Evaluating the Use of the Open Trip Model for Process Mining:
An Informal Conceptual Mapping Study in Logistics
Jean Paul Sebastian Piest
, Jennifer Alice Cutinha
, Rob Henk Bemthuis
and Faiza Allah Bukhsh
University of Twente, Drienerlolaan 5, 7522 NB, Enschede, The Netherlands
Open Trip Model, Process Mining, Logistics, Event Data.
When aggregating logistic event data from different supply chain actors and information systems for process
mining, interoperability, data loss, and data quality are common challenges. This position paper proposes and
evaluates the use of the Open Trip Model (OTM) for process mining. Inspired by the current industrial use
of the OTM for reporting and business intelligence, we believe that the data model of OTM can be utilized
for unified storage, integration, interoperability, and querying of logistic event data. Therefore, the OTM data
model is mapped to a generic event log structure to satisfy the minimum requirements for process mining.
A demonstrative scenario is used to show how event data can be extracted from the OTM’s default scenario
dataset to create an event log as the starting point for process mining. Thus, this approach provides a foundation
for future research about interoperability challenges and unifying process mining models based on industry
standards, and a starting point for developing process mining applications in the logistics industry.
The logistics sector can be referred to as a network
where multiple organizations come together for the
planning, organization, coordination, and execution
of transportation of goods and logistics services. Typ-
ically, a logistic process involves multiple parties
(e.g., shippers, logistics service providers, transport
operators, or carriers), different entities within an or-
ganization, and is spread across different countries.
As a result, logistic processes are exceedingly com-
plex and dynamic, and data usually comes from het-
erogeneous data sources in various (un)structured for-
mats (Intayoad and Becker, 2018).
Shipment data are commonly administered in
multiple information systems (e.g., ERP, WMS, TMS,
and FMS) (Evofenedex, TLN, and Beurtvaartadres,
2019). As a result of business transactions, the status
of shipments and whereabouts of goods are tracked
and traced during the physical handling processes.
Data are exchanged in different formats (e.g., e-mail
and EDI) and supported by industry standards and in-
teroperability models (Evofenedex, TLN, and Beurt-
vaartadres, 2019).
The Open Trip Model (OTM) is such a data ex-
change standard and adopted by the Dutch logistics
industry as part of a federated data sharing infras-
tructure (Bastiaansen et al., 2020). Figure 1 depicts
the default OTM scenario for data exchange and data
sharing between involved stakeholders and their in-
formation systems.
Figure 1: Interaction of multiple logistic parties (adapted
from OTM presentation).
In the industry, the OTM data model is also used for
reporting and business intelligence, as illustrated in
Figure 1, however, to our best knowledge, not for
process mining. Process mining focuses on extract-
ing knowledge from data generated and stored in the
Piest, J., Cutinha, J., Bemthuis, R. and Bukhsh, F.
Evaluating the Use of the Open Trip Model for Process Mining: An Informal Conceptual Mapping Study in Logistics.
DOI: 10.5220/0010477702900296
In Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS 2021) - Volume 1, pages 290-296
ISBN: 978-989-758-509-8
2021 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
databases of information systems in the form of event
logs (Van der Aalst et al., 2012). Logistic processes
generate large amounts of event data. Event data are
expected to be a rich source for behavior analysis as
it comprises data concerning the dynamic behavior of
people, objects, and systems at a detailed level. Pro-
cess mining techniques can be helpful to produce in-
sightful information based on logistic event data.
When event data are aggregated from different
systems for process mining, interoperability, data
loss, and data quality issues are common challenges.
Existing approaches aim to increase the accuracy of
process mining techniques despite noisy data. Con-
sequently, various tools and algorithms have been
built in process mining tools to eliminate the effect
of noisy data and determine the actual control-flow
of a process. However, as logistic event data are typi-
cally complex, dynamic, and heterogenous, it remains
challenging to generalize their results (Intayoad and
Becker, 2018). More precisely, much of the current
literature pays little attention to unified standards of
logistic process definitions. The OTM provides such a
standard for sharing logistic data. We believe that the
usage of the OTM, in addition to addressing (some)
interoperability issues, can provide a promising foun-
dation for a more unified implementation of process
mining practices in the logistics domain and its het-
erogeneous environment.
