Data Visualization Support for Complex Logistics Operations and
Cyber-Physical Systems
Didem Gürdür
1
, Klaus Raizer
2
and Jad El-Khoury
1
1
Department of Machine Design, KTH, Royal Institute of Technology, Stockholm, Sweden
2
Ericsson Research - ARBB, Brazil
Keywords: Data Visualization, Information Visualization, Supply Chain, Automated Warehouse, Intelligent Agent,
Cyber-Physical Systems, Dashboard Design, Soar.
Abstract: Today, complex logistics operations include different levels of communication and interactions. This paper
explores the requirements of these operations and conceptualizes important key performance indicators,
stakeholders, and different data visualizations to support the stakeholders in order to understand interactions
between entities easier and faster. Three different levels were identifiedsupply chain, automated
warehouse, and intelligent agentto define the complex logistics operations. For each level, important
stakeholders and performance indicators were determined. A case study was designed and described to
exemplify the role of cyber-physical systems in complex logistics operations. Moreover, different data
visualizations were developed as part of a dashboard to illustrate key performance indicators of different
levels for the purpose of supporting stakeholders. This exploratory study concludes by identifying important
data necessity for each performance indicator, suggesting ways to collect these data, and exemplifying how
data visualization approach can be used through a dashboard design.
1 INTRODUCTION
Automated warehouses include different forms of
cyber-physical systems (CPSs) (Lee and Seshia,
2016)such as intelligent robots and autonomous
vehicles, which require collaborative behavior for
effective and efficient handling and distribution of
goodsto manage complex logistics operations.
Even though the autonomous guided vehicles have
been used in warehouses to move very large and
heavy objects since the 1950s (Wurman, D’Andrea
and Mountz, 2008), the use of CPSs in this industry
has gained speed in recent years with the help of
ongoing developments in control, communication,
and computation capabilities of these systems.
Today, CPSs have inexpensive computational power
and wireless communication and components, which
are making them cheaper, smaller, and more
capable.
While the adaptation of CPSs is increasing, the
need to provide real-time feedback to support the
design and control decisions of these systems is also
arising. This research focuses on developing
appropriate data visualizations to support
stakeholders in their decision-making activities
when architecting complex logistic operations,
which includes automated warehouses and
intelligent agents (IAs).
It has been decided that Soar (Laird, Newell and
Rosenbloom, 1987) will be used as a cognitive
architecture to explain a variety of phenomena
related with IAs. Generally, cognitive architectures
are producing textual data, and the data is often
considered impractical by stakeholders who try to
understand the behavior of the IAs. Nevertheless,
both the architecture developers and subject matter
experts want to learn how cognitive architectures
work in detail (Avraamides and Ritter, 2002;
Councill, Haynes and Ritter, 2003). One approach to
help stakeholders understand the behavior of these
architectures is to provide a graphical representation
of the processes and behaviors of the agents.
In earlier research (Gürdür, 2016), existing data
collection methods were not developed well enough
to be directly useful for data analytics and data
visualization development in order to improve the
understanding of the CPS. Therefore, this study aims
to identify important stakeholders, who are part of
the decision-making activities of the automated
warehouses, in order to develop necessary data
200
Gürdür, D., Raizer, K. and El-Khoury, J.
Data Visualization Support for Complex Logistics Operations and Cyber-Physical Systems.
DOI: 10.5220/0006569402000211
In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 3: IVAPP, pages
200-211
ISBN: 978-989-758-289-9
Copyright © 2018 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
collection, presentation, and interactivity methods to
generate intuitive data visualizations for the purpose
of increasing the understanding of important key
performance indicators (KPIs). As part of this effort,
this study has initiated the development of several
data visualizations and a dashboard to measure KPIs
and inform stakeholders about the current situation
about the system.
To this end, this study aims to answer the
following research questions (RQs):
RQ1: What are the important KPIs for
improving the understanding of complex
logistics operations?
