Real-time Operational Dashboards for Facilitating Transparency in

Supply Chain Management: Some Considerations

Siv Merethe Magnus

1 a

and Amit Rudra

Curtin University, School of Management, Kent Street, Perth, Australia

Keywords: Real-time Data, Intuitive Dashboard, Cognition, Supply Chain.

Abstract: The real-time sharing of data has created a unique opportunity to design software applications for the purpose

of improving operations in a supply chain (SC) - both horizontally and vertically. As a result of these

developments, dashboards have been designed to facilitate transparency - providing a better overview of a

specific operation. In this paper, we outline our research to show that most of the operational dashboard

designs are data driven but only a very few of them are designed from a user’s perspective. Further, not many

in their design process tap into the benefits of building a dashboard based on the principles of cognition. We

argue that building dashboards based on how our brain is wired will result in enhancing the decision-making

processes for a Supply Chain.

1 INTRODUCTION

For the past two decades, the introduction and use of

the Internet and various software applications have

radically changed the working environment and

possibilities in a supply chain. This has resulted in a

re-design of the supply chain to meet the various

needs of different stakeholders. Information sharing

platforms and real-time data connectedness have

made it possible for the different members in a Supply

Chain to capitalize on the information and its

accuracy. However, in spite of real-time access to

vast databases of information there is still room for

improvement in creating better software applications

and tools with the goal of increasing the transparency

and user friendliness which will enable personnel to

work more efficiently in the supply chain

management (SCM) process (Chopra and Meindl,

2013). Today, the needed information is available but

is not always extracted and displayed in a manner

conducive to the user for a quick overview of

resources available (Records and Shimbo, 2010). Of

course, various limitations and restrictions dictate

availability of storage of spare inventory for example

on offshore oilrigs. Due to the very limited storage

capacity on offshore rigs inventory planning and

https://orcid.org/0000-0002-9125-1483

https://orcid.org/0000-0001-9992-0154

“warehousing” are critical factors. However, there is

an opportunity to capitalize on the real-time data

capabilities that IT can provide today. The movement

of goods and delivery of services in the supply chain

are a major part of the operational day-to-day tasks of

an operations manager and his/her team. In the North

Sea, the operations on a rig run 24 hours a day and

365 days a year. Currently, there is a huge potential

to improve the supply chain in terms of inventory and

logistics handling to become more transparent,

efficient and sustainable by tapping into the potential

of exploiting accessible real-time data and IT

capabilities in order to create a more seamless and

dynamic flow of information integrating the members

in the supply chain. For example, in oil and gas

drilling operations, an operational and customized

dashboard can give the user a timely overview of real-

time logistics. In this research, we focus on this aspect

of dashboard and argue that there is a need for more

research into this aspect of cognition as related to

operational dashboards.

The rest of the paper is organized as follows.

Section 2 presents the background literature, while

research methodology is explained in Section 3. In

Section 4, we present data collection process. Section

5 presents our analysis and findings. We conclude our

research in Section 6.

Magnus, S. and Rudra, A.

Real-time Operational Dashboards for Facilitating Transparency in Supply Chain Management: Some Considerations.

DOI: 10.5220/0007721304330443

In Proceedings of the 21st International Conference on Enterprise Information Systems (ICEIS 2019), pages 433-443

ISBN: 978-989-758-372-8

433

2 BACKGROUND LITERATURE

To date, many businesses face the challenge of having

numerous information systems within their own

company and their strategic supply chain partners

share the same predicament, namely their internal

systems are not integrated in such a manner that it

gives them easy access to real-time data (Chopra and

Meindl, 2013, Flynn et al., 2010). The potential for

creating new real-time information platforms to

ensure flow upstream and downstream in the supply

chain, will result in a higher optimized visibility and

accurate information. This will decrease the

uncertainty and need for counter measures to mitigate

slack due to inadequate and wrong information,

resulting in less manpower and buffers in the SC,

saving time, money and resources. Good cooperation

through information-sharing platforms is a win-win

for all parties involved. The purpose of the study is to

research and potentially discover the gap between

theory and practice in terms of the availability of a

dashboard that is user-friendly. If the potential gap is

found, use the findings for further research to build a

model dashboard for operational purpose. Numerous

dashboards are in use for logistics and SCM purposes

in the oil and gas industry. The objective of this

research is to examine potential gap of the utility of

such dashboards, used for monitoring and retrieval of

information.

The scope of the research will be limited to

investigating the field of IT for dashboards that are

already in use for logistics purposes via secondary

data such as peer articles books and professional

journals and databases, Google Scholar, and Google

Grey Web. Principally, we shall use bibliometrics as

our research methodology.

2.1 Supply Chain

The main objectives of supply chain management are

to maintain a high level of customer satisfaction,

minimize costs, and improve the flexibility of system

controllers. It is important to understand the role that

information availability plays in a supply chain. In

this regard, it can be viewed as a flow. For example,

Figure 1 shows the flow in a global environment,

showing the demand flow, which results in

forecasting for products and services, information

flow and processes linked with these flows and

financial resources. The customary business

functions starting with marketing, sales, research and

development, forecasting derived from sales

expectations lead to the purchasing of raw materials,

production and logistics utilized through information

systems.

