cultural authority [33]) to BPM, an approach that
has found some application in IS research. Moreover, Sachs (see [64]) proposes
the concept of sufficiency, focusing on deceleration (less speed and
efficiency), deglobalization (less fragmented cross-global productions chains
that externalize environmental impacts), decluttering, and decommodification
(moving away from commodified mental models focused on numbers and financial
benefits) [27], as alternatives to existing economic objectives, which might
be able to contribute towards a more sustainability-oriented derivative of
BPM.

Finally, it should be noted that, as the discussion above has made clear,
thorough considerations and even transformations of BPM and the context in
which it is applied are necessary for facing the grand challenge of addressing
sustainability — but they can only be achieved step by step. The authors would
like to underline that the contribution of SOPA could potentially constitute
such a step.

###  5.3 Future Work

Finally, this section considers future work and research opportunities
highlighted by this contribution. One interesting line of research would be,
how (un)certainty and confidence intervals could be included in the analysis
and simulation, to do the uncertainty inherent to LCA and process simulation
justice. This could indicate whether “the general direction of improvement” is
correct or not, instead of implying absolute certainty. Further, this
resembles the reality of process execution more closely, where variations in
impact exist due to minute fluctuations and variations in process execution,
while still providing a benefit for impact analysis (e.g., one process variant
is 30 to 33% less impactful than the other).

Moreover, a more systematic tracking of sustainability factors can ameliorate
both analysis results and simulation models: a “data mining for LCA” (e.g.,
based on sensor data) could form a data-driven basis for the analysis, by
either supplementing existing LCA information with sensor data recorded in IS,
or by mining relevant data from process executions to make LCA analysis more
exact. Further, the simulation could be made more precise by extracting
accurate simulation parameters based on observed behavior. A need for these
data-driven approaches is only underlined by the recent discussion about the
actual efficacy of carbon offset certificates999See
https://www.theguardian.com/environment/2023/jan/18/revealed-forest-carbon-
offsets-biggest-provider-worthless-verra-aoe and https://www.source-
material.org/vercompanies-carbon-offsetting-claims-inflated-methodologies-
flawed/ [Accessed: Feb. 6, 2023], where a surplus of emissions is still
caused, and the actual offset is not closely in relation to what is supposed
to be offset, due to limited traceability [78].

Furthermore, the exactness of LCA could be increased by encouraging
cooperation of manufactures and produces within and across industries to
enable sharing of more exact LCA values. While some industry-specific
databases have been emerging (see [18]), further proliferation can only be
helpful. Moreover, following the strength of LCA to assess impacts across
organizational boundaries, investigating how this approach could be extended
to process collaborations or choreographies could be fruitful. Notably, [70]
outlines a network-centric sharing system for environmental performance
indicators across enterprises, which could be extended with LCA databases and
results of SOPA analyses.

With respect to the other two dimensions of sustainability not yet addressed
by SOPA, a combination with other frameworks for process analysis and
optimization might be feasible, allowing for optimization towards, e.g.,
ecological and economic facets. Concretely, literature has proposed the so-
called Life Cycle Sustainability Assessment (LCSA), an extension of
traditional LCA that aims at incorporating all three facets of sustainability
in one indicator [17]. As to the discussion of Section 5.1.2, LCSA also
requires a weighting of the three dimensions of sustainability, and thus, a
value judgement. Integrating this approach with SOPA by including an explicit
non-technical mechanism for performing such a weighting could potentially
allow for considering all three facets of sustainability at once during
process optimization, and could be a promising avenue of research.

Additionally, [50] propose a model with which sustainability concerns are
explicitly extended to software products so that software products can be
developed, operated and used in a sustainable manner. This practice, also
known as green coding, could be employed to further the scope of analysis
around the analyzed business processes, by e.g., improving process engines or
other IT systems employed in process execution, especially when they
contribute heavily to execution of individual activities or processes.

