=Paper=
{{Paper
|id=Vol-2428/paper17
|storemode=property
|title=Data-driven deep learning for proactive terminal process management
|pdfUrl=https://ceur-ws.org/Vol-2428/paper17.pdf
|volume=Vol-2428
|authors=Andreas Metzger,Johannes Franke,Thomas Jansen
|dblpUrl=https://dblp.org/rec/conf/bpm/MetzgerFJ19
}}
==Data-driven deep learning for proactive terminal process management==
https://ceur-ws.org/Vol-2428/paper17.pdf
Data-driven Deep Learning for Proactive Terminal
Process Management
Andreas Metzger1[0000−0002−4808−8297] , Johannes Franke2 , and Thomas Jansen2
1
paluno – The Ruhr Institute for Software Technology
University of Duisburg-Essen, Essen, Germany
andreas.metzger@paluno.uni-due.de
2
duisport – Duisburger Hafen AG, Duisburg, Germany
johannes.franke@duisport.de, thomas.jansen@duisport.de
Abstract. Big data offers tremendous opportunities for transport process innova-
tion. One key enabling big data technology is predictive data analytics. Predictive
data analytics supports business process management by facilitating the proactive
adaptation of process instances to mitigate or prevent problems. We present an in-
dustry case employing big data for process management innovation at duisport,
the world’s largest inland container port. In particular, we show how data-driven
deep learning facilitates proactive port terminal process management. We demon-
strate the feasibility of our deep learning approach by implementing it as part of
a terminal productivity cockpit prototype. The terminal productivity cockpit pro-
vides decision support to terminal operators for proactive process adaptation. We
confirm the desirability of our approach via interviews. We assess the viability
of our approach by estimating the improvements in a key business KPI, as well
as experimentally measuring the cost savings when compared to terminal oper-
ations without using proactive adaptation. We also present our main technical
lessons learned regarding the use of big data for predictive analytics.
Keywords: Business process monitoring, proactive adaptation, prediction, accu-
racy, earliness, reliability, decision support, terminal operations
1 Introduction
Big data offers tremendous opportunities for transport process innovation and will have
a profound economic and societal impact on mobility and logistics. As an example, with
annual growth rates of 3.2% of passenger transport and 4.5% of freight transport in the
EU [6], transforming the current mobility and logistics processes to become signifi-
cantly more efficient, will have major impact. Improvements in operational efficiency
empowered by big data are expected to save as much as EUR 440 billion globally in
terms of fuel and time within the mobility and logistics sector, as well as reducing 380
megatons of CO2 emissions [27]. The mobility and logistics sector is ideally placed to
benefit from big data technologies, as it already manages massive flows of goods and
people whilst generating vast amounts of data [4].
One key enabling big data technology in transport is predictive data analytics [23].
Predictive analytics is a significant next step from descriptive analytics [13]. Where de-
scriptive analytics aims to answer the question “what happened and why?”, predictive
Copyright © 2019 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
analytics aims to answer the question “what will happen and when?”. Predictive analyt-
ics is considered a key technology and technical priority within the European big data
ecosystem; e.g., see the Strategic Research and Innovation Agenda of the European Big
Data Value Association [30].
Predictive analytics – in the form of predictive process monitoring [10,15,24] – sup-
ports business process management by facilitating proactive process adaptation. Proac-
tive process adaptation can help prevent the occurrence of problems and it can mitigate
the impact of upcoming problems during process execution by dynamically re-planning
a running process instance [29,18,26,36,16,19]. As an example, a delay in the expected
delivery time for a freight transport process may incur contractual penalties [12]. If dur-
ing the execution of such freight transport process a delay is predicted, faster transport
services (such as air delivery instead of road delivery) can be proactively scheduled to
prevent the delay. Proactive process adaptation thereby helps transport operators to be
proactive and avoid contractual penalties or time-consuming roll-back and compensa-
tion activities.