RQ2: Who are the important stakeholders that
can benefit from real-time data visualizations
and visual analytics in order to assess the
identified KPIs?
RQ3: What are the possible data resources to
be used in the development of data
visualizations?
RQ4: Which data visualization technique(s)
should be used to support decision-making
activities of these stakeholders?
These exploratory questions are answered by
different research methods, which we will detail in
Section 3. The expert opinion technique and semi-
structured interviews were used to answer RQ1 and
RQ2. Moreover, an example case study has been
designed and described, and sample dashboard was
developed to answer RQ3 and RQ4.
In Section 2, the earlier studies on automated
warehouse systems will be explained, and the
background information about chosen cognitive
architecture will be described. Secondly, the
research approach and the methodology will be
explained in Section 3. Then, the case study will be
described in Section 4 in order to demonstrate the
data visualization needed to improve the
understanding of complex logistics operations. This
section identifies the KPIs and stakeholders, and
presents the data visualization dashboard to
accomplish better understanding of the system.
Afterward, the findings of the case study will be
discussed in Section 5. Section 6 summarizes related
work, and finally, Section 7 will recapitulate the
findings of the research to conclude the study, in
addition to presenting the areas where future efforts
will be devoted.
2 BACKGROUND
Logistics or supply chain management is “the
process of planning, implementing, and controlling
the efficient, cost-effective flow and storage of raw
materials, in-process inventory, finished gods, and
related information flow from point-of-origin to
point-of-consumption for the purpose of conforming
to customer requirements” (Management, 1986).
Automated warehouses play an important role in
today’s supply chains, and they consist of a
combination of computer-controlled systems that
automatically handle, store, and retrieve products
with great speed and accuracy. Some parts of these
warehouses are also called automated storage and
retrieval systems (AS/RSs). They offer the
advantages of improved inventory control and cost-
effective utilization of time, space, and equipment
(Hur et al., 2004; Manzini, Gamberi and Regattieri,
2006). They can be considered as CPSs since they
are equipped with motors, sensors, actuators,
controllers, and the ability to communicate with
other systems (Basile, Chiacchio and Coppola,
2016).
It is necessary to address the design and control
decisions of these systems to fully take advantage of
all the opportunities they offer. For this reason,
several studies are included that examine the
AS/RSs from different perspectives. Roodbergen
and Vis (Roodbergen and Vis, 2009) published an
extensive literature review that examines the current
state of the art in AS/RSs. In this study, the authors
summarized the issues, such as system
configuration, travel time estimation, storage
assignment, dwell-point location, and request
sequencing. After discussing the reviewed papers,
the paper addresses the individual control policies
for storage assignment, batching, parking of idle
AS/RSs, and sequencing. The authors also
commented that the majority of the literature
addressed the design and control problems in static
environments. Moreover, the authors underlined that
today customer demands, order quantities, and
delivery schedules are rapidly changing, and the
competition is constantly increasing; hence, more
flexible approach is needed.
The literature review (Roodbergen and Vis,
2009) identified that only one or two decision
problems are addressed simultaneously, instead of
combining different problems and developing an
overall optimization solution. Certainly, it is a
difficult task to include a multitude of design and
control aspects of the system for an overall
optimization. However, it is vital to understand the
system as a whole rather than focusing on only one
decision problem. Furthermore, the existing
literature mainly concentrated on the relationship
between AS/RSs; little effort was spent on
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201
understanding the relationship between AS/RSs and
other systems in production and distribution
facilities.
The literature review (Roodbergen and Vis,
2009) concluded by highlighting the need “to move
towards developing models, algorithms, and
heuristics that include the dynamic and stochastic
aspects of current business. In this context, one can
think of self-adaptive storage assignment methods,
online-batching policies and dynamic dwell-point
rules. Also, algorithms for physical design may need
to focus more on the robustness of the design than
on perfect optimality to ensure that the system will
be capable of remaining efficient in yet unknown
future situations.” One way to fulfill the need that
has been identified by the authors (Roodbergen and
Vis, 2009) is to implement intelligent automated
warehouses. These intelligent warehouses can
examine the relationships between CPSs such as
autonomous vehicles, AS/RSs, conveyor systems,
cooperative robots, and humans. Moreover, it is
possible to develop analytic support within
intelligent automated warehouses that aids
stakeholders in their decision-making activities.