Further, the emerging technique of object-centric event logs [22], where
instead of a flat stream of events belonging to individual cases, events
across several objects, are recorded without a singular case notion during
process execution, appears promising: that way, the impact of objects across
their life cycle and their contribution during process execution might be
captured even more precisely, closely relating to the life cycle aspect of
LCA. This, however, would require extended tool- and simulation support, given
the young nature of this technique.

Finally, the authors would like to underline that the area of business process
simulation is still an area of ongoing research, where continuous improvements
are being made — future improvements, so that process behavior can be
simulated even more accurately, can only be a positive contribution to SOPA.
Clearly, improving simulation leads to better results and analysis of the
environmental impact of business processes.

##  6 Conclusion

Given the accelerating human-made climate change, businesses have an
increasing need to assess, monitor, and improve the environmental impact of
their business processes. To this end, we have combined Life Cycle Assessment
and Activity-based Costing for Business Process Management purposes with a
Design Science Research-based approach. The resulting framework of SOPA fills
a gap in existing works, which we have found to be focussed on a limited set
of environmental indicators and limited in their capability to be used for
sustainability-oriented process re-design. We have applied SOPA to a case
study, and shown its potential, in conjunction with Business Process
Simulation, to contribute towards monitoring and re-designing a business
process to be less environmentally impactful. Moreover, we have discussed how
SOPA might benefit from additional environmental data and more accurate
simulation models. While SOPA provides a technical solution for environmental
impact analysis and re-design of business processes, the overall role of BPM
in contributing towards environmentally unsustainable business activities also
needs to be considered. SOPA is a way of integrating environmental
considerations into BPM, but its underlying mechanisms that might prevent even
more sustainable transformations warrant a critical investigation, in order to
transition towards a more sustainable future.

##  Appendix A Additional Figures

The following provides additional details of the data elicited during the
evaluation of SOPA.