We present an industry case employing big data for process management innova-
tion at duisport, the world’s largest inland container port. In particular we focus on how
data-driven deep learning facilitates proactive port terminal process management. Deep
learning employs artificial neural networks with many neurons and layers [11]. Apply-
ing such deep neural networks became feasible with recent breakthroughs in learning
algorithms and the advent of powerful hardware.
Section 2 describes the situation faced in terminal process management. Section 3
elaborates on the actions taken in order to exploit data-driven deep learning for terminal
process management. Section 4 presents results with respect to the impact on terminal
operations. Section 5 provides our lessons learned.
2 Situation faced
2.1 Context and challenges for terminal process management
The case we present is located at duisport, an inland container port that handles 4.1
million containers per year. Duisport is situated in the middle of a large city (with
close to 1/2 million inhabitants) and at the center of Germany’s largest metropolitan
region, the Rhine-Ruhr metropolitan region (with close to 10 million inhabitants). This
means that a multitude of roads, tracks and water ways serve as entry and exit points for
containers to and from the terminals and ports. In addition, the transport infrastructure
(roads and tracks) need to be shared within the metropolitan region.
Given the location of duisport within a dense metropolitan region, the increase in
container volumes (due to the growth of freight transport) cannot be captured by a
growth in space. It requires an improvement of terminal productivity.
The duisport case we report focuses on improving the productivity of a specific ter-
minal: logport III. The logport III terminal covers an area of 15 hectares, offers nine rail
connections, runs seven transhipment tracks and operates two gantry cranes. The termi-
nal is interconnected with other duisport port areas and to more than 80 destinations in
Europe and Asia. This includes daily rail and barge shuttles to the seaports of Antwerp
and Rotterdam, as well as more than 30 trains per week between duisport and China.
We developed the duisport case in the context of the EU-funded lighthouse project
TransformingTransport [3]. TransformingTransport is part of the European Big Data
Value Public-Private Partnership3 . The project started in January 2017 and brings to-
gether knowledge, solutions and impact potential of major European ICT and big data
technology providers with the competence and experience of key European industry
players and public bodies in the mobility and logistics domain. TransformingTransport
developed 13 pilot cases that demonstrate how various transport sectors will benefit
from big data solutions and the increased availability of data.
2.2 Big data availability as opportunity for process management innovation
The main driver to explore big data technologies for process management innovation at
duisport was the increasing availability of data due to the instrumentation and digitiza-
tion of terminal equipment. To illustrate, the two gantry cranes of the terminal that move
the containers between trains and towing vehicles (i.e., trucks) produce data about 100
variables in 5 second intervals. These variables include information such as the crane’s
current state, its position, its current speed, its energy consumption, whether it trans-
ports a container or not, as well as observed faults.
At the time of writing, eight different data sets and over 30 million data entries
from nine devices were available at the duisport terminal logport III. Figure 1 illustrates
some of the available data. On the left hand side, the figure shows the terminal and its
equipment. On the right hand side, the figure shows a visualization of the integrated and
aggregated data in the form of a heat map, which shows the density of containers over
the last 96 hours (ranging from low density = “green” to high density = “red”).
2.3 Requirements towards predictive process management solutions
With respect to the usefulness of predictions as input for proactive process adaptation,
we had to address two important requirements.
Requirement 1 – “Prediction accuracy”. Informally, prediction accuracy character-
izes the ability of a prediction technique to forecast as many true violations as possible,
while generating as few false alarms as possible [32]. Prediction accuracy is important
due to several reasons. Accurate predictions deliver more true violations and thus trig-
ger more required adaptations. Each missed required adaptation means one less oppor-
tunity for preventing or mitigating a problem. Also, accurate predictions mean less false
alarms, which in turn means triggering less unnecessary adaptations [22]. Unnecessary
adaptations incur additional costs for executing the adaptations, while not addressing
actual problems. A too high rate of false alarms will mean that a terminal operator will
not trust the predictions and thus will not use them for decision making.