One way to construct an intelligent warehouse is
to use cognitive architectures that adapt the tools
from computational psychology. Newell (Newell,
1987) proposed the development of cognitive
architectures that provide fixed computational
structures that form the building blocks for creating
an intelligent system.
A cognitive architecture is a task-independent
infrastructure that brings an agent’s knowledge to be
concerned with a problem in order to produce a
behavior other than a single algorithm or method for
solving a problem (Laird, 2012). Cognitive
architectures are the most well-known approaches to
improve the intelligence and autonomy of robots.
There are different architectures that focus of
modelling different aspects of cognition at different
levels of abstraction (Ernst and Newell, 1969;
Georgeff and Lansky, 1986; Laird, Newell and
Rosenbloom, 1987; Anderson, 1996; Freed, Shafto
and Remington, 1999; Just, Carpenter and Varma,
1999; Cassimatis, 2006; Franklin and Patterson,
2006).
Soar (Laird, Newell and Rosenbloom, 1987) is
one of these architectures that possesses several
capabilities, making it a promising candidate for use
in autonomous and cooperative robots. Some of
these capabilities include the following:
simple communication between the
architecture and environment through many
sensors and motors;
a mix of reactive and deliberative behaviors;
definition of a learning mechanism; and
the ability to collaborate with other agents or
software systems (Long et al., 2007).
In particular, Soar architecture provides the
ability to use a wide variety of types and levels of
knowledge for problem solving. It has been used to
develop agents that use several methods for tasks
such as reasoning, algorithm design, robotic control,
simulating pilot behaviors, and so on.
3 RESEARCH DESIGN
This study has been designed to answer the three
exploratory research questions mentioned in Section
1. The expert opinion technique (Clayton, 1997) was
used to assist in the preliminary problem
identification phase. This technique aims to gather
opinions of experts in clarifying the issues relevant
to a particular topic.
Several meetings have been conducted with
researchers at Ericsson who extensively work on the
cognitive architectures, intelligent agents, and
complex logistics operations. For the purpose of
identifying significant stakeholders and key factors,
semi-structured interviews (SSIs) (Drever, 1995)
were used as a qualitative inquiry method. SSIs are
designed to collect subjective responses from
interviewees regarding a particular situation or
phenomenon they have experienced (Drever, 1995).
They can be used when there is sufficient objective
knowledge about an experience but the subjective
knowledge is lacking (Richards and Morse, 2012).
In this research, the subjective knowledge of the
experts plays a big role in identifying relevant KPIs
and stakeholders, and eventually affects the design
of the data visualizations and the dashboard. The
interview questions were used to collect responses of
each participant and constitute the structure of the
SSI. These questions aimed to understand the
architecture of the system, to identify the important
people and positions, and to extract the needs of
each for the purpose of developing data
visualizations.
Finally, an exploratory case study method (Fidel,
1984) was used to assist the development of the
dashboard according to the needs that identified by
expert interviews. This method is especially useful
to investigate complex real-world issues, such as
those involving humans and their interactions with
technology. The exploratory (or pilot) case studies
are condensed case studies that can be used before
implementing a large-scale investigation or solution.
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Their basic function is to help identify questions and
select types of measurement prior to the main
investigation. Hence, the case study method is an
ideal methodology for this particular study, where a
holistic investigation is needed (Feagin, Orum and
Sjoberg, 1991; Shneiderman and Plaisant, 2006).
4 CASE STUDY
This section first describes the case study and the
overall architecture/structure of an automated
warehouse. Section 4.1 focuses on the identification
of primary KPIs and stakeholders to answer RQ1
and RQ2. Then, the possible data visualization
techniques are exemplified and discussed in Section
4.2 to answer RQ3 and RQ4.