In Figure 1, supply chain levels can be interpreted

in a historical context depicting the development on

how the SCM has changed. Figure 1b shows a closed

supply chain and it was handled by vendor took care

of all aspects of the SC itself. However, further down

the line the need for extended supply chain was more

stakeholders were added on, but still in a closed SC.

The ultimate supply chain (Figure 1c) structure

represents the global supply chain scenario today.

Being highly complex and challenging to manage for

the partners involved. One of the advantages is that a

third-party financial provider is involved and shares

the risk. The organizational structure has an outside

logistics (3PL) company that executes the logistics,

serving the two companies and an external marketing

company that provides information about the crucial

customers. Figure 1c, represents the structure of how

oil and gas companies manage the supply chain.

Figure 1: Supply chain in a globalized contest. [Adapted

from Mentzer (2001, 5)].

2.2 Administration of Logistic Tasks

According to Stadtler (2015) “Inter-organizational

collaboration is a necessity for an effective supply

chain”. Today, many companies still face the

challenge of having several information systems

within their own company, resulting in not having

fully integrated real-time data information flow. This

is due to systems having been developed over time

and not always being adaptable to the new generation

of technology and integration of in-house data. In

addition, in an extended supply chain, partners in the

company may also have their own variations and lack

of data flow (Simchi-Levi et al., 2008). The rapid

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development of new technologies creates an

opportunity for supply chain strategies to change the

process cycle in supply chain and become more

integrated, resulting in increased interaction between

the stakeholders. Information, facilitated through

information platforms, is shared among the supply

chain partners. This will result in higher transparency

and reduce the uncertainty and bullwhip effect

occurring in the supply chain (Kvie, 2015).

According to Pereira (2009), the source of

inefficiencies in a supply chain is information that is

wrong, lacking or inaccurate. These scenarios

represent a considerable challenge for supply chain

management. Therefore, accurate and timely

information is immensely important (Verissimo

Pereira, 2009). To mitigate the challenge of lacking

visibility in the supply chain, the typical

countermeasure would be to have more staff and

increased inventory, resulting in higher overall costs.

Gartner analyst, Art Mesher, developed in the 1990s

the concept of 3 Vs of supply chain: visibility,

velocity and variability, and claims that a higher

visibility throughout the supply chain will lead to

velocity (higher speed in quantity) and will reduce the

variability factors (Wilhjelm, 2013).

The importance of sharing information has

increased significantly and can be considered as

another set of dynamics and layer in SCM (Stadtler,

2015). However, one approach in optimizing the

visibility of information can be to form a closer

relationship with the transport companies and

suppliers through information systems that are

tailored to accommodate the needs of the members

across the supply chain and share information

(Frazelle, 2002). The information sharing in

collaboration with partners and customers also faces

challenges because of the high complexity of the

supply chain integrations in global networks due to

multiple layers of partners, and IT challenges due to

the vast diversity of software and hardware (Stank et

al., 2015).

2.3 Dashboard: Interactive Design

To date it seems that both academia and the industry

have not sufficiently taken into consideration the user

friendliness aspect of the dashboards they are

developing (Sharp, 2006). Presthus and Canales

(2015) found that dashboard design has mostly been

data-driven. The dashboard should be designed based

on cognitive psychology. However, it seems that few

designers have focused on the visual perception and

eye tracking that occurs when dashboards are viewed.

2.4 Human: Computer Interaction

The human–computer interaction model is a

simplification of the process and stages of the

interaction that will take place between the user and

the machine/product. The purpose for such a model is

to give the creators (designers) a better in-depth

understanding of how the dynamics are in “dialogue”

with “software” (human behavior), and to test the user

friendliness and level of integration.

Prior to the design phase, it is important that the

developers factor in human behavior, cognition, and

emotions. The human perception/cognition refers to

our everyday thought processes such as recollection,

reflection, absorbing knowledge, daydreaming,

seeing and observing, making decisions, reading and

writing (Sharp, 2006). Some of the processes are

more or less automatic and do not demand a lot of

conscious thinking - they flow effortlessly through

the human mind. Comparing and contrasting, making

decisions, and doing tasks that require specific skills

trigger creativity which leads to new ideas (Sharp,

2006).

2.5 The Design Process

The design process has the following dimensions:

Visceral Design: the visual physical appearance of

the product and aesthetics such as colours and layout.

Behavioural Design: research has shown that the

choice of correct aesthetics will influence the users’

perception of usability and how the product “feels”

while being used (Bonnardel et al., 2011).

Reflective Design: is the opinion that the user has of

the product and whether the user felt positive or

negative about the experience of using it (Bonnardel

et al., 2011).

