Development of ERP and Other Large Business Systems in the Context of New Trends and Technologies.
Suman, Sabrina ; Pogarcic, Ivan
Development of ERP and Other Large Business Systems in the Context of New Trends and Technologies.
1. Introduction
Getting specific information for specific job-situation-task, means
that data from different sources must continuously be collected, stored
and analyzed. Care and use of so much data is impossible without a
well-structured database and/or data warehouse, and the analysis of this
data is impossible without a series of BI (Business Intelligence) tools.
Information therefore needs to be within easy reach and analyzed in
order to give effect to the business objectives [1], and information
systems should be changed, integrated and introduced precisely in order
to realize this goal. [2]
Companies whose executives understand data as a core asset and
resources of business are six times more likely to be successful than
those whose top management does not perceive data in this way (31% vs.
5%) and have a lot more success with BI projects. [3].
Technological progress and the information needs of companies today
create a continuous cycle of growing needs for ever more effective ways
of coverage and analysis of data in and around the company. Currently,
the society is faced with a growing, large and various sources of data,
which is often referred to as big data, and is used for decision
support. Big data are complex, layered, large amounts of data and
finding i.e. getting the right information in such a large amount of
data is like searching for a needle in a haystack, and the greater the
amount of data it is more difficult to find the true value of the data.
[4].
Many observers, including [5], [6], [7] and [8] argue that the
potential of using big data to improve the personal life or to help
companies to compete is unlimited. According to [6], "better access
to information and technology for the management and analysis of data is
changing the world." In a way, big data will lead to better health,
better teachers, improved education, and better decision-making. Gartner
hype curve predicts that the big data for two to five years [9] will do
much to transform and influence the business.
The goal is not the production and use of vast amounts of data, but
the way the data is being analyzed and presented, which techniques are
being used to get valuable information and the right support and a base
for decision making, and in reality it is about big analytics not just
about big data [10] [11]. [12] argues that the management and analysis
of these data is precisely the most difficult part in the field of big
data. Availability and timeliness of the results of quality analysis in
the moments when you have to make decisions is a major challenge. Such
phenomena of large amounts of data, and the need for high-quality
analysis also require new expertise and experts in many organizations
[13].
After describing the perceived problems caused by the appearance of
massive amounts of data from various sources, we can see the importance
of choosing and using various software tools, technologies and
techniques and the efficient management of all data concerning the
business seems like a prerequisite for the survival of the various
activities of the company.
In consideration of the successful management of a company
interaction of large- scale integrated enterprise systems such as ERP,
CRM, and SCM with the DSS and BIS is also important, where analytics of
business intelligence based on integrated data of the entire company can
directly affect a more quality, timely and flexible decision support,
and decisions will again directly affect the effectiveness of CRM and
SCM. [14].
Setting an ERP for the origin of this systematic review is also
reflected in the main target for the implementation of an ERP--access to
data in a controlled manner at the level of the entire company and
information sharing across business processes [15]. [16] suggest that a
suitable choice of an ERP system provides results such as increased
productivity, punctual delivery, decreased implementation time, and
reduced price of a product while a bad choice of an ERP system leads to
project failure or performance degradation of a company.
Furthermore, a review links together concepts of BI, DSS--Decision
Support Systems, SCM--Supply Chain Management, CRM--Customer
Relationship Management, BPM--Business Performance Management with the
involvement of the big data sphere. Also shown is the dynamic of
representation of concepts from the paper through publications of 3
global scientific-technical databases from 1991-2016 in phases of 5
years. To view the future development of new trends and technologies
related to retrieving, storing and analyzing information, generated was
a graphical representation obtained by meta analysis of research [17].
Objective of the paper is to review and synthesize the
inter-related areas that need to be considered in today's business.
Purpose of the work is to indicate the complexity of the successful
implementation of maintenance and future development of ERP and other
business systems through a systematic review of interrelationships of
large systems, new technologies and trends.
2. Overview of connection between the ERP and business-critical
systems
This section will connect the following terms and concepts: ERP
(Enterprise Resource Planning), DSS (Decision support system), BI
(Business Intelligence), CRM (Customer Relationship Managemenet),
SCM-(Supply Chain Managemenet), BPM (Business performance Management),
DW (Data Warehouse, Data Warehousing), KM (Knowledge management) and MEC
(Multi Enterprise Collaboration). The objectives of this chapter are:
* To stress the importance, function, purpose and potential
benefits of ERP systems,
* To point out the known problems and bad practices observed in the
planning and/or after the implementation of ERP in the company,
* To include the actuality of today's business conditions,
which requires inter- organizational collaboration--MEC
(Multi-Enterprise Collaboration) in decision-making and technological
implications and possible solutions to such business.