4.1 Identifying KPIs and Stakeholders
In this case study, an automated warehouse and a
supply chain were simulated to explore multi-
objective computational intelligence approaches and
autonomous robotics for managing complex logistics
operations (Azevedo et al., 2016).
The case study includes three distinct levels to
describe the whole logistics operations. Level 1
describes a typical supply chain that has components
such as suppliers, retailers, and an automated
warehouse. Level 2 focuses on the automated
warehouse component and includes different types
of CPSs that work autonomously to fulfill the
inventory replenishment, storage, and delivery
requests, in addition to humans. Level 3 zooms in on
CPSs, specifically to the intelligence of CPSs. This
level illustrates intelligent agents’ knowledge with
Soar architecture. In the following subsections, we
will describe the different KPIs, stakeholders, and
the challenges for each of the three levels of this
automated warehouse architecture.
Level 1: Supply Chain (SC) Level
The supply chain (SC) level consists of an
automated warehouse, trucks, retailers, and
suppliers, as shown in Figure 1. At this level, several
predictive algorithms are needed to execute an
optimum plan for the most profitable option. A
simulation was developed to analyze how an
intelligent, automated vendor-managed inventory
method allows for efficient real-time integration of
warehouse operations with multi-retailer inventory
replenishment tasks (Azevedo et al., 2016). V-REP
software was used for this purpose, where a robot
Figure 1: Overview of supply chain level.
simulator generates instructions by a multi-objective
evolutionary algorithm running in real-time, aiming
to simultaneously maximize profit and minimize
shortage and surplus risks while deciding on-the-fly
which and how many products should be delivered
to which retailers and when.
The essential stakeholders of this level were
identified as warehouse manager, supply chain
manager, and truck driver. At this level, we
identified profitability, risk, and sustainability as
important KPIs by conducting SSI, as discussed in
Section 3. Profitability metric refers to the degree to
which a business or activity yields profit or financial
gain. The risk is associated with the excess supply, a
situation in which the quantity of a good or service
supplied is more than the quantity demanded, and
the price is above the equilibrium level determined
by supply and demand (Sullivan and Sheffrin, 2003).
Sustainability metric is related to the characteristics
of the route that trucks take to/from suppliers,
retailers such as distance, traffic congestion
situation, and so on.
Level 2: Warehouse (W) Level
The warehouse (W) level is concerned with the
interactions between IAs, where many IAs are
communicating with each other and parameters such
as performance, safety, and sustainability are the
focus. Possible shortest path, which is doable with
the current battery level, and the most efficient way
to complete the task without any collision, are two
relevant examples.
Figure 2 illustrates the overview of the
warehouse level, where AS/RSs, robotics arms,
autonomous robots, cameras, conveyor belts, and
humans work together to accomplish tasks related to
the warehouse. The warehouse manager, system
engineer and warehouse staff are stakeholders who
should be able to see KPIs to support their decision-
making processes. Furthermore, knowledge
reusability, safety, interoperability, performance,
and sustainability are important KPIs that need to be
considered.
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Figure 2: Overview of Warehouse Level.
Level 3: Intelligent Agent (IA) Level
The intelligent agent (IA) level is compliant with the
Soar architecture (see Figure 3) and, therefore,
consists of a framework for representing tasks and
subtasks, long-term memory (LTM), working
memory (WM), and related mechanism for
generating goals, as well as mechanism for learning
(Steinman, Lammers and Valinski, 2009). The LTM
is knowledge available at the agent’s inner
“database.” It is composed of rules, facts (semantic
knowledge), and episodes the agent has experienced
in the past (or had them input by a designer) and that
can be retrieved when necessary. WM, on the other
hand, holds only what is necessary for dealing with
the current situation. It is composed of rules being
used at the moment and of facts about the agent’s
current environment. It also contains perceptual
information coming from sensors and motor
instructions that are sent to actuators.