This paper aims to evaluate the use of the OTM
for process mining. An informal conceptual mapping
study is conducted to determine whether the basic re-
quirements for process mining can be fulfilled. More
specifically, the data model of the OTM is mapped
to a generic event log structure. Based on a step-by-
step walkthrough, we demonstrate how the OTM data
model can be used to extract event data and create an
event log. The event data are imported in the pro-
cess mining tool Disco to generate a process model.
This way, we provide researchers a common ground
for solving interoperability issues related to process
mining in heterogeneous environments using industry
standards, including OTM, and practitioners a ded-
icated, potentially generalizable, process model and
approach to develop process mining applications.
The remainder of this paper proceeds as follows.
Section 2 discusses related work. Section 3 positions
the use OTM for process mining. Section 4 discusses
preliminary results. Section 5 concludes and provides
an outlook for future research.
The practice of process mining has gained attention in
many domains, such as healthcare (Mans et al., 2012;
Erdogan and Tarhan, 2018), education (Bogarín et al.,
2018), finance (De Weerdt et al., 2013), logistic, and
supply chain processes.
2.1 Process Mining in Logistics
There are several studies available on process mining
in the logistics domain.
A systematic mapping study (dos Santos Garcia
et al., 2019) illustrates that less than 5% of their pa-
per sample is about the logistics domain. This map-
ping study identified 27 studies with a focus on lo-
gistic processes, including transportation, storage of
goods, and stock management. Most of the studies
focus on process discovery in the logistical context.
Specific studies study process mining in regard to net-
work analysis, resource configuration, prediction of
event times, and remodeling of business processes.
These studies indicate a rich spectrum of use cases.
Additional studies examined logistic processes
through process mining, mainly focusing on the inter-
nal logistics of case-specific scenarios (Knoll et al.,
2019a; Knoll et al., 2019b). Others developed a pro-
cess mining system for determining the root causes
of quality problems in a supply chain (Lau et al.,
2009). Based on daily captured logistic data, the au-
thors fine-tuned configuration parameters to improve
operational performance.
2.2 Interoperability Challenges
There are relevant studies in the logistics domain that
address the interoperability challenges using process
mining techniques and the need for standardization.
On a high abstraction level, the interoperability is-
sues are addressed in the four levels of big data in-
teroperability (Singh and van Sinderen, 2016). More
specifically, the study of (Lont et al., 2018) shows
how different systems and devices can be linked to
the data model of OTM, eliminating certain interop-
erability issues.
Some studies emphasize the complexity of mon-
itoring logistic processes (Cabanillas et al., 2013;
Wang et al., 2014). The authors pinpoint the im-
portance of new research, novel contributions on dis-
cretizing, aggregating, and correlating events in a way
that the overall business process can be better traced.
This work indicates that further research should be
done on improving the quality of the event log data
by including a reconciliation of the data.
Evaluating the Use of the Open Trip Model for Process Mining: An Informal Conceptual Mapping Study in Logistics
3.1 Aim and Data Model of OTM
The OTM is an open-source, flexible data sharing
model that contributes to uniform and consistent ex-
change of information across various information sys-
tems. This model is managed by the Stichting Uni-
forme Transport Code (SUTC) and its goal is to help
logistics companies in the Netherlands share real-time
logistic data efficiently (Stichting Uniforme Transport
Code (SUTC), 2019).
The constructed data model of the OTM, as shown
in Figure 2, is centered around event data and consid-
ers eight entities and four lifecycles.
Figure 2: OTM data model (Open Trip Model, 2020).