Requirement 2 – “Prediction earliness”. Predictions should be produced early dur-
ing process execution, as this leaves more time for adaptations. An adaptation typically
has a non-negligible latency, i.e., it may take some time until an adaptation becomes
effective [25,14]. As an example, dispatching additional personnel to mitigate delays
in container transports may take several hours. Also, the later a process is adapted, the
3
http://www.big-data-value.eu/
Transhipment Track
Train
Gantry Crane Data
Integration
and
Aggregation
Towing Vehicle
Data streams from terminal equipment Integrated data of container moves
(1.3 mio states / month) (10,000 moves / month)
Fig. 1. Illustration of terminal data availability
fewer options may be available for adaptation. As an example, while at the beginning
of a transport process one may be able to transport a container by train instead of ship,
once the container is on-board the ship, such an adaption is no longer be feasible. Fi-
nally, if an adaptation is performed late in the process and turns out to be non-effective,
not much time remains for remedial actions or further adaptations.
There is an important tradeoff between these two requirements. Later predictions
typically have a higher accuracy (as depicted in Figure 2), because more information
about the ongoing process instance is available. This means later predictions have a
higher chance to be correct predictions. Therefore, one should favor later predictions
as basis for proactive process adaptation. However, later predictions leave less time for
process adaptations.
Accuracy
Process
completion
Cargo 2000 BPIC 2012 BPIC 2017
Fig. 2. Prediction earliness vs. prediction accuracy for different data sets (from [20] and [34])
3 Actions taken
3.1 Exploiting advanced analytics for decision support
One of the main actions to leverage the data availability described in Section 2.2 was to
employ advanced analytics to provide decision support for terminal operators, thereby
helping them better manage terminal processes.
The key concept we prototypically developed in the duisport case is the so-called
terminal productivity cockpit (TPC). The TPC exploits advanced data processing and
predictive analytics capabilities to facilitate terminal operators in proactive decision
making and process adaptation. In particular, the terminal productivity cockpit lever-
ages data-driven deep learning techniques for predictive business process monitoring
(see Sections 3.2–3.3). Figure 3 shows a screenshot of the TPC prototype, which visu-
alizes the current and predicted situation in the duisport terminal.
Train Loading Status of Container
Time until Train Departure (Earliness)
Planned Departure Time
Reliability Estimate
Alarm about Delay Predicted Departure Time
Fig. 3. Screenshot of Terminal Productivity Cockpit – TPC prototype (excerpt)
For each train that is currently in the terminal (in one of the seven transhipment
tracks), the TPC shows the following information:
– Loading status of container: Each train can carry multiple containers. The TPC
shows the status for each of the containers of a train. The arrows indicate the
scheduled activities per container, with an upward-facing arrow indicating that a
container is to be offloaded, while a downward-facing arrow indicating that a con-
tainer is to be loaded onto the train. Green means a container has been successfully
loaded onto the train, while red indicates a potential problem in container loading.
– Planned departure time: For each train, the scheduled departure time is shown.
This is essential information, as each train usually has a fixed time slot when it has
to depart. On the one hand, such fixed time slots are imposed by the use of the
public train infrastructure when leaving the terminal. On the other hand, the train
may have to meet fixed departure windows of sea vessels if it connects to a sea port.
– Time until train departure: To inform the operators of how much time remains for
any potential proactive actions, the TPC shows the time remaining until the planned
train departure. This contributes to addressing the earliness requirement.
– Predicted departure time: The TPC shows the predicted departure time for the train,
which takes into account the current status and data from terminal equipment.
– Alarm about delay: To facilitate a quick identification of problems, the TPC visibly
highlights alarms, i.e., predictions which indicate a potential delay. Thereby, the
attention of the operators can focus on important information, which helps address
potential cognitive overload [7].