However, more factors need to be considered in

the process of creating a dynamically good dashboard

such as the graphic design and also the use of colours

and the impact of these on the human emotions and

aesthetics. According to the research of (Bonnardel et

al. 2011), there is a direct correlation between the

choice of colours and willingness to spend more time

on the web page when navigating through the

internet. The appeal, layout, aesthetics and colour

determine within seconds whether the user will

continue navigating the company’s web pages

(Bonnardel et al., 2011).

Real-time Operational Dashboards for Facilitating Transparency in Supply Chain Management: Some Considerations

435

2.6 Cognition Processes Implemented

in the Design of Dashboards

In the entire process in designing the intuitive

dashboard should be including the cognition

processes to optimize and capitalize on how the brain

are wired. This will in return create a tool for the end

user, which will be properly customized and

perceived easy and logically deductive.

3 RESEARCH METHODOLOGY

The research objective was to search for written

material about logistics dashboards designed for

operational purposes that are user-friendly and built

intuitively based on how the human brain is wired. In

this research, we investigate whether there is a gap

between academia and the business world in relation

to the design and actual implementation of

dashboards. Moreover, it is sought to use the findings

for further research to build a dashboard model. The

scope of the research will be limited to investigating

the field of IT for dashboards that are already in use

for logistics purposes via secondary data such as peer

articles books and professional journals and

databases, and Google Scholar. Hence, the

methodology for the study will be bibliometric, which

has the capability to canvas millions of records,

through the use of database tools, such as Scopus and

Web of Science.

Limitations of this Study

Due to the limitation of time and the size of the study,

it can only be used as a suggestion to further develop

potential improvements for real-time operational

dashboards in logistics.

4 DATA COLLECTION

In addition to regular searches on the internet, we

searched university catalogues, various books on

supply chain management and dashboard design,

peer-reviewed articles from academia and research

institutions, white papers presented at conferences,

newspapers and relevant industry journals. The

choice of databases, fell on Web of Science and

Scopus used by universities because of their size and

global span. Scopus is the largest peer-review

database in the world with 69 million records and

Web of Science has over 90 million records

(Wikipedia 2017). The “in text strings” should be

identical for both databases to ensure validity and

accuracy. “In text string” refers to the free text area

where the researchers type in, for example, key words

or phrases or topics. Using this secondary data

bibliometrics was used to enumerate through the

reports generated via the “in text string”. However, in

the planned “in text string” for dashboard design,

more keywords were added in the second search

limited to “dashboard design” and only publications

which contained those two keywords was to appear,

the (..) will command the search to only show articles

which have “dashboard design” in their content. In

this context, drilldown is a term that indicates a

deeper search of the gathered information, by

prompting the search engine to go deeper into the

information retrieved from Scopus and Web of

Science.

After searching for the most appropriate

keywords, bibliometrics analysis of “in text string”

search yielded the following:

(1) “visualization” AND “dashboard” AND “real

time”; all records were for 2006-2017. This generates

a broad search for the keywords in the databases, and

was also meant to exclude any irrelevant areas of

research or industry. The time span was set to a

decade with the purpose of investigating the research

developments that have occurred in the field.

(2) “dashboard design”, all records are for the period

2006-2017. Further, in the analysis the following

table structure has been used in the drill down in the

data from the generated database report using the

facilities of Web of Science and Scopus drill down

possibilities. In both databases, you can “pick” new

search words within the generated reports. Here after

supply chain one level down (level 2) Logistic was

tapped in and next level Oil and Gas (level 3). This

structure has been followed through the research.

When referring to findings this structure will be

presented.

Table 1: Search “supply chain” in Scopus and Web of

Science 2006-17.

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5 FINDINGS AND ANALYSIS

With regard to the number of articles in both databases

using the search string “visualization” and “dashboard

and “real time”, very little has been written over the

past decade on the topic of dashboards with only 37

articles retrieved from Web of Science and 85 articles

in Scopus, spread over at 10-year period. (Process

Model 1). This represents an average of only 11

publications per year. With regard to the research

areas, science and engineering are predominantly the

main contributors. Educational institutions appear with

only four relevant articles in Web of Science. For the

source of the publications, such as conferences, the

result for Scopus is 69.1% and for Web of Science,

62%. With regard to peer reviews, the results indicate

that articles from academia in Web of Science account

for 39% and for Scopus, 28.6%. This shows evidence

of the traditional gap between academic theory and

business practice; in the research field, these are not

aligned.

First Process Model

The search in Scopus on the string resulted in 85

matches and the same string in Web of Science yielded

37 hits (Process Model 1). Taking into consideration

the huge number in these two of the databases, the

results shows a low number of matches of the chosen

search keywords, indicates that the information about

development and research have not been published in

the public media. So, further analysis was performed

on the data available. The first step was to investigate

the number of publications in both databases according

to each year within the specified ten-year range.

Web of Science did not have any publications

between 2006-2010 and in 2014, and Scopus for the

same period showed no publications in 2006 and only

five hits between 2007-2010. A mere total of five

articles in both databases indicate that the area attracted

little interest from researchers in academia and from

the business world.