Figure 3: Overview of Soar9 (Laird, 2012).
Experts and intelligence developers are the main
stakeholders who need to understand important KPIs
for improving the IAs. The KPIs at this level were
identified as performance and knowledge
reusability.
4.2 Data Visualizations
Once we identified the stakeholders and their
specific KPIs, we designed an interactive dashboard
to visualize each of the KPIs for their relevant
stakeholders. The dashboard is structured into three
main tabs associated with each of the three levels
that have been defined in earlier sections. The top
panel lists these three levels.
Dashboard View 1: Supply Chain
Level
As will be detailed in this section, KPIs
(performance, knowledge reusability,
interoperability, safety, profitability, and risk) are
visualized in the tabs by different visualization
techniques and metrics that were found to be most
suitable. The dashboard design accommodates
different stakeholders’ needs. Each stakeholder can
use one or more tabs to focus on different KPIs
according to their need and can navigate through the
dashboard to reach detailed analysis for specific
KPIs. Moreover, all information is interactively
presented on the dashboard, where stakeholders can
hover or click on a visualization element and see
more information about a particular KPI. KPIs can
be selected to be included in the multi-objective
optimization of the end-to-end supply chain process.
Figure 4 shows the dashboard design for the
supply chain level. The essential information this
level is summarized in the bottom part of the
dashboard. The dashboard was constructed using a
simple and clear design to easily represent the
information. For instance, the capacity of each
retailer and the warehouse is visualized as donut
charts. One can see the average value of these KPIs
according to week, month, or year ranges. Three
trucks and their work processes are visualized below
the donut charts. Stakeholders can click on the truck
and can list the details about the truck on the middle
panel. Important factors listed there include the
name of the truck, route direction information,
percentage of the completed work, sustainability
metric percentage and the relationship with the
average sustainability percentage, time spend to
complete work, and time necessary to reach the
warehouse for next shipment. On the left bottom of
the dashboard, the profitability and risk metrics are
shown as a density plot. This information will be
updated in real time. Finally, on the right panel, all
relevant stakeholders are listed, including their
profile and contact information. It is also possible to
provide notification mechanisms through the
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Figure 4: Dashboard design for the supply chain level, where Truck 1 is in focus for the details.
dashboard in order to inform other stakeholders for a
specific situation.
Dashboard View 2: Warehouse Level
Figure 5 is the view of the automated warehouse
dashboard. In the warehouse level, the main focus is
the robots, AS/RSs, and metrics related to these
systems. One of the important KPIs mentioned in the
earlier section is the performance. The performance
of each AS/RS is illustrated with the stacked bar
chart, where each color represents different ASs/RSs
over a period of working hours. It is also possible to
visualize data over a week, month, or a year to see
the average performance KPI of each AS/RS. The
user can hover on the bar chart to learn the exact
number of packages picked and placed by a
particular AS/RS.
Moreover, the autonomous robot status is illustrated
in real time by a dot map. The location of each robot
is shown in this dot map visualization. The re-
charging area and location of the conveyor belts are
also included in this map in order to observe the
behavior of robots. A user can learn more about a
particular robot by clicking on a dot. When a robot is
selected, an arrow illustrates the direction of the
movement. Furthermore, the middle panel is
designed to list important information related to the
selected robot. For this example, this information
includes the name of the robot, direction of the
movement, battery status, safety status, performance
and its relationship with the average performance,
activity time of the robot and its relationship with
the average activity, and expected time of the
recharging to return back to the warehouse floor.
Users can check the current situation of any other
CPSs by clicking on their representation (label or
element of visualization) to update the information
in the middle panel. On the left bottom of the
dashboard, the energy consumption status of each
robotic arm is summarized. The donut chart
visualization technique is used to show this
information. The energy consumption metrics are
important to calculate the sustainability KPI of a
task or the warehouse as a bigger system.