Table 4: Environmental activity and process instance costs of the different scenarios evaluated with SOPA in Section 4.3 Parameter | Scenario A | Scenario B | Scenario C
---|---|---|---
Check contents of advertisement req. (SC) | $3.91\times 10^{-5}$ | $4.22\times 10^{-6}$ | $1.51\times 10^{-7}$
Check contents of hiring req. (SC) | $3.91\times 10^{-5}$ | $4.22\times 10^{-6}$ | $1.51\times 10^{-7}$
Formally assess hiring req. (HR) | $3.91\times 10^{-5}$ | $4.22\times 10^{-6}$ | $1.51\times 10^{-7}$
Formally assess hiring req. (Faculty) | $3.91\times 10^{-5}$ | $4.22\times 10^{-6}$ | $1.51\times 10^{-7}$
Finalize contract (HR) | $2.54\times 10^{-5}$ | $2.54\times 10^{-5}$ | $1.95\times 10^{-5}$
Submit request for job advertisement (Dep) | $2.89\times 10^{-5}$ | $2.89\times 10^{-5}$ | $1.95\times 10^{-5}$
Check contents of advertisement req. (DO) | $3.91\times 10^{-5}$ | $4.22\times 10^{-6}$ | $1.51\times 10^{-7}$
Sift and select candidates (Dep) | $5.85\times 10^{-5}$ | $5.85\times 10^{-5}$ | $2.93\times 10^{-5}$
Check contents of advertisement req. (WR) | $3.91\times 10^{-5}$ | $4.22\times 10^{-6}$ | $1.51\times 10^{-7}$
Submit job advertisement (HR) | $2.91\times 10^{-5}$ | $2.91\times 10^{-5}$ | $1.95\times 10^{-5}$
Check contents of hiring req. (DO) | $3.91\times 10^{-5}$ | $4.22\times 10^{-6}$ | $1.51\times 10^{-7}$
Formally assess advertisement req. (Faculty) | $3.91\times 10^{-5}$ | $4.22\times 10^{-6}$ | $1.51\times 10^{-7}$
Formally assess advertisement req. (HR) | $3.91\times 10^{-5}$ | $4.22\times 10^{-6}$ | $1.51\times 10^{-7}$
Check contents of hiring req. (WR) | $3.91\times 10^{-5}$ | $4.22\times 10^{-6}$ | $1.51\times 10^{-7}$
Request hiring of candidate (Dep) | $4.31\times 10^{-5}$ | $4.31\times 10^{-5}$ | $1.95\times 10^{-5}$
Conduct interview with candidate | $3.50\times 10^{-5}$ | $3.50\times 10^{-5}$ | $3.50\times 10^{-5}$
Average Environmental Process Instance Cost | $7.00\times 10^{-4}$ | $3.48\times 10^{-4}$ | $2.44\times 10^{-4}$
Table 5: Relative differences in environmental activity and process instance costs of the different scenarios in Section 4.3 Parameter | Scenario A — B | Scenario A — C
---|---|---
Check contents of advertisement req. (SC) | $-89.2\%$ | $-99.61\%$
Check contents of hiring req. (SC) | $-89.2\%$ | $-99.61\%$
Formally assess hiring req. (HR) | $-89.2\%$ | $-99.61\%$
Formally assess hiring req. (Faculty) | $-89.2\%$ | $-99.61\%$
Finalize contract (HR) | $0\%$ | $-23.22\%$
Submit request for job advertisement (Dep) | $0\%$ | $-32.52\%$
Check contents of advertisement req. (DO) | $-89.2\%$ | $-99.61\%$
Sift and select candidates (Dep) | $0\%$ | $-50.00\%$
Check contents of advertisement req. (WR) | $-89.2\%$ | $-99.61\%$
Submit job advertisement (HR) | $0\%$ | $-32.98\%$
Check contents of hiring req. (DO) | $-89.2\%$ | $-99.61\%$
Formally assess advertisement req. (Faculty) | $-89.2\%$ | $-99.61\%$
Formally assess advertisement req. (HR) | $-89.2\%$ | $-99.61\%$
Check contents of hiring req. (WR) | $-89.2\%$ | $-99.61\%$
Request hiring of candidate (Dep) | $0\%$ | $-54.75\%$
Conduct interview with candidate | $0\%$ | $0\%$
Average Environmental Process Instance Cost | $-50.18\%$ | $-65.08\%$

Figure 15: Bar chart displaying the average environmental activity costs per
activity and per scenario resulting from evaluating SOPA with the case study,
see Section 4.3