Despite the small amount of data retrieved from

Web of Science. The generated data, shows a steady

yearly increase in the number of publications, and a

drop in 2017 although this is likely to increase by the

end of the year. With regard to Scopus data depicted

the last three years (2015-17) shows a significant surge

in publications. An increase is seen in the number of

articles (122 in total) in both databases. However, the

results of the keyword search are low, given that they

represent a decade of publications. The next step was

to analyze the research areas/fields to which the articles

derive from. In Web of Science, the retrieved results of

37 publications shows 17 publications from computer

science, followed by engineering with 10 publications

and educational research with 4 publications,

representing the three major fields of contributors.

Scopus has 85 publications, contributed by:

computer science with 48 (56 %) publications,

followed by engineering with 37 (44%). In Web of

Science, of the 37 publications, computer science

accounted for 17 (46%) and engineering for 10 (27%).

In both databases, the top two contributors are the

fields of computer science and engineering. This may

be explained by the fact that these two disciplines

would be the ones involved with dashboards, real-time

data and visualization.

Having established the research areas pertaining to

the publications, the next question is: Where were the

texts published? The analysis shows for 2006-2017, of

the 85 articles from Scopus, 65.5 % were conference

papers, 3.6 % were conference reviews, 2.4% were

from books and 28.6% were from published articles. In

the 37 articles sourced from Web of Science, 61%

derive from conferences and 39% from published

articles.

Analyzing the data shows that the majority of the

research papers are (white paper level) from

conferences (Web of Science 61% and Scopus 69.1%).

Moreover, there is prima facie evidence of a classic

gap between research and practice. In Scopus, only

28.6% are peer reviewed articles and in Web of

Science, 39%. Academia is not following the

development of dashboards, visualization and real-

time. In addition, only 2.4% of the sources in the

Scopus database are published books, and %0 in Web

of Science. In industry, managers need solutions to

help with everyday operational activities. Hence, there

is a need to work towards solutions to fix current

problems, or to propose better alternatives to increase

business efficiency and decrease costs. The findings

presented in tables pertaining to the in-text searches of

Scopus and Web of Science using keywords

“visualization”, “data visualization”, “real time data”,

“cognition”, “eye tracking”, “color”, “supply chain or

logistics”, will be discussed below. Starting with

“visualization” the search of a total (930+620) 1550

publications produced 350 hits (22.6%). This result is

small when taking into consideration that the

visualization process is an essential element of the

dashboard design process. Further, in the search using

keyword “real time data” (236+114) from the 350

drilldown source, the hits were 82 (23.5%) in total from

visualization search and for a further keyword

“cognition” (61+21) 82, the total hit was 11

publications.

With regard to real-time data and cognition, the

data analysis shows that only a small percentage of

1550 articles factor in cognition in their research or

Real-time Operational Dashboards for Facilitating Transparency in Supply Chain Management: Some Considerations

437

when designing or building dashboards. Further data

collection on keyword data visualization (Table 3)

produced a total hit of 287 (18.6%) from a total of 1550

publications, which is a low figure with regard to

dashboard design process. Also, the data analysis

showed (199+88) that for 287, for the keyword “real-

time data”, the result was 80 (28%) publications. In the

last search for the keyword “cognition” there was a

total of 11 (14%) publications. Based on the results, the

conclusion drawn is that in research so far, the

elements of visualization and cognition are not

factored in to a large degree during the design and

construction of dashboards. For the next keyword

analysis “real-time data” from a total of 1550

publications, there were 233 (15%) hits. This indicates

that 233 out of 1550 publications on dashboard designs

() included real- time data.

A further level down from “real-time data” the

keyword “supply chain” OR “logistics” produced 40

(11+23) (17%), and the last step used to check the

presence of “oil and gas” produced only one

publication. In the supply chain and logistics context,

the research shows that at this point, only 17% of real-

time data in this drilldown relates to the field. With

regard to the search using the keyword “cognition”

(Table 4), of 1550 publications, the total was (57+4) 61

publications (4%), indicating a very low representation

of cognition concepts used in dashboard design. With

regard to the next search using “supply chain or

logistics” as the keywords, (57+4) 61, the results were,

11 articles, and further one level down “Oil and Gas”

search produced four articles. The results of the

cognition search show a very low level of published

research in this area, considering that the data search

covers over a decade of articles globally. The next step

is related to the cognition process and how the brain

processes information. Eye tracking is a visual cortex

activity. The following results for the search using

keyword “eye tracking” are: of 1550 publications,

there were 34 articles (2%) on eye tracking; further

drilldown from the eye tracking results for a

“perception” search produced a total of 11 articles and

further drilldown found six articles with the keyword

“cognition”. Part of the cognition process is also how

the brain processes colors and color coding. Hence, the

next keyword search was “color”. The analysis shows:

Out of 1550 publications, the search produced (43+11)

54 (3.5%). in the next search using the keyword

“perception” yielded a total of 22 articles and the

keyword “cognition” resulted in five articles.