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205
Figure 5: Dashboard design for the warehouse level, where Autonomous Robot 1 and AS/RS1 are in focus for the details.
Dashboard View 3: Intelligent Agent
Level
The last dashboard design is illustrated in Figure 6.
In this dashboard, the intelligent agent level is
represented to inform stakeholders such as
intelligence developer(s) and systems engineer. In
this design, similar to the earlier Figures 4 and 5, the
stakeholders are listed on the right panel. In the
middle information panel, KPIs related to a single
intelligent agent (IA 2) are summarized. This
information includes the name of the agent, the task
it is working on, memory usage, performance and its
relationship with the average performance, and time
for the task and its relationship with the average.
On the left side of the dashboard, a combination
of a sunburst diagram and a chord diagram is used to
visualize the active IAs and the interactions between
them. The order of the sunburst diagram starts with
the inner ring, where the IAs are illustrated with
different colors. Then, the larger ring shows the
active tasks for each IA. The last two outer rings
illustrate the working memory and long-term
memory, respectively. The viewer of the dashboard
can get information about a specific slice or chord
by hovering over it. Moreover, the viewer can view
detailed information by clicking on any agent and
making the middle information panel active for that
specific agent.
It is important to know the amount of pre-defined or
acquired knowledge for a specific agent, speed of
gaining new knowledge, and amount of knowledge
used between IAs. Moreover, in case an agent does
not have knowledge in memory, a stakeholder needs
to know the amount of time and energy needed for a
search of information. Such KPIs can help identify
the quantity of gained useful knowledge that should
be shared between robots, or identify useless
knowledge that should be forgotten. For this reason,
a node link diagram illustrates the information
interactions between the selected IA and other IAs.
This visualization shows the useful knowledge that
has been used and shared with other IAs and the
relationships associated with it.
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Figure 6: Dashboard design for the intelligent agent level, where Intelligent Agent 2 is in focus for the details.
4.3 Data Measurement
At the end of the case study, a dashboard was
designed to visualize important KPIs about the three
different levels: (1) supply chain, (2) automated
warehouse, and (3) the intelligent agent. This
dashboard design was developed according to expert
opinions for the purpose of improving the
understanding of interoperability, knowledge
reusability, sustainability, profitability, risk, and
safety of the system.
The input data for each KPI needs to be well-
structured so that one can easily develop a
dashboard that responds to a real-time stream of
input data connected to an intelligent automated
warehouse. One of the aims of this study was to
generate necessary data collection methods. To this
end, the KPIs and data needed to actualize the KPIs
are listed below:
Interoperability: To visualize the
interoperability KPI, one needs information about
the interactions between entities. In the dashboard
design, interoperability is visualized on the IA tab
where the communications between intelligent
agents are represented in the chord diagram. To be
able to generate this visualization, data about the
interactions between the robots needs to be logged.
For instance, whenever a CPS encounters another
CPS, the interaction should be saved by stating the
names of the IAs, their actions, duration of the
interaction, and so on. Moreover, this data should
also include time stamps, so it can be tracked on
time. Categorization of the interactions according to
different actions can give information to the user
about which type of actions requires better
interoperability, and then the stakeholder can make
prioritization decisions on these actions. To collect
this useful data one can use sensors on/around the
robots and other CPSs and/or network data.
Sustainability: The sustainability KPI is
visualized on different levels to inform the user
about the energy consumption situation of the
system. Any data related to power, energy, and
battery life needs to be saved for this purpose. More
details about the truck, route distance, and fuel
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207
consumption can give a more holistic view about the
whole supply chain sustainability.
Knowledge Reusability: The development
process of knowledge is visualized on the IA level.
The Soar architecture includes an epidemic memory
as a record of an agent’s stream of experiences. Each
chunk of knowledge inside the agent’s WM could
have a “level of reusability” attached to it. The
aggregation of all these values across all agents can
be used to calculate/measure the knowledge
reusability KPI. One can include initial set of
knowledge components such as goals, milestones,
self-knowledge, and other agents (Taylor et al.,
2002) for further analysis.
Performance: The performance KPI is very
much related to time. Log files about each CPS’s
task should be collected for this purpose. Time-
stamped data related to the goal, process, and details
about each robot’s name, position, battery situation
are needed to generate the visualization(s).
Safety: Safety-level-related data could be
acquired by a set of sensors located inside the
warehouse. These sensors can detect human
existence and change the level of safety for specific
robots, distribute this information through the
network, and use different notifications to inform
both CPSs and humans. However, more detailed
safety requirements are needed to understand the
safety-related measures. For example, in case of a
human-robot interaction, one should consider
movements of the robots that can cause hazards to
humans surrounding them. To prevent accidents, it is
necessary to identify dangerous or potentially
harmful movements. This is especially difficult in
cases where autonomous robots are included since
such robots share the warehouse space with humans
instead of having dedicated spaces. During the case
study, we identified safety as an important KPI to
consider, but did not develop any visualizations or
specific data needs for its assessment. We have
listed this KPI among others since it is crucial to
consider. Further work on understanding existing
safety standards, such as ISO: ISO/TS 15066
(International Organization for Standardization,
2016), and on identifying cases where humans will
be present in an autonomous warehouse is essential
to extract the data needs for the safety KPI. Safety
and necessary data to observe safety will be detailed
in future studies.
Risk: This is shortage risk, or the risk of not
being able to deliver the expected products in due
time according to plan. To calculate the risk KPI,
inventory levels and customer ordering events needs
to be known. With this, expected consumption is
estimated and fed into a model, which relies on a
Poisson distribution to estimate shortage risk.
Profitability: In order to calculate profit, one
needs the measured "revenue," "inventory cost," and
"transportation costs" of the whole cycle. We also
need the "missed revenue," which is calculated from
the expected revenue, based on how many sales
would be lost if there were shortages of specific
products.
5 DISCUSSION
This study showed that it is vital to identify
important KPIs and the need of data as a preliminary
stage of the project to be able to assess them before
designing and implementing the system. Moreover,
the case study illustrated how one can use these
KPIs with different data visualization techniques in
order to develop dashboards.
Another aim of the study was to understand
required methods for designing an intelligent
complex logistics operations system. Choosing a
cognitive architecture for modeling intelligent agents
in our scenarios was motivated by a number of
factors. Decision-making in a cognitive architecture
happens similar to how it happens in the human
mind, albeit at a higher abstract level. Thus,
extracting an explanation from the agent for why it
made a particular decision is more straightforward.
This AI capability, of providing explanations for its
decisions, has been gaining much importance in the
design of current and future autonomous systems
(Goodman and Flaxman, 2016).
In addition, cognitive architectures tend to
provide a framework for developing very general
agents, which ought to be applicable to many
different domains. Generic software that can be
applied to many domains with little extra
engineering needed tend to lower time to market and
reduce costs.
Despite the exploratory nature of this study, we
endeavored to validate the findings by different
methods. Using expert opinion at this preliminary
stage of the research is a fast and comprehensive
way to structure the concept and to identify the
needs. In the future, we plan to employ user
experience methodologies, where specialized
research tools can capture the participant behaviors
and attitudes when going through some scenarios.
This kind of formal laboratory user studies can
provide more details about the usage of the
dashboard and help to draw clear conclusions.
However, designing and running controlled
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experiments requires substantial time and resources.
Formal laboratory user studies might even be
inappropriate during an exploratory phase of
research when clear objectives and variables might
not yet be defined (Tory and Möller, 2005). Tory
and Möller (Tory and Möller, 2005) summarize this
as “formal laboratory user studies often focus on
perceptual or simple cognitive tasks. High-level
cognitive tasks (for example, thinking, deciding, and
exploring ideas) are important activities, yet
performance of these tasks is difficult to measure
objectively and quantitatively.”