– Reliability estimate: In addition to showing an alarm in the case of a delay, the TPC
also shows a reliability estimate. The reliability estimate gives the probability (in
%) of the predicted delay being accurate, i.e., whether the alarm indeed is a true
alarm. This is quite similar to today’s weather forecasts. For instance, in addition
to predicting that it will rain, a forecast typically also gives the probability that
it will rain. Reliability estimates facilitate distinguishing between more and less
reliable predictions on a case by case basis [19]. Reliability estimates can help
decide whether to trust an individual prediction (and thus alarm) and consequently
whether to perform a proactive adaptation of the given process instance [18,35,9].
The reliability estimates together with the earliness indicators of the TPC provide
additional information to the terminal operator for decision making. For example, in
the situation visualized in Figure 3, the terminal operator is informed that the train to
Katrinholm scheduled for 20:30 may be delayed (until 21:08) with a probability of
69% and that there would be 3:12 hours remaining for any proactive action. Given the
relatively low probability that the prediction is correct and the little time remaining for
taking proactive actions (e.g., rescheduling the terminal workforce may take around 3
hours), the terminal operator may decide not to act in this specific case.
3.2 Ensemble deep learning for predictive process monitoring
We compute the aforementioned predictions and reliability estimates by using ensem-
bles of deep learning models. Ensemble prediction is a meta-prediction technique where
the predictions of m prediction models are combined into a single prediction [28].
In the literature, ensemble prediction is primarily used to increase aggregate predic-
tion accuracy. In our case, using ensembles of deep learning models provided an 8.4%
higher accuracy when compared to a single deep learning model (as used in [20]). How-
ever, increased accuracy is not the main reason why we use ensembles in our approach.
We use ensembles in order to compute good estimates of the prediction reliability [19].
Fig. 4 gives an overview of our approach. Each of the individual deep learning
models of the ensemble delivers a prediction of the train departure time Ti,j for each so-
called checkpoint j. Using these individual predictions, the three main pieces of infor-
mation shown in the TPC are computed employing the strategies defined in [19,18,21]:
(1) the predicted train departure time Tj , (2) the alarm about a potential delay Aj , and
(3) the reliability estimate ρj for the alarm.
For computing the predicted departure time Tj , we follow the recommendations
in [1] and compute the mean value of the individual predictions Ti,j , i.e.,
1 X
Tj = · Ti,j
m
Predicted
Departure Time
Deep Learning T1,j Tj
Model 1 Alarm
Process
about Delay
monitoring Aj
…
data at Reliability
checkpoint j Deep Learning Estimate
Model m
Tm,j j
Ensemble Prediction
Fig. 4. RNN ensemble for predictive transport process monitoring
For computing the alarm Aj , we first determine for each of the individual predic-
tions Ti,j whether they indicate a delay or not by comparing the prediction with the
scheduled departure time. This means Ai,j = true indicates a predicted delay. Then,
Aj is computed as a majority vote over Ai,j , i.e.,
m
Aj = {true if |i : Ai,j = true| ≥ ; false otherwise}
2
The reliability estimate ρj for Aj is computed as the fraction of predictions Ai,j
that predicted the delay, i.e.,
1
ρj = · |i : Ai,j = true|
m
We use bagging (bootstrap aggregating [5]) as a concrete ensemble technique. Bag-
ging generates m new training data sets from the whole training set by sampling from
the whole training data set uniformly and with replacement. For each of the m new
training data sets an individual deep learning model is trained. We use bagging with a
sample size of 60% to increase the diversity of the ensemble. Bagging contributes to the
scalability of our approach, as training the individual models can happen in parallel.
3.3 RNN-LSTMs as Deep Learning Models
We use RNN-LSTMs (Recurrent Neural Networks – Long Short-term Memory) as the
individual deep learning models in the ensemble. RNN-LSTMs offer the following ad-
vantages over other prediction models:
– High Accuracy. RNN-LSTMs have shown significant improvements in prediction
accuracy when compared to other prediction models [2,33]. As an example, we
experimentally measured accuracy improvements of up to 42% when compared to
Multi-Layer Perceptrons [20].