The findings to date in the key word search

regarding brain wiring show that only a small

percentage of designers consider designing dashboards

based upon the capability and limitations of the human

brain and how it is wired. It is evident that there is a

gap between academia (universities) and practitioners

(businesses). When a theory or process coming from

academia is deployed by an organization, quite often,

it does not work. The theory practice gap between the

management researcher and the practitioners is due to

the fact that they are looking at a process or system

from different perspectives. The practitioners are in an

environment where theories are put into actual

practice, and where the focus is to obtain knowledge

that employees and management can utilize in their

daily operations. Business researchers have a different

perspective in that they are examining theories in the

field, seeking to gain more knowledge from a more

intellectual stance by posing critical questions. The

methodological imperative is also different. For the

researcher, everything is executed according to a strict

methodology and scientific methods. For the

practitioners, it is an ongoing process imbedded in their

everyday business activity and their aim is to solve the

challenges and problems they face and fix it.

On the topic of dashboards with only 37 articles

retrieved from Web of Science and 85 articles in

Scopus, spread over at 10-year period. This represents

an average of only 11 publications per year. With

regard to the research areas, science and engineering

are predominantly the main contributors. Educational

institutions appear with only four relevant articles in

Web of Science. For the source of the publications,

such as conferences, the result for Scopus is 69.1% and

for Web of Science, 62%. With regard to peer reviews,

the results indicate that articles from academia in Web

of Science account for 39% and for Scopus, 28.6%.

This shows evidence of the traditional gap between

academic theory and business practice; in the research

field, these are not aligned. The next step in the

research process is to analyze the data generated by the

search of Web of Science and Scopus, to find what has

been published in the area of supply chain and logistics

with regard to real-time dashboards. The purpose of the

analysis is to determine and further mitigate the

aforementioned gap between research and practical

application. The following keywords, “supply chain”,

“visualization”, “real time data”, “dashboard” were

used for the text search of relevant material in the

reports generated from Scopus and Web of Science.

(Process Model 1). Drilldown search in collected data

reports from databases Scopus (85 hits) and Web of

Science (37 hits), searching in text “visualization”

“dashboard” and “real time”.

As shown in Table 1, further drilldown using the

keyword “supply chain” shows that out of 85 Scopus

publications, only three were found in the area of

supply chain and logistics, and two for oil and gas. The

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same drilldown was performed in Web of Science,

returning 0 for all categories. In both databases

comprising a total of 122 hits, the three and one

publications respectively are an extremely low number

given that the data was collected for a one-decade time

span. The next keywords to be used for the text search

were “visualization”, “real time data” and “cognition”.

The results from the drilldown show for the 85 Scopus

publications, real-time data totals 77 and cognition, 9.

In Web of Science, 37 (level 1) mention visualization,

33 consider real-time data (level 2), and cognition had

only article (level 3).

The topic of dashboards with only 37 articles

retrieved from Web of Science and 85 articles in

Scopus, spread over at 10-year period. This represents

an average of only 11 publications per year. With

regard to the research areas, science and engineering

are predominantly the main contributors. Educational

institutions appear with only four relevant articles in

Web of Science. For the source of the publications,

such as conferences, the result for Scopus is 69.1% and

for Web of Science, 62%. With regard to peer reviews,

the results indicate that articles from academia in Web

of Science account for 39% and for Scopus, 28.6%.

This shows evidence of the traditional gap between

academic theory and business practice; in the research

field, these are not aligned.

The next step in the research process is to analyze

the data generated by the search of Web of Science and

Scopus, to find what has been published in the area of

supply chain and logistics with regard to real-time

dashboards. The purpose of the analysis is to determine

and further mitigate the aforementioned gap between

research and practical application. The following

keywords, “supply chain”, “visualization”, “real time

data”, “dashboard” were used for the text search of

relevant material in the reports generated from Scopus

and Web of Science. (Process Model 1). Drilldown

search in collected data reports from databases Scopus

(85 hits) and Web of Science (37 hits), searching in text

“visualization” “dashboard” and “real time”.

As shown in Table 1, further drilldown using the

keyword “supply chain” shows that out of 85 Scopus

publications, only three were found in the area of

supply chain and logistics, and two for oil and gas. The

same drilldown was performed in Web of Science,

returning 0 for all categories. In both databases

comprising a total of 122 hits, the three and one

publications respectively are an extremely low number

given that the data was collected for a one-decade time

span. The next keywords to be used for the text search

were “visualization”, “real time data” and “cognition”.

The results from the drilldown show for the 85 Scopus

publications, real-time data totals 77 and cognition, 9.

In Web of Science, 37 (level 1) mention visualization,

33 consider real-time data (level 2), and cognition had

only article (level 3).

Table 2: Results of “Real-time data” search in Scopus and

Web of Science 2006-2017.

The findings of over 100 publications (77+33)

(level 2) on real-time data, presented in Table 2 above,

validate the prima facie evidence that the visualization

element is a factor in real-time data research and

processes. However, only 10% of the 100 publications

show that the cognition theory, based on how the brain

perceives patterns, has been applied when designing

dashboards. Further investigation into “real-time data”

in connection with logistics in the oil and gas industry

in the visualization drilldown produced the following

results. The results of the search using “real time data”

as the keyword. In Scopus, 77 out of 85 (91%)

considered ‘real-time” data; four articles were the

result of searching for “logistics”, and “oil and gas”

retrieved 0. In Web of Science the same drilldown

shows 37 of 37 in the search for “real time data”,

“logistics” zero (0) and “oil and gas” zero (0).