Entities are used to represent various objects within a
logistic process, e.g., vehicles. All dynamic behavior
of actors is modeled as (a series of) event(s) and are
related to shipments, trips, and routes. The order of
these events indicates the workflow over time, and this
is depicted by the lifecycle. This way, a trail of event
data is created. The lifecycle expresses the different
phases in the transport process and enables different
views on the operation (e.g., look ahead at events that
have been planned, what is taking place right now
or look back at what has been realized). Event data,
together with related entities and the lifecycles, pro-
vide the foundation to develop process mining appli-
cations, behavioral analysis, and performance man-
3.2 Using OTM for Process Mining
In addition to data exchange, the data model of the
OTM can be used for storage, integration, and query-
ing of logistic event data originating from multiple in-
formation systems as the foundation for process min-
ing use cases relevant to various stakeholders in the
logistics industry. The event log should contain four
data elements to fulfill the minimal requirements for
process mining, namely, the case id, which represents
the process instance, the names of the events or activ-
ities in the process, the timestamps of the events, and
the resource that conducted the activity (Van der Aalst
et al., 2012).
3.3 Evaluating and Demonstrating the
Use of OTM for Process Mining
The OTM walkthrough scenario and example data de-
scribed on the website (Open Trip Model, 2020) are
used to evaluate the use of the OTM for process min-
ing. Figure 3 shows how version 4.2 of the data model
of the OTM can be mapped to satisfy the minimum
process mining requirements.
Figure 3: Adapted OTM data model version 4.2 linked to
the minimum process mining requirements.
In the following part, we will walk through an op-
erational logistics scenario and discuss how this sce-
nario can be expressed in OTM entities and events,
and eventually used for process mining.
3.3.1 Describe the Scenario
The example scenario is related to the logistics oper-
ation of a supermarket chain that contracts transport
companies to transport goods from their warehouse
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems
to the stores. Only a limited part of the operations is
discussed here, as that will be enough to highlight the
most important concepts of OTM.
The OTM scenario (Open Trip Model, 2020) starts
with the planning department of the supermarket
chain, sending the following instructions to a con-
tracted transport company:
I have a transportation request, requiring a
diesel-powered boxtruck to transport refrigerated
goods from “warehouse A”, dock 14 (start loading
at 6:15 AM, 30 minutes loading time) to two stores:
“store B”, where there is 30 minutes unloading time
and “store C”. From store C some goods must be re-
turned to “terminal D" at the warehouse location. At
store C there’s also 30 minutes for loading and un-
loading. According to our route planner, you should
be able to go from A to B, B to C and C to D in a
given time and by driving no more than the given dis-
tance. The set access routes to the stores are taken
into account.
3.3.2 Identify and Map Entities
We can identify the following OTM entities in this
text: vehicle, shipment, location, route, and trip. The
scenario contains a refrigerated boxtruck, which can
be mapped to the vehicle entity as follows:
id: 1
type: refrigerated boxtruck
fueltype: diesel
The scenario informally describes three types of
goods that need to be transported and can be mapped
to the shipment entity as follows:
id: 1
contents: refrigerated goods
from: location A
to: location B
id: 2
contents: refrigerated goods
from: location A
to: location C
id: 3
contents: returned goods
from: location C
to: location D
The shipments are transported to four locations and
can be mapped to the location entity as follows:
id: A
name: warehouse A, dock 14
id: B
name: store B, loading bay
id: C
name: store C, loading bay
id: D
name: returned goods, terminal D
The route and trip are informally formulated and con-
sidered identical in this scenario. Event data typically
originates from multiple actors and systems.