* To show links of the ERP systems with other large systems, the
importance of connection between these systems and the consequent
implications of these connections on the criteria in the selection of
software modules and ways of implementation
* List some guidance on the selection of software packages related
to big business systems and the type of BI analytics
2.1. ERP
ERP software automates and integrates business processes and
enables the sharing of information and data in a variety of business
functions. ERP software enhances the functionality and efficiency of
business processes that take place in the departments of finance, human
resources, operations and logistics, and sales and markets [18]. Since
it affects the entire business, or at least a large part of the
business, and its purchase and introduction is a large organizational
cost, selection of an ERP system is a particularly complex and delicate
task. It is estimated that 40-70% of ERP implementations have
experienced some type of failure [19]. Determination of the potential
benefits of ERP implementation is a challenging task, because most of
the advantages do not come from the ERP system, but from different ways
in which the system can be implemented and used. Although this is true
for any type of information system (IS), it is a special burning
question for the ERP systems, because of their decisive influence on
almost all aspects of the organization. This is even more important in
the context of ERP implementation through multiorganization, a subject
of work by Eckartz et al. [20].
Companies that decide to introduce the ERP systems are often
inspired by imitation of successful practices in similar companies. Such
a conviction management board can lead to so-called homogenization of
companies which weakens the differentiation, i.e. the uniqueness or
specificity by which a company and its products are recognized and stand
out in the market. The loss of differentiation and uniqueness can weaken
the company's potential and performance that delivers business
value. [21] emphasizes the importance of detailed and elaborate
evaluation protocols and practices to choose the solution that best
suits the company instead of imitating the competitors. This argues the
need to inform about all the influential factors of the optimal
selection of ERP solutions and overall quality management of performance
and operations of the company.
Complexity of approach in the selection of an ERP and the number of
factors that affect the success of the implementation was discussed
gradually in the further part of the review where the function, purpose,
benefits and success of ERP are viewed in relation to the relevant terms
of the wider area of managing the performance of a company.
2.2. ERP and decision making support
Davenport [22] suggests that the main reason and the greatest
potential of ERP is making quality and timely decisions, and Palaniswamy
and Frank [23] concluded that the ERP is a prerequisite for decision
making support. Part of the research on the effects of ERP to support
the decision making proved that ERP systems offer significant benefits
in the area of decision making support [24].
Although ERP systems integrate knowledge and provide reporting
tools for users to analyze data, decision making support is not their
primary purpose. This is supported by a multitude of BI software
solutions which the company uses to implement a decision support system,
which can not be fully developed only with an ERP solution. Therefore,
it can be said that the quality of implementation of ERP systems is
closely linked to the quality of the decision making system [25].
2.3. ERP and Business Intelligence
Business intelligence is a huge opportunity for any company to
collect valuable insights from all the data covered by their ERP and
those of other systems. Li [26], referring to the need for the ERP
systems to link internal and external data, identifies the need to
create an effective business intelligence as the primary objective of an
ERP system.
BI software enables companies' quality and quick
decision-making because of the availability of data and information in
an easily customizable form. The standard functionality of the BI
software allows DSS to use the data from the data warehouse, shaped to
measure. This warehouse provides management with BI analytical tools,
the ability to create ad hoc reports, graphics, tables, use graphical
dashboards that offer information through financial statements,
scorecards and KPIs (key performance indicators).
BI software is also available as a stand-alone package and as
modules in ERP solutions. In recent years, ERP vendors have included BI
products and sell them in their systems and have thus made way for
potentially highly effective solutions that put on the business-user the
new burdens of selection, costs of consultants, education etc. In
addition to the BI module, many ERP vendors include the very ERP
functionality of managing client interactions and promotional campaigns,
but such applications are sold as separate software, such as SAP CRM and
Oracle's Siebel cRM applications that have additional management
capabilities with client activities.
2.4. ERP and multi-organizational collaboration
Tools for decision making support and application should be open
and flexible enough, not only to provide support within their own
organization, but also across organizational boundaries. It stresses the
need for research of multiorganizational decision-making and the
development of IT which will support and optimize the production [27]
and in other areas of business. These includes heterogeneity in terms of
the breadth and depth of data, complexity in terms of models, algorithms
for solution and data visualization / process, distribution to the scope
and range, versatility of domains and paradigms, flexibility, ability to
reuse and extensibility [28].
More recently, vendors, including Microsoft, SAP and Oracle have
introduced tools and systems that support decision-making aspect of the
MEC (MEC, multi-enterprise collaborative). The shift of the market and
scientific research towards this field of research is still in its early
stages and new perspectives and insights are constantly being acquired
as new products are being released.
2.5. ERP, data warehouse and knowledge management
[29] point out that the knowledge of the employee is the most
important resource of the company, and point out that although the
repositories of knowledge in companies often exist, they are not
organized in a way that they could be effectively used. The authors [30]
observed the importance of integrating the processes of knowledge
management (KM) within the Decision Support System so that the
decision-makers can combine different types of knowledge (explicit and
implicit) and data (internal and external) available in various forms.