Scopus analysis shows, 85 for the search of

“dashboard”, and further drilldown produced three

(3.5%) for “supply chain” publications and zero (0) for

“oil and gas”. In Web of Science 37 publications

contained the keyword “dashboard”, but “supply

chain” and “oil and gas” returned a result of zero. The

same method was applied when searching for

dashboards in relation to supply chain, logistics, and oil

and gas. In Web of Science the top of string (37) list

was applied in each search for “supply chain”,

“logistic” and “oil and gas” to support the claim that

little has been done with regard to supply chain

dashboards related to logistics in oil and gas.

Preliminary Summary

It can be observed from the above search results that

the outcome of the in text string search of Scopus and

Web of Science indicate a significant trend, viz. there

has been an increase number of publications in

searched topic from 2015-2017. Further, the research

publications over the past decade have been mainly

contributed by computer science (64), engineering

(47). As prima facie evidence, a theoretical practice

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439

gap has been found between academia and the business

world since the research stemming from conferences

accounts for a majority of 61% in Web of Science, and

69.1% in Scopus. Peer review publications in Scopus

account for 28.6%, and 39% in Web of Science.

Academia and the business world are not in sync.

We now recap the findings from the drilldown into

respective data reports from Scopus and Web of

Science, generated through in-text searches using

keywords. Table1 shows the search results using the

keyword "supply chain" for the period 2006-2017. In

both databases, only three publications were found for

this period. This indicates very low research activity in

the field, for no apparent reason. Table 3 shows the

search results using the keyword "visualization" for the

period 2006-2017. The results indicate that the

research field of visualization is a significant element

in real-time data research, although 22 (26.84%)

publications did not consider real-time data. The search

results using the keyword "real-time data" for the

period 2006-2017. The hit on real time data was high

in the reports, returning114 out of a total search of 122.

However, with regard to logistics, there were only four

publications. The last keyword search in process model

1. The drilldown was for the keyword "dashboard" for

the period 2006-2017. There was a 100% hit on the

search in both data reports but, consistently, “supply

chain” hits were very low at only three, and logistics a

mere two.

Second Process Model

To assess the development in the research and

performance of dashboard design over the past decade,

in the analysis referring to publications, the results

from 930 articles shows a noteworthy increase in the

number of publications from 2012 to 2017, with a total

of 682 (73.3%) publications, compared with only 248

(26.7%) publications for 2006-2011. In the analysis

results of 620 articles shows the same trend: an

increase from 2012-2017 to a total of 499 (80.4 %)

publications, compared with 121 (19.6%) publications

from 2006-2011.

Both databases show the same trend with regard to

an increase in the number of publications from 2012-

2017. Adding up the reports in the two databases, there

have been 1181 publications in total over the last five

years, compared with 369 publications for 2006-2011.

These statistics indicate that the implementation of

dashboards within the business world has increased

and this trend is likely to continue in the future. The

analysis of the data from the two databases shows the

results of the search for written texts and the category

to which each belongs.

Out of 930 publications found in the Scopus

database, conference papers account for 507

publications, (i.e. 58.6%, comprised of conference

papers and conference reviews) and 313 (33.7%)

publications are articles. Thirty three (3.5%)

publications were book chapters.

Further, it shows that out of 620 publications found

in the Web of Science database, conference papers

account for 317 publications (i.e. 51.2%), and 292

(47.7%) are articles. Six (1%) publications were book

chapters.

The analysis of data acquired from Scopus. It

confirms the theory- practice gap between, academia

and conference (white paper level) is 24.9% comparing

the findings, the same gap is only 3.5% which indicates

only a small difference between academia and

conference papers these results are evidence of prima

facie. The large difference in results may be due to the

different population of the database reports with 310

more hits on Scopus using the keyword in the text

search. Having determined the categories of each text

type, the, next step will be to analyze the research area

segmentation.

Regarding the 930 publications the contributing

fields can be summarized as follows: computer science

(the largest) – 461 (49.6%); engineering - 318 (34%);

medicine – 135 (14.5%); social science – 117 (12.6%);

mathematics – 110 (11.9%); and business management

– 97 (6%). Out of 930 publications, the largest research

categories derive from computer science, 461

publications (49,6 %) is the area which has the most

publications followed by engineering 318 publications

(34%), Medicine 135 publication (14,5%) social

science 117 publications (12,6%), Math 110

publications (11,9%) and business management 97

publications (6%). Further analysis of the data shows:

computer science has 227 (36.6%) publications,

engineering -181 publications (29.2%); educational

research - 50 (8%), business economics - 48 (7.7%) are

the business economics.