3.3.3 Identification of Events
Based on the identification and mapping of entities
from the text, the following events can be identified:
Planned event on Trip 1:
start loading 6:15 AM
Planned event on Vehicle 1:
load Shipment 2
Planned event on Vehicle 1:
load Shipment 1
Planned event on Trip 1:
stop loading 6:45 AM
Planned event on Trip 1:
start driving from A 6:45 AM
Planned event on Trip 1:
stop driving at B 7:45 AM
Planned event on Trip 2:
start loading/unloading 7:45 AM
Planned event on Vehicle 1:
unload Shipment 1
Planned event on Trip 2:
stop loading/unloading 8:15 AM
Planned event on Trip 2:
start driving from B 8:15 AM
Planned event on Trip 2:
stop driving at C 9:15 AM
Planned event on Trip 3:
start loading/unloading 9:15 AM
Planned event on Vehicle 1:
unload Shipment 2
Planned event on Vehicle 1:
load Shipment 3
Planned event on Trip 3:
stop loading/unloading 9:45 AM
Planned event on Trip 3:
start driving from C 9:45 AM
Planned event on Trip 3:
stop driving at D 10:45 AM
Planned event on Trip 3:
start loading/unloading 10:45 AM
Planned event on Vehicle 1:
unload Shipment 3
Planned event on Trip 3:
stop loading/unloading 11:05 AM
3.3.4 Extract the Event Data
Based on the identified entities and events, event data
are created based on the scenario. Table 1 presents
Evaluating the Use of the Open Trip Model for Process Mining: An Informal Conceptual Mapping Study in Logistics
Table 1: Event Log Created based on the OTM Walkthrough and Example Data.
Shipment Trip Activity Start time End time From To Vehicle
1 1 Loading 6-1-2021 06:15 6-1-2021 06:45 A A 1
2 1 Loading 6-1-2021 06:15 6-1-2021 06:45 A A 1
1 1 Driving 6-1-2021 06:45 6-1-2021 07:45 A B 1
2 1 Driving 6-1-2021 06:45 6-1-2021 07:45 A B 1
1 1 Unloading 6-1-2021 07:45 6-1-2021 08:15 B B 1
2 2 Driving 6-1-2021 08:15 6-1-2021 09:15 B C 1
2 2 Unloading 6-1-2021 09:15 6-1-2021 09:45 C C 1
3 3 Loading 6-1-2021 09:15 6-1-2021 09:45 C C 1
3 3 Driving 6-1-2021 09:45 6-1-2021 10:45 C D 1
3 3 Unloading 6-1-2021 10:45 6-1-2021 11:05 D C 1
the test dataset. The dataset contains three shipments,
three trips to visit three locations, three types of activ-
ities, planned start and end times, three locations, and
an assigned vehicle.
3.3.5 Create the Event Log
We used the process mining tool Disco (developed by
Fluxicon) to import the event data using the following
Import column mapping:
‘Shipment’ Case ID
‘Activity’ Activity
‘Start time’ Timestamp
(Pattern: ‘yyyy/MM/dd HH:mm:ss’)
‘End time’ Timestamp
(Pattern: ‘yyyy/MM/dd HH:mm:ss’)
‘Vehicle’ Resource
Based on the import script, the event log can be cre-
ated in Disco. Figure 4 shows how the minimal re-
quirements for process mining are fulfilled.
Figure 4: Screenshot of the event log created in Disco.
3.3.6 Generate the Process Model
Based on the event log, the process model can be gen-
erated. Figure 5 depicts the generated process model,
including some basic model statistics (e.g., frequen-
cies, repetitions) and performance indicators (e.g., du-
Using process mining techniques, the event data
are split up into three cases and two variants, as shown
in Figure 6, which can be analyzed for patterns. When
Figure 5: Screenshot of the process model, statistics, and
performance in Disco.
the actual process is executed, the planned lead times
could be used as a norm for performance monitoring
based on the OTM data model. The lifecycles might
be used to detect deviations and compliance check-
ing (e.g., using rules and regulations regarding driving
and rest times). In addition, the lifecycles could po-
tentially be used for simulations, optimizations, pre-
dictions, and model enhancements.
Figure 6: Screenshot of the detailed view with cases and
variants in Disco.
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems
Current literature indicates a rich spectrum of process
mining use cases in logistics. The absence of unified
process definitions or standardized process models
makes it hard to generalize their results. Furthermore,
the aggregation of event data in logistics has proven
difficult due to the complex and heterogeneous nature
of logistics. Interoperability issues are addressed in
several studies. However, established approaches and
tools focus on working with noisy data.