Improved creation and sharing of knowledge should increase the
flexibility and innovation [31]. Literature dealing with ERP success
suggests careful knowledge management in order to maximize its
potential. Results of the Ifinedo research (Ifinedo, 2006) [32] have
shown that knowledge management, including the creation of knowledge, is
a strong predictor of ERP success. The more implicit and explicit
knowledge the organization has, the more likely that the ERP will be
successful [33].
Knowledge exists in the heads of corporate staff and management.
Knowledge also exists in the environment outside of the company. The
needs of business are also to formalize, store, access and use the
entire knowledge of the company.
Data warehousing is not just a problem-solving process, but also a
concrete architecture. Different companies have different data
warehouses. According to the features of their ERP systems, many
companies have designed the architecture of their data warehouses. Data
warehouse is responsible for providing the information needed to support
decision-making at different levels [34].
Existing data warehouse of a company can be extended to create a
knowledge warehouse (KW) [35]. KW will not only facilitate the retrieval
and creation of knowledge, but will also improve the retrieval and
exchange of knowledge through the organization, providing at the
decision-maker's disposal an intelligent platform for analysis.
Knowledge warehouses will be continuously upgraded, with the aim of
structuring knowledge from previous situations of decision-making and
optimize future decisions. Shafiei et al., also mention the concept of a
knowledge warehouse (KW) which is "above" the analysis of data
from the ERP system via a data warehouse. [28]. [36] is also in the same
vein, that with the advancement of technology expects that the existing
business system will provide current intelligence information and not
just the numbers, which can be used for making business decisions and
strategies. This trend and the need are applicable today through various
methods of artificial intelligence, especially in areas of text
processing and natural language.
2.6. ERP and CRM
With the goal of providing "a single face to the
customer," the basic principle behind CRM is that every employee in
contact with the client must have access to information on the latest
customer interactions with the company [37]. While CRM tools within an
ERP, such as the SAP ERP system, when used properly, can help in the
management of customer relationships, companies that opt for the concept
of CRM often use a separate CRM system that communicates with the ERP
system. The advantage of this approach is that the planning and analysis
conducted in the CRM system does not interfere with the performance of
the ERP system, which primarily handles large volumes of business
transactions.
When compared with ERP, for CRM can be said that it, just like the
ERP, offers ways to automate the process and conduct business more
efficiently. However, these two systems are designed to streamline the
various functions. While CRM is used to manage contacts, accounts,
opportunities, activities, marketing, etc., ERP is designed to manage
the operations and business functions, such as product planning,
purchasing, inventory, customer service, order tracking and other
back-end business processes. However, after the ERP vendors included the
CRM features in their software, and CRM vendors included ERP
capabilities into their tenders, the differences between them began to
disappear.
2.7. ERP and SCM
The study [38] has empirically proven theoretical assumptions in
the existing literature on the impact of ERP benefits on SCM
competencies. A typical configuration of business software in the
manufacturing enterprise includes at least three large systems ERP, SCM
and CRM. All are constructed of one or more database management systems
(DBMS)--which usually use the same logical integrated database. ERP, SCM
and CRM systems are usually standard software that can adapt to the
requirements of individual organizations. Today, these three types of
systems tend to be integrated: SCM module, for example, will have access
to information available in the ERP system directly or through a common
database [39].
Supply Chain Management (SCM) is directed across boundaries, taking
into account that companies are increasingly concentrating on their core
competencies, leaving the other activities to the partners who have more
knowledge. With the growing dependence on partners, effective supply
chain has become equally important to the success of the company as the
efficiency of internal business processes. Information systems that
support supply chain management (SCM systems) are developed either by
ERP vendors or software companies specializing in logistics. They have
either expanded their ERP systems with additional SCM functions or
developed new SCM systems that collaborate with their ERP systems.
Software companies are developing dedicated SCM systems and
providing interfaces to standard ERP systems. The reason for this is
that the SCM without ERP is hardly possible. An interesting trend is
that some specialized SCM vendors were bought by large ERP vendors. In
this way, ERP vendors are now able to offer supply chain management as
part of their business portfolio. A typical company today uses a large
number of information systems. These systems tend to be integrated, so
that they can work together [39].
2.8. ERP and BPM
Over time, it turned out that, despite the fact that ERP systems
hold most of the organization's data, they can provide insight into
valuable information necessary for strategic decision making. ERP
vendors are aware of this and offer BI functionality with their software
to help "liberate" the data from these complex systems. At its
core, BI is designed to consolidate data that extends across different
business areas and systems, and help managers to make better decisions
faster. In the new paradigm, the ERP system acts as an important source
of data, but not necessarily the only source. This opens the door to
choice to combine ERP with BI and/or Performance Management Suites and
tools that meet the needs of the organization. This may or may not be
from the same vendor.
In order to differentiate concise