To summarize the findings so far, with regard to

publications for 2006-2017, shows a much larger

population than the search on “visualization”

dashboard and real time which had 122 publications 85

in Scopus and 37 in Web of Science. In the new search

of the databases, over 1550 publications were found,

930 in Scopus and 620 in Web of Science. The total

number of publications found by the searches of the

two databases was 1687.

With regard to the number of publications per year

in both text searches, there was a substantial increase

in publications in both Scopus and Web of Science. As

discussed earlier, there was a significant increase in the

number of publications from 2012 to 2017 with a total

of 682 publications compared with 248 publications

from 2006 to 2011. Further study shows the same

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trend: 121 (19.6%) publications from 2006 to 2011,

increasing to a total of 499 (80.4 %) for 2012 to 2017.

The majority of publications comprise conference

papers and proceedings, followed by articles. Further

analysis shows: (Scopus data analysis), the gap

between academia and conference (white paper level)

are 24.9%, in comparison with data from Web of

Science the gap is 3.5% which indicates only a small

difference between academia and conference paper

level. It is difficult to know why there is such a large

difference in the size of the gaps in Scopus and Web of

Science. It may be due to the numbers of relevant

articles in the databases. The next step was to identify

the major contributors to the written documentation

found in the databases. The results for both Scopus and

Web of Science showed that the majority of

contributors are from the field of computer science,

followed by engineering.

The next step is to drilldown in the Scopus and

Web of Science databases to search for texts containing

“dashboard design”. The procedure is identical to the

drilldown process for “visualization”, “dashboard” and

“real time”. The choice of drilldown key words for

further data collection as mentioned before was

carefully chosen, with the purpose of investigating the

areas concerned with the design of dashboards. The

keywords are the same as those discussed in the

literature review chapter. The purpose is to analyze

dashboard design in terms of visualization and, real-

time time data, and to determine how many researchers

and designers apply knowledge of cognition, eye

tracking and color when designing dashboards. Using

the keywords, the results of the drilldown were 930

publications for Scopus and 620 for Web of Science.

Drilldown search in collected data reports from

databases Scopus (930 hits) and Web of Science (620

hits), searching in text “dashboard design”.

Out of 930 publications “visualization” appeared in

236 articles (level 1); further drilldown using the

keyword “real time data” generated 61 (level 2)

articles. When determining how many of the real-time

data dashboards were based on “cognition”, the result

of the search was 10 articles (level 3). Of the 620 Web

of Science publications, there were 114 articles for

“visualization” (level 1), 21 articles for “real time data”

(level 2), and further drilldown found 1 articles related

to “cognition” (level 3). The next search was

conducted using “data visualization” to determine how

many of the 1550 publications contained the term. The

results are given in the table below. For “real time data”

(level 2), and further drilldown found 1 articles related

to “cognition” (level 3). The next search was

conducted using “data visualization” to determine how

many of the 1550 publications contained the term. The

results are given in the table below.

Table 3: Results of “Data Visualisation” search in Scopus

and Web of Science 2006-2017.

In Table 3 above, out of 930 publications in

Scopus, the keyword “data visualization” yielded 199

(21.4%) articles (level 1), and further drilldown using

the keyword “real time data” found 61 articles (level

2), and further drilldown showed a result of 10 for

“cognition” (level 3). Out of 620 Web of Science

publications the keyword “data visualization”

retrieved 88 articles (level 1), and further drilldown

using keyword “real time data” produced 19 articles

(level 2) and for “cognition” one article (level 3). To

ensure that the real-time data aspect was covered,

another search using the keyword “real-time data”

was generated, to search for publications in supply or

logistics. The results of the data search in Scopus

were: 160 articles using keyword “real-time data”

(level 1), 11 publications for keyword ‘supply chain”

or “logistics” (level 2) and only one was found for “oil

and gas” (level 3). The same drilldown was done in

Web of Science. Out of 620 publications, the

keyword “real- time data” had 73 hits (level 1); the

keyword “supply chain” or “logistics” yielded 23

(level 2), and keyword “oil and gas” had 0 hits (level

3). In the next keyword drilldown with regard to

cognition in both databases, the purpose is to check

how many of the 1550 publications on dashboards,

consider the cognition process in relation to

dashboards and, further, specifically focus on supply

chain or logistics in relation to the gas and oil

industry. The results are given in the table below.

Table 4: Results of “Cognition” search in Scopus and Web

of Science 2006-2017.

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For the “cognition” keyword drilldown, out of 930

publications in Scopus, 57 articles were found (level 1)

and further drilldown using keyword “supply chain” or

“logistics” found 11 articles (level 2), and for “oil and

gas” there was a zero result (level 3). In Web of Science

out of 620 publications, “cognition” yielded four

articles (level 1), further drilldown produced no articles

at levels 2 and 3. An additional step is the search for

“eye tracking” which is part of the brain’s cognition

process. The results of the keyword search are

discussed below. In the keyword search on color, the

results are as follows, in Scopus 930, resulted in 43

articles (level 1), further drilldown on keyword

“perception” 20 articles (level 2) and “cognition” 5

articles (level 3). Identical drilldown on keywords in

620 Web of Science publications resulted in 11 articles

for “color” (level 1), “perception” had two articles

(level 2) and “cognition” had zero (0) articles (level 3).