6 RELATED WORK
Several graphical displays have been developed for
cognitive models. Even though it is not common to
have a graphical display for every model, there are
models that come with graphical displays, and these
displays are used to explain the models. Some
relevant graphical displays are summarized below to
highlight their capabilities and how this particular
study differs from them:
APEX (Freed, Shafto and Remington, 1999)
modelling framework is a tool that
automatically generates pictorial
representations of the actions associated with
the models and their dependencies. It uses
pert charts for a critical path analysis for the
analysis of total task time.
The Developmental Soar Interface (DSI) was
created to support model creation, debugging,
and presentations for Soar architecture. It
provides the ability to understand and
manipulate process models built within Soar.
The Tcl/Tk Soar Interface (TSI) (Ritter, Jones
and Baxter, 1998) provides multiple views of
the working memory and decision processes
of a Soar agent, including a semi-graphical
trace of the goal stack and the operators in a
Soar model.
The Situational Awareness Panel (SAP)
(Jones, 1999) provides a number of views of a
synthetic agent. These views are updated
continuously during the lifetime of the agent
and aim to support users to inspect the
reasoning processes of the agents.
The Visualization Toolkit for Agents
(VISTA) (Taylor et al., 2002) provides insight
into an agent's internal reasoning processes.
VISTA allows agent developers, subject-
matter experts, and other stakeholders to
verify the correctness of an agent's behavior
without requiring technical details of the
implementation. It uses Gantt and pert charts.
The Categorical Data Display (CaDaDis) is an
extension to VISTA. It offers pert charts to
show tasks by category, nonstandard pert
charts that show the temporal dependencies,
and Gantt charts that help show occurrences
of agent events along a time line
In this paper, data visualization and visual
analytics techniques, rather than pictorial or
graphical displays, were exercised to visualize the
important KPIs in order to improve the
understanding of intelligent agents and support
stakeholders in their decision-making processes.
Unlike in earlier graphical display aids, these KPIs
do not necessarily focus only on the behavior,
situational awareness, the agent’s working memory,
and long-term or short-term knowledge.
Furthermore, this study aimed to develop data
collection, mapping, selection, presentation, and
interactivity methods to generate these data
visualizations.
7 CONCLUSIONS
This study aimed to conceptualize the needs for
complex logistics operations where cooperative
robots, intelligent transportation systems, and
stakeholders related with the system can work
together. We have identified three different levels of
this CPS: supply chain, warehouse, and intelligent
agent. The important KPIs related to the system are
interoperability, sustainability, knowledge
reusability, performance, safety, risk, and
profitability. Moreover, the supply chain manager,
warehouse manager, truck driver, systems engineer,
warehouse staff, and intelligence developers are also
recognized as essential stakeholders who would
have access to and use the dashboard to support their
decisions.
Future work will be to extend the existing
simulation to provide useful data identified by this
study, for the use of dashboard implementation. To
develop KPIs further, surveys with relevant
stakeholders may be conducted. Furthermore, formal
laboratory user studies will be designed and
conducted as a next step to assess the success of the
dashboard design.
Data Visualization Support for Complex Logistics Operations and Cyber-Physical Systems
209
ACKNOWLEDGEMENTS
The authors gratefully acknowledge discussions,
contributions, and helpful suggestions from Aneta
Vulgarakis, Rafia Inam, Carlos Azevedo,
Konstantinos Vandikas, Leonid Mokrushin, Ricardo
Souza, Elena Fersman, and Martin Törngren.
The research leading to these results has received
funding from the “SCOTT - Secure Connected
Trustable Things.” SCOTT (www.scottproject.eu)
has received funding from the Electronic
Component Systems for European Leadership Joint
Undertaking under grant agreement No. 737422.
This Joint Undertaking receives support from the
European Union’s Horizon 2020 research and
innovation programme and Austria, Spain, Finland,
Ireland, Sweden, Germany, Poland, Portugal,
Netherlands, Belgium, and Norway.
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