## References

  *   * Aguirre et al. [2013] Aguirre S, Parra C, Alvarado J (2013) Combination of Process Mining and Simulation Techniques for Business Process Redesign: A Methodological Approach. In: van der Aalst W, Mylopoulos J, Rosemann M, et al. (eds) Data-Driven Process Discovery and Analysis, vol 162. Springer, Berlin, Heidelberg, p 24–43, 10.1007/978-3-642-40919-6_2
  * Bauer [2008] Bauer S (2008) Leitbild der Nachhaltigen Entwicklung — bpb. Bundeszentrale für politische Bildung
  * vom Brocke et al. [2012] vom Brocke J, Seidel S, Recker J (eds) (2012) Green Business Process Management: Towards the Sustainable Enterprise. Springer, Berlin, Heidelberg, 10.1007/978-3-642-27488-6
  * Brooks et al. [2012] Brooks S, Wang X, Sarker S (2012) Unpacking Green IS: A Review of the Existing Literature and Directions for the Future. In: vom Brocke J, Seidel S, Recker J (eds) Green Business Process Management. Springer, Berlin, Heidelberg, p 15–37, 10.1007/978-3-642-27488-6_2
  * Brundtland [1987] Brundtland GH (1987) Our Common Future—Call for Action. Environmental Conservation 14(4):291–294. 10.1017/S0376892900016805
  * Cleven et al. [2012] Cleven A, Winter R, Wortmann F (2012) Managing Process Performance to Enable Corporate Sustainability: A Capability Maturity Model. In: vom Brocke J, Seidel S, Recker J (eds) Green Business Process Management. Springer, Berlin, Heidelberg, p 111–129, 10.1007/978-3-642-27488-6_7
  * Cooper and Kaplan [1991] Cooper R, Kaplan RS (1991) The Design of Cost Management Systems: Text, Cases, and Readings. Prentice Hall
  * de Bruijn et al. [2002] de Bruijn H, van Duin R, Huijbregts MAJ, et al. (eds) (2002) Handbook on Life Cycle Assessment: Operational Guide to the ISO Standards, Eco-Efficiency in Industry and Science, vol 7. Springer Netherlands, Dordrecht, 10.1007/0-306-48055-7
  * Díaz et al. [2019] Díaz SM, Settele J, Brondízio E, et al. (2019) The Global Assessment Report on Biodiversity and Ecosystem Services: Summary for Policy Makers. Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services
  * Dumas et al. [2018a] Dumas M, La Rosa M, Mendling J, et al. (2018a) Fundamentals of Business Process Management. Springer, Berlin, Heidelberg, 10.1007/978-3-662-56509-4
  * Dumas et al. [2018b] Dumas M, La Rosa M, Mendling J, et al. (2018b) Process Redesign, Springer, Berlin, Heidelberg, pp 297–339. 10.1007/978-3-662-56509-4_8
  * Elkington [1998] Elkington J (1998) ACCOUNTING FOR THE TRIPLE BOTTOM LINE. Measuring Business Excellence 2(3):18–22. 10.1108/eb025539
  * Ellram [1993] Ellram LM (1993) A Framework for Total Cost of Ownership. The International Journal of Logistics Management 4(2):49–60. 10.1108/09574099310804984
  * European Commission [2014] European Commission (2014) Directive 2014/95/EU of the European Parliament and of the Council of 22 October 2014 amending Directive 2013/34/EU as regards disclosure of non-financial and diversity information by certain large undertakings and groups
  * European Commission et al. [2018] European Commission, Joint Research Centre, Schau E, et al. (2018) Supporting Information to the Characterisation Factors of Recommended EF Life Cycle Impact Assessment Methods : New Methods and Differences with ILCD. Publications Office, 10.2760/671368
  * Feola [2020] Feola G (2020) Capitalism in sustainability transitions research: Time for a critical turn? Environmental Innovation and Societal Transitions 35:241–250. 10.1016/j.eist.2019.02.005
  * Finkbeiner et al. [2010] Finkbeiner M, Schau EM, Lehmann A, et al. (2010) Towards Life Cycle Sustainability Assessment. Sustainability 2(10):3309–3322. 10.3390/su2103309