The ideas put forward in this position paper are
based on a different approach, and propose the use
of OTM for process mining, addressing the interoper-
ability challenges and develop applications based on a
dedicated but generalizable process model. Although
this position paper contains promising preliminary re-
sults and demonstrates how the basic requirements for
process mining can be fulfilled, the support is limited.
The conducted informal conceptual mapping study in
logistics requires further experimental research and
comparative studies.
This position paper describes and traces the use of
OTM for process mining through an example, map-
ping OTM to the requirements for process mining and
demonstrating its use. Nevertheless, it is likely that
even if OTM is widely adopted that handling data in-
tegration and extraction challenges for other systems
would still need to be addressed.
Besides that, the work is still in an initial stage and
the approach should be tested in industry to determine
how it helps decision-makers in the process of con-
ducting process mining analysis. Additional exam-
ples and more complex use of the proposed approach
should be explored, considering the relevant concerns
and issues of the decision-makers in the logistic pro-
cesses. More precisely, to verify whether OTM has
the potential to be considered.
Inspired by the current industrial use of the OTM for
reporting and business intelligence, its use for process
mining is evaluated and demonstrated in this paper.
5.1 Preliminary Results and Findings
Based on an informal conceptual mapping study in
logistics, it is shown how the minimum process min-
ing requirements can be satisfied based on the OTM
data model. Based on the default OTM scenario and
example data, the step-by-step walkthrough discusses
and demonstrates how event data can be extracted to
create an event log. The process mining tool Disco is
employed to generate a process model.
5.2 Implications and Limitations
This demonstration provides initial support that the
OTM can fulfill the minimum requirements for pro-
cess mining. However, this paper’s demonstration
only covers one implementation of the OTM and is
based on synthetic data. Therefore, further experi-
mental research and development is required, involv-
ing decision-makers from the logistics industry.
The approach proposed in this paper requires the
adoption of the OTM by all involved actors and their
supporting information systems. The OTM is cur-
rently mainly used in the Netherlands. However,
given its prominent position within the federated data
sharing infrastructure for the Dutch logistics industry,
it is considered to be a promising direction for future
research and development. To increase the applica-
bility and potential generalization of the proposed ap-
proach, similar global industry standards should be
evaluated and compared.
5.3 Future Research Directions
Future research could focus on systematically map-
ping the process mining spectrum to the OTM based
on formal methods and techniques.
To extend the preliminary research, a full im-
plementation of OTM is required and this approach
should be tested for robustness with real-life datasets
in multiple use cases. The identified use cases provide
a starting point to conduct case study research. Future
work should aim to determine if implementations of
OTM and real-world data are also as straightforward
to map for process mining.
Further research directions could also include ap-
plying process mining techniques to these explored
use cases in organizations that implement the OTM.
A comparison-based study, in this regard involving
organizations that implement and do not implement
the OTM, would be an interesting next step.
Furthermore, a comparison study on solution al-
ternatives (e.g., the GS1 EPICS) and alternative ap-
proaches (e.g., data mining, machine learning) should
be conducted to evaluate the use of industry standards
in a broader sense.
This research is financially supported by TKI DINA-
LOG (grant nr. 2018-2-169TKI) as part of the IC-
Evaluating the Use of the Open Trip Model for Process Mining: An Informal Conceptual Mapping Study in Logistics
COS Project. The authors thank the involved consor-
tium partners for their support and contribution to this
research. The authors also thank the anonymous re-
viewers for their constructive feedback.
Bastiaansen, H., Nieuwenhuis, C., Zomer, G., Piest, J., van
Sinderen, M., Dalmolen, S., and Hofman, W. (2020).
The logistics data sharing infrastructure: whitepaper
august 2020. Technical report. Available online:
(accessed on 8 March 2021).