– Arbitrary Length Sequences. RNNs can handle arbitrary length sequences of input
data [11]. Thus, a single RNN can be employed to make predictions for business
processes that have an arbitrary length in terms of process activities. In contrast,
other prediction models (such as random forests or multi-layer perceptrons) may
require the special encoding of the input data [15,34,24]. However, these encodings
entail information loss and thus may limit prediction performance.
– Scalability. RNNs facilitate the scalability of our approach. Assume we have c
checkpoints in the business process. A single RNN model can make predictions
at any of these c checkpoints [8,33]. If we want to avoid information loss, other
prediction models (such as random forests or multi-layer perceptrons) require the
training of c prediction models, one for each of the c checkpoints. Performance
measurements using a benchmark data set indicate a training time of ca. 8 minutes
per checkpoint for multi-layer perceptrons on a standard PC, while the training
time for an RNN was 25 minutes4 . RNNs provide better scalability if the process
has many potential checkpoints (in our example already if c > 3).
We use RNNs with LSTM cells as they better capture long-term dependencies in
the data [33,17]. We use a shared multi-tasks layer architecture as presented by Tax et
al. as this provided higher prediction accuracy [33]. In addition to a shared layer, we use
three separate layers to predict (1) the next process activity, (2) the time stamp, and (3)
the binary process outcome (delay / no delay). Our implementation is available online5 .
4 Results achieved
4.1 Feedback from terminal operators
Based on demonstrations and structured interview sessions with the logport III terminal
operator, a qualitative assessment of the TPC with respect to its usefulness and usabil-
ity was collected. The general feedback was very positive. However one key point was
raised during the first rounds of interviews. Given the amount and diversity of data avail-
able for the TPC, the terminal operator felt overwhelmed by the amount of information
displayed in the TPC. Thus, the terminal operator suggested only providing information
that could indicate a problem and its root cause. As a result, the current version of the
TPC shows only the information deemed relevant and – as depicted in Section 3.1 –
visibly highlights alarms about potential problems in terminal operations.
While the terminal operator was interacting with the TPC, an important side effect
was observed. The terminal operator became aware of the broad range of existing data
about the terminal and thereby the possibilities that data may provide in finding answers
for hitherto unanswerable questions.
4.2 Potential improvements in terminal operations
To quantify the usefulness of the TPC, we analyzed the potential improvements in ter-
minal operations with respect to terminal productivity and costs.
4
Further performance speedups are possible via special-purpose hardware and RNN implemen-
tations. RNN training time reduced to 8 minutes on GPUs (using CuDNN), and further to 2
minutes on TPUs (Tensor Processing Units).
5
https://github.com/Chemsorly/BusinessProcessOutcomePrediction
Productivity. For what concerns the productivity of terminal operations, we set out
to measure the improvement of a specific business KPI: “Number of trains leaving the
terminal on-time”. This is one of the critical success factors, because – as mentioned
above – trains have designated time slots. If a train misses its time slot, re-scheduling is
necessary and penalties for late deliveries can occur. Using historic data about terminal
operations, we estimated that the use of the TPC may increase the rate of number of
trains leaving the terminal on time by up to 4.7%.
Costs. For what concerns costs, we performed controlled experiments using the
public Cargo2000 transport data set6 . The cost models we employed considered various
penalty costs in the case of actual delays, as well as various adaptation costs for adapting
the running process instance. Details of these experiments are reported in [19,18,21].
Here, we summarize the key outcomes.
We first used a fixed point for predictions (the 50% mark of process execution), and
thus did not consider the requirement of prediction earliness. We computed reliabili-
ties via ensembles of classification models (delay/non-delay predictions). When using
these reliability estimates to decide on proactive process adaptation, we measured cost
savings of 14% on average [19]. When also including the magnitude of a predicted vi-
olation (computed from ensembles of regression models), we measured additional cost
savings of 14.8% on average [18].