  * Finnveden et al. [2009] Finnveden G, Hauschild MZ, Ekvall T, et al. (2009) Recent developments in Life Cycle Assessment. Journal of Environmental Management 91(1):1–21. 10.1016/j.jenvman.2009.06.018
  * Freitas and Pereira [2015] Freitas AP, Pereira JLM (2015) Process simulation support in BPM tools: The case of BPMN
  * Fritsch et al. [2022] Fritsch A, von Hammerstein J, Schreiber C, et al. (2022) Pathways to Greener Pastures: Research Opportunities to Integrate Life Cycle Assessment and Sustainable Business Process Management Based on a Systematic Tertiary Literature Review. Sustainability 14(18):11164. 10.3390/su141811164
  * Georgoulakos et al. [2017] Georgoulakos K, Vergidis K, Tsakalidis G, et al. (2017) Evolutionary Multi-Objective Optimization of business process designs with pre-processing. In: 2017 IEEE Congress on Evolutionary Computation (CEC). IEEE, Donostia, San Sebastián, Spain, pp 897–904, 10.1109/CEC.2017.7969404
  * Ghahfarokhi et al. [2021] Ghahfarokhi AF, Park G, Berti A, et al. (2021) OCEL: A Standard for Object-Centric Event Logs. In: Bellatreche L, Dumas M, Karras P, et al. (eds) New Trends in Database and Information Systems, vol 1450. Springer International Publishing, Cham, p 169–175, 10.1007/978-3-030-85082-1_16
  * Giddings et al. [2002] Giddings B, Hopwood B, O’Brien G (2002) Environment, economy and society: Fitting them together into sustainable development. Sustainable Development 10(4):187–196. 10.1002/sd.199
  * Glasson and Therivel [2019] Glasson J, Therivel R (2019) Introduction to Environmental Impact Assessment, fifth edition edn. Routledge, New York, 10.4324/9780429470738
  * Goebel et al. [1998] Goebel DJ, Marshall GW, Locander WB (1998) Activity-Based Costing. Industrial Marketing Management 27(6):497–510. 10.1016/S0019-8501(98)00005-4
  * Goedkoop [2007] Goedkoop M (2007) The Eco-indicator 99 Methodology. Journal of Life Cycle Assessment, Japan 3(1):32–38. 10.3370/lca.3.32
  * Göpel [2016] Göpel M (2016) The Great Mindshift, The Anthropocene: Politik—Economics—Society—Science, vol 2. Springer International Publishing, Cham, 10.1007/978-3-319-43766-8
  * Hernández González et al. [2019] Hernández González A, Calero C, Pérez Parra D, et al. (2019) Approaching Green BPM characterisation. Journal of Software: Evolution and Process 31(2):2145. 10.1002/smr.2145
  * Herring [2006] Herring H (2006) Energy efficiency—a critical view. Energy 31(1):10–20. 10.1016/j.energy.2004.04.055
  * Houy et al. [2011] Houy C, Reiter M, Fettke P, et al. (2011) Towards Green BPM – Sustainability and Resource Efficiency through Business Process Management. In: zur Muehlen M, Su J (eds) Business Process Management Workshops, vol 66. Springer, Berlin, Heidelberg, p 501–510, 10.1007/978-3-642-20511-8_46
  * Houy et al. [2012] Houy C, Reiter M, Fettke P, et al. (2012) Advancing Business Process Technology for Humanity: Opportunities and Challenges of Green BPM for Sustainable Business Activities. In: Green Business Process Management. Springer, Berlin, Heidelberg, p 75–92, 10.1007/978-3-642-27488-6_5
  * Hovorka et al. [2012] Hovorka DS, Labajo E, Auerbach N (2012) Information Systems in Environmental Sustainability: Of Cannibals and Forks. In: vom Brocke J, Seidel S, Recker J (eds) Green Business Process Management. Springer, Berlin, Heidelberg, p 59–72, 10.1007/978-3-642-27488-6_4
  * Howcroft and Trauth [2005] Howcroft D, Trauth E (2005) Handbook of Critical Information Systems Research. Edward Elgar Publishing, 10.4337/9781845426743