Bogarín, A., Cerezo, R., and Romero, C. (2018). A survey
on educational process mining. WIREs Data Mining
and Knowledge Discovery, 8(1):e1230.
Cabanillas, C., Baumgrass, A., Mendling, J., Rogetzer, P.,
and Bellovoda, B. (2013). Towards the enhancement
of business process monitoring for complex logistics
chains. In International Conference on Business Pro-
cess Management, pages 305–317. Springer.
De Weerdt, J., Schupp, A., Vanderloock, A., and Baesens,
B. (2013). Process mining for the multi-faceted anal-
ysis of business processes—a case study in a finan-
cial services organization. Computers in Industry,
dos Santos Garcia, C., Meincheim, A., Junior, E. R. F., Dal-
lagassa, M. R., Sato, D. M. V., Carvalho, D. R., San-
tos, E. A. P., and Scalabrin, E. E. (2019). Process min-
ing techniques and applications–a systematic mapping
study. Expert Systems with Applications, 133:260–
Erdogan, T. G. and Tarhan, A. (2018). Systematic mapping
of process mining studies in healthcare. IEEE Access,
Evofenedex, TLN, and Beurtvaartadres (2019). Nationaal
onderzoek data en digitalisering in de logistiek. Avail-
able online: https://www.tln.nl/app/uploads/2019/11/
Rapport-data-en-digitalisering.pdf (accessed on 8
March 2021).
Intayoad, W. and Becker, T. (2018). Applying process min-
ing in manufacturing and logistic for large transac-
tion data. In International Conference on Dynamics in
Logistics, pages 378–388. Springer International Pub-
Knoll, D., Reinhart, G., and Prüglmeier, M. (2019a). En-
abling value stream mapping for internal logistics us-
ing multidimensional process mining. Expert Systems
with Applications, 124:130–142.
Knoll, D., Waldmann, J., and Reinhart, G. (2019b). Devel-
oping an internal logistics ontology for process min-
ing. Procedia CIRP, 79:427–432.
Lau, H., Ho, G., Zhao, Y., and Chung, N. (2009). De-
velopment of a process mining system for support-
ing knowledge discovery in a supply chain net-
work. International Journal of Production Eco-
nomics, 122(1):176–187. Transport Logistics and
Physical Distribution Interlocking of Information Sys-
tems for International Supply and Demand Chains
Management ICPR19.
Lont, Y., van Duin, R., Jens, D.-P., and van Lier, B.
(2018). Demasking the black hole of transportation?
A blocking road naar de ontwikkeling van een CO2
Blockchain-georienteerde (product) app. Bijdragen
vervoerslogistieke werkdagen 2018, pages 1–12.
Mans, R. S., van der Aalst, W. M., Vanwersch, R. J., and
Moleman, A. J. (2012). Process mining in healthcare:
Data challenges when answering frequently posed
questions. In Process Support and Knowledge Repre-
sentation in Health Care, Lecture Notes in Computer
Science, pages 140–153. Springer, Berlin, Heidelberg.
Open Trip Model (2020). The OTM5 model. Available on-
line: https://www.opentripmodel.org/docs (accessed
on 8 March 2021).
Singh, P. and van Sinderen, M. (2016). Big data interoper-
ability challenges for logistics. In Enterprise interop-
erability in the digitized and networked factory of the
future, pages 325–335. ISTE Press.
Stichting Uniforme Transport Code (SUTC) (2019).
Algemene Presentatie SUTC. Available online:
algemene-presentatie-sutc-pptx?version=1 (accessed
on 1 September 2020).
Van der Aalst, W. et al. (2012). Process mining manifesto.
In Business Process Management Workshops, Lecture
Notes in Business Information Processing, pages 169–
194. Springer, Berlin, Heidelberg.
Wang, Y., Caron, F., Vanthienen, J., Huang, L., and Guo,
Y. (2014). Acquiring logistics process intelligence:
Methodology and an application for a chinese bulk
port. Expert Systems with Applications, 41(1):195–
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems