Data Visualization Support for Complex Logistics Operations and

Cyber-Physical Systems

Didem Gürdür

, Klaus Raizer

and Jad El-Khoury

Department of Machine Design, KTH, Royal Institute of Technology, Stockholm, Sweden

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 identified—supply chain, automated

warehouse, and intelligent agent—to 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

goods—to 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

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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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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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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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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