  * Hueting and Reijnders [2004] Hueting R, Reijnders L (2004) Broad sustainability contra sustainability: The proper construction of sustainability indicators. Ecological Economics 50(3-4):249–260. 10.1016/j.ecolecon.2004.03.031
  * IEEE [2016] IEEE (2016) IEEE Standard for eXtensible Event Stream (XES) for Achieving Interoperability in Event Logs and Event Streams. 10.1109/IEEESTD.2016.7740858
  * Ihde et al. [2022] Ihde S, Pufahl L, Völker M, et al. (2022) A framework for modeling and executing task-Specific resource allocations in business processes. Computing 104(11):2405–2429. 10.1007/s00607-022-01093-2
  * IPCC [2015] IPCC (2015) Climate Change 2014: Mitigation of Climate Change: Working Group III Contribution to the IPCC Fifth Assessment Report. Cambridge University Press, Cambridge, 10.1017/CBO9781107415416
  * ISO [2006] ISO (2006) ISO 14040: 2006 - Environmental Management—Life Cycle Assessment—Principles and Framework. ISO
  * Johnston et al. [2007] Johnston P, Everard M, Santillo D, et al. (2007) Reclaiming the definition of sustainability. Environmental science and pollution research international 14(1):60–6. 10.1065/espr2007.01.375
  * Klöpffer et al. [2014] Klöpffer W, Grahl B, Klöpffer W (2014) Life Cycle Assessment (LCA): A Guide to Best Practice. Wiley-VCHVerlag GmbH & Co. KGaA, Weinheim
  * Knöpfel et al. [2005] Knöpfel A, Gröne B, Tabeling P (2005) Fundamental Modeling Concepts: Effective Communication of IT Systems. J. Wiley & Sons, Chichester ; Hoboken, NJ
  * Lee and Kao [2001] Lee TR, Kao JS (2001) Application of simulation technique to activity-based costing of agricultural systems: A case study. Agricultural Systems 67(2):71–82. 10.1016/s0308-521x(00)00042-1
  * Low et al. [2016] Low WZ, Vanden Broucke SKLM, Wynn MT, et al. (2016) Revising history for cost-informed process improvement. Computing 98(9):895–921. 10.1007/s00607-015-0478-1
  * Lübbecke et al. [2018a] Lübbecke P, Fettke P, Loos P (2018a) Towards Guidelines of Modeling for Ecology-Aware Process Design. In: Teniente E, Weidlich M (eds) Business Process Management Workshops, vol 308. Springer International Publishing, Cham, p 510–519, 10.1007/978-3-319-74030-0_40
  * Lübbecke et al. [2018b] Lübbecke P, Goswami A, Fettke P (2018b) A Method for Ecological Process Optimization Based on Compliance Checking. In: 2018 IEEE 20th Conference on Business Informatics (CBI). IEEE, Vienna, pp 119–128, 10.1109/CBI.2018.00022
  * Mansar and Reijers [2005] Mansar SL, Reijers HA (2005) Best practices in business process redesign: Validation of a redesign framework. Computers in Industry 56(5):457–471. 10.1016/j.compind.2005.01.001
  * Martin et al. [2014] Martin N, Depaire B, Caris A (2014) The use of process mining in a business process simulation context: Overview and challenges. In: 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). IEEE, Orlando, FL, USA, pp 381–388, 10.1109/CIDM.2014.7008693
  * Martinez-Alier et al. [1998] Martinez-Alier J, Munda G, O’Neill J (1998) Weak comparability of values as a foundation for ecological economics. Ecological Economics 26(3):277–286. 10.1016/S0921-8009(97)00120-1
  * Meredith et al. [1989] Meredith JR, Raturi A, Amoako-Gyampah K, et al. (1989) Alternative research paradigms in operations. Journal of Operations Management 8(4):297–326. 10.1016/0272-6963(89)90033-8
  * Naumann et al. [2011] Naumann S, Dick M, Kern E, et al. (2011) The GREENSOFT Model: A reference model for green and sustainable software and its engineering. Sustainable Computing: Informatics and Systems 1(4):294–304. 10.1016/j.suscom.2011.06.004
  * Nowak et al. [2011] Nowak A, Leymann F, Schumm D, et al. (2011) An Architecture and Methodology for a Four-Phased Approach to Green Business Process Reengineering. In: Kranzlmüller D, Toja AM (eds) Information and Communication on Technology for the Fight against Global Warming, vol 6868. Springer, Berlin, Heidelberg, p 150–164, 10.1007/978-3-642-23447-7_14
  * Nunamaker and Chen [1990] Nunamaker J, Chen M (1990) Systems development in information systems research. In: Twenty-Third Annual Hawaii International Conference on System Sciences, vol iii. IEEE Comput. Soc. Press, Kailua-Kona, HI, USA, pp 631–640, 10.1109/HICSS.1990.205401
  * OMG [2011] OMG (2011) Business process model and notation (BPMN), version 2.0
  * Peffers et al. [2007] Peffers K, Tuunanen T, Rothenberger MA, et al. (2007) A Design Science Research Methodology for Information Systems Research. Journal of Management Information Systems 24(3):45–77. 10.2753/MIS0742-1222240302
  * Pufahl et al. [2018] Pufahl L, Wong TY, Weske M (2018) Design of an Extensible BPMN Process Simulator. In: Teniente E, Weidlich M (eds) Business Process Management Workshops, vol 308. Springer International Publishing, Cham, p 782–795, 10.1007/978-3-319-74030-0_62
  * Pufahl et al. [2022] Pufahl L, Zerbato F, Weber B, et al. (2022) BPMN in healthcare: Challenges and best practices. Information Systems 107:102013. 10.1016/j.is.2022.102013
  * Purvis et al. [2019] Purvis B, Mao Y, Robinson D (2019) Three pillars of sustainability: In search of conceptual origins. Sustainability Science 14(3):681–695. 10.1007/s11625-018-0627-5
  * Recker [2013] Recker J (2013) Scientific Research in Information Systems: A Beginner’s Guide. Springer, Berlin, Heidelberg, 10.1007/978-3-642-30048-6
  * Recker et al. [2011] Recker J, Rosemann M, Gohar ER (2011) Measuring the Carbon Footprint of Business Processes. In: Business Process Management Workshops, vol 66. Springer, Berlin, Heidelberg, p 511–520, 10.1007/978-3-642-20511-8_47
  * Recker et al. [2012] Recker J, Rosemann M, Hjalmarsson A, et al. (2012) Modeling and Analyzing the Carbon Footprint of Business Processes. In: vom Brocke J, Seidel S, Recker J (eds) Green Business Process Management. Springer, Berlin, Heidelberg, p 93–109, 10.1007/978-3-642-27488-6_6
  * Romanelli et al. [2015] Romanelli C, Cooper D, Campbell-Lendrum D, et al. (2015) Connecting Global Priorities: Biodiversity and Human Health: A State of Knowledge Review. World Health Organistion / Secretariat of the UN Convention on Biological Diversity
  * Roohy Gohar and Indulska [2020] Roohy Gohar S, Indulska M (2020) Environmental Sustainability through Green Business Process Management. Australasian Journal of Information Systems 24. 10.3127/ajis.v24i0.2057
  * Satyal et al. [2019] Satyal S, Weber I, Paik H, et al. (2019) Business process improvement with the AB-BPM methodology. Information Systems 84:283–298. 10.1016/j.is.2018.06.007, URL https://www.researchgate.net/publication/326256699_Business_Process_Improvement_with_the_AB-BPM_Methodology
  * Schneidewind and Zahrnt [2014] Schneidewind U, Zahrnt A (2014) The institutional framework for a sufficiency driven economy. Ökologisches Wirtschaften - Fachzeitschrift 29(3):30. 10.14512/OEW290330
  * Seidel et al. [2012] Seidel S, Recker J, vom Brocke J (2012) Green Business Process Management. In: vom Brocke J, Seidel S, Recker J (eds) Green Business Process Management. Springer, Berlin, Heidelberg, p 3–13, 10.1007/978-3-642-27488-6_1
  * Shove [2018] Shove E (2018) What is wrong with energy efficiency? Building Research & Information 46(7):779–789. 10.1080/09613218.2017.1361746