To consider prediction earliness and thus find a trade-off between earliness and
accuracy, we used the reliability estimates to dynamically determine the earliest pre-
diction with sufficiently high reliability and used this prediction as basis for proactive
adaptation [21]. This meant that the actual checkpoint chosen for a proactive adaptation
decision varied among the different process instances, in the same way the reliabil-
ity estimates varied among the predictions and process instances. Experimental results
suggest that dynamically determining the checkpoint offers cost savings of 9.2% on
average when compared to using a fixed, static checkpoint. Dynamically determining
the checkpoints thus effectively addresses the tradeoff between prediction accuracy and
prediction earliness and thus meets the requirements as identified in Section 2.3.
5 Lessons learned
To complement the results from above, we present our main recommendations based
on the technical lessons learned regarding the use of big data for predictive analytics:
– Deep learning works well without extensive hyper-parametrisation. If enough
good quality data is available (like in our case), we experienced that deep learning
techniques provide high prediction accuracy without the need for extensive hyper-
parameter tuning. In addition, the deep learning models we used (RNNs) did not
require special encoding of the input data. Thus, consider using deep learning to
make the engineering of data-driven predictive process monitoring solutions more
productive!
– Data quality is a key concern for the usefulness of data analytics. Data quality
is an important concern in data analytics (“garbage in – garbage out”), but also a
6
Available from https://archive.ics.uci.edu/ml/datasets
very resource- and time-intensive activity. With respect to data quality we had to
face missing data (e.g., because it was not available in digital form or because of
network outages), cope with low data accuracy (due to imprecise measurements),
and handle data timeliness (due to delays in data collection). Thus, plan sufficient
time and effort for data quality and refinement of data collection!
– Data processing and integration can consume significant time and resources. We
estimate that data processing, integration and quality assurance consumed around
80% of the resources spent in the duisport pilot case. The reasons were manifold.
Oftentimes, we did not have control over the data from third parties (such as equip-
ment manufactures), or data collection and semantics drifted over the course of de-
velopment. Other examples were telemetry data using different coordinate systems
(such as GPS vs XYZ) and timestamps being based on non-synchronized clocks.
Thus, plan sufficient time for data processing and integration!
– Operators benefit from information about data reliability. Getting additional infor-
mation about how reliable an individual prediction helps operators decide whether
to act on a prediction or not. It supports operators in finding the earliest predic-
tion with sufficient accuracy, thereby allowing more time for proactive actions. In
addition, we observed that operators benefit from understanding how reliable the
actual data is; e.g., in the form of descriptive analytics outcomes or when visu-
alized in the terminal productivity cockpit. Thus, consider augmenting descriptive
and predictive analytics results with reliability estimates, confidence intervals, error
ranges, etc. in order to provide additional support to process operators for decision
making!
6 Conclusions and Perspectives
The duisport case we presented in this paper shows how data-driven deep learning can
deliver profound transport process innovation. We have shown the feasibility of our
deep learning approach by implementing it as part of a terminal productivity cockpit
prototype. The terminal productivity cockpit provides decision support to terminal op-
erators for proactive process adaptation. The viability of our approach is supported by
an estimated improvement in a key business KPI, as well as experimentally measured
cost savings when compared to terminal operations without using proactive adaptation.
The desirability of our approach is confirmed by positive feedback received from the
terminal operator during interviews.
The continuing significant growth of transport data volumes and the rates at which
such data is generated will be an important driver for the next level of business process
innovation in transport: Data-driven Artificial Intelligence [31]. From an industrial point
of view, artificial intelligence means algorithm-based and data-driven computer systems
that enable machines and people with digital capabilities such as perception, reasoning,
and learning, as well as autonomous decision making and actuation. Building on today’s
promising results in using artificial intelligence, we can expect artificial intelligence to
deliver the next level of productivity improvements in transport.
Acknowledgments. Research leading to these results received funding from the EU’s
Horizon 2020 R&I programme under grant agreement no. 731932 (TransformingTrans-
port) and 732630 (BDVe).
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