Table 5: Results of “Color” search in Scopus and Web of

Science 2006-2017.

The analysis shows in table 6: the following

results after in-text drilldown, firstly in 930 Scopus

publications, keyword “supply chain” has 46 articles

(level 1) and further drilldown “logistic” yielded 18

articles (level 2), and “oil and gas” one article (level

3). Identical process in keyword drilldown was

performed in Web of Science and keyword “supply

chain” produced 19 articles (level 1), “logistic” 4

articles (level 2) and “oil and gas” produced 0 (level

3).

The findings presented in tables pertaining to the

in-text searches of Scopus and Web of Science using

keywords “visualization”, “data visualization”, “real

time data”, “cognition”, “eye tracking”, “color”,

Table 6: Search “supply chain” in Scopus and Web of

Science 2006-17.

supply chain or logistics”, will be discussed below.

Starting with “visualization” the search of a total

(930+620) 1550 publications produced 350 hits

(22.6%). This result is small when taking into

consideration that the visualization process is an

essential element of the dashboard design process.

Further, in the search using keyword “real time data”

(236+114) from the 350 drilldown source, the hits

were 82 (23.5%) in total from visualization search

and for a further keyword “cognition” (61+21) 82, the

total hit was 11 publications.

With regard to real-time data and cognition, the

data analysis shows that only a small percentage of

1550 articles factor in cognition in their research or

when designing or building dashboards. Further data

collection on keyword data visualization (Table 3)

produced a total hit of 287 (18.6%) from a total of

1550 publications, which is a low figure with regard

to dashboard design process. Also, the data analysis

showed (199+88) that for 287, for the keyword “real-

time data”, the result was 80 (28%) publications. In

the last search for the keyword “cognition” there was

a total of 11 (14%) publications. Based on the results,

the conclusion drawn is that in research so far, the

elements of visualization and cognition are not

factored in to a large degree during the design and

construction of dashboards. For the next keyword

analysis “real-time data” from a total of 1550

publications, there were 233 (15%) hits. This

indicates that 233 out of 1550 publications on

dashboard designs () included real- time data.

A further level down from “real-time data” the

keyword “supply chain” OR “logistics” produced 40

(11+23) (17%), and the last step used to check the

presence of “oil and gas” produced only one

publication. In the supply chain and logistics context,

the research shows that at this point, only 17% of real-

time data in this drilldown relates to the field. With

regard to the search using the keyword “cognition”

(Table 4), of 1550 publications, the total was (57+4)

61 publications (4%), indicating a very low

representation of cognition concepts used in

dashboard design. With regard to the next search

using “supply chain or logistics” as the keywords,

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(57+4) 61, the results were, 11 articles, and further

one level down “Oil and Gas” search produced four

articles. The results of the cognition search show a

very low level of published research in this area,

considering that the data search covers over a decade

of articles globally. The next step is related to the

cognition process and how the brain processes

information. Eye tracking is a visual cortex activity.

The following results for the search using keyword

“eye tracking” are: of 1550 publications, there were

34 articles (2%) on eye tracking; further drilldown

from the eye tracking results for a “perception” search

produced a total of 11 articles and further drilldown

found six articles with the keyword “cognition”. Part

of the cognition process is also how the brain

processes colors and color coding. Hence, the next

keyword search was “color”. The analysis shows: Out

of 1550 publications, the search produced (43+11) 54

(3.5%). in the next search using the keyword

“perception” yielded a total of 22 articles and the

keyword “cognition” resulted in five articles.

The findings to date in the key word search

regarding brain wiring show that only a small

percentage of designers consider designing

dashboards based upon the capability and limitations

of the human brain and how it is wired. It is evident

that there is a gap between academia (universities)

and practitioners (businesses). When a theory or

process coming from academia is deployed by an

organization, quite often, it does not work. The theory

practice gap between the management researcher and

the practitioners is due to the fact that they are looking

at a process or system from different perspectives.

The practitioners are in an environment where

theories are put into actual practice, and where the

focus is to obtain knowledge that employees and

management can utilize in their daily operations.

Business researchers have a different perspective in

that they are examining theories in the field, seeking

to gain more knowledge from a more intellectual

stance by posing critical questions.

6 CONCLUSION

Based on the data analysed the representation of

articles in relation to dashboards real-time data,

shows the results are extremely low in the context of

supply chain, logistic and oil and gas. This shows

evidence prima facie, a gap between academia and

the practitioners. With regard to brain wiring to date,

shows that only a small percentage of the retrieved

articles, factor in and apply the benefits of designing

dashboards based upon the capability and limitations

of the human brain and how it is wired, the same

evidence of prima facie in relation to theoretical

practice gap, between academia and practitioners

which use the dashboard in their daily operations.

Also, a low number of book chapters on the search

was detected, is evidence prima facie, theoretical

practice gap between the industry and academia.

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