  * Sohns et al. [2023] Sohns TM, Aysolmaz B, Figge L, et al. (2023) Green business process management for business sustainability: A case study of manufacturing small and medium-sized enterprises (SMEs) from Germany. Journal of Cleaner Production 401:136667. 10.1016/j.jclepro.2023.136667
  * Stolze et al. [2012] Stolze C, Semmler G, Thomas O (2012) Sustainability in Business Process Management Research–a Literature Review. AMCIS 2012 Proceedings
  * Supran et al. [2023] Supran G, Rahmstorf S, Oreskes N (2023) Assessing ExxonMobil’s global warming projections. Science 379(6628):eabk0063. 10.1126/science.abk0063
  * Thies et al. [2012] Thies H, Dada A, Stanoevska-Slabeva K (2012) The Potential of a Network-Centric Solution for Sustainability in Business Processes. In: vom Brocke J, Seidel S, Recker J (eds) Green Business Process Management. Springer, Berlin, Heidelberg, p 181–201, 10.1007/978-3-642-27488-6_11
  * UN Environment [2019] UN Environment (ed) (2019) Global Environment Outlook – GEO-6: Summary for Policymakers. Cambridge University Press, Cambridge, 10.1017/9781108639217
  * van der Aalst [2016] van der Aalst W (2016) Process Mining. Springer, Berlin, Heidelberg, 10.1007/978-3-662-49851-4
  * van der Aalst et al. [2010] van der Aalst W, Nakatumba J, Rozinat A, et al. (2010) Business process simulation. In: vom Brocke J, Rosemann M (eds) Handbook on Business Process Management 1. International Handbooks on Information Systems, Springer, p 313–338, 10.1007/978-3-642-00416-2
  * van der Kolk [2022] van der Kolk B (2022) Numbers Speak for Themselves, or Do They? On Performance Measurement and Its Implications. Business & Society 61(4):813–817. 10.1177/00076503211068433
  * Vergidis and Tiwari [2008] Vergidis K, Tiwari A (2008) Business process design and attribute optimization within an evolutionary framework. In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence). IEEE, Hong Kong, China, pp 668–675, 10.1109/CEC.2008.4630867
  * Wernet et al. [2016] Wernet G, Bauer C, Steubing B, et al. (2016) The ecoinvent database version 3 (part I): Overview and methodology. The International Journal of Life Cycle Assessment 21(9):1218–1230. 10.1007/s11367-016-1087-8
  * Weske [2012] Weske M (2012) Business Process Management - Concepts, Languages, Architectures, 2nd edn. Springer, Berlin, Heidelberg, 10.1007/978-3-642-28616-2
  * West et al. [2023] West TAP, Wunder S, Sills EO, et al. (2023) Action needed to make carbon offsets from tropical forest conservation work for climate change mitigation. 10.48550/ARXIV.2301.03354
  * Wesumperuma et al. [2011] Wesumperuma A, Ginige JA, Ginige A, et al. (2011) A Framework for Multi-dimensional Business Process Optimization for GHG Emission Mitigation. ACIS 2011 Proceedings 91
  * Wesumperuma et al. [2013] Wesumperuma A, Ginige A, Ginige JA, et al. (2013) Green activity based management (ABM) for organisations. ACIS 2013 Proceedings
  * Wieringa [2014] Wieringa RJ (2014) Design Science Methodology for Information Systems and Software Engineering. Springer, Berlin, Heidelberg, 10.1007/978-3-662-43839-8
  * Wynn et al. [2008] Wynn MT, Dumas M, Fidge CJ, et al. (2008) Business Process Simulation for Operational Decision Support. In: Hutchison D, Kanade T, Kittler J, et al. (eds) Business Process Management Workshops, vol 4928. Springer, Berlin, Heidelberg, p 66–77, 10.1007/978-3-540-78238-4_8
  * Zeise et al. [2012] Zeise N, Link M, Ortner E (2012) Measurement Systems for Sustainability. In: vom Brocke J, Seidel S, Recker J (eds) Green Business Process Management. Springer, Berlin, Heidelberg, p 131–146, 10.1007/978-3-642-27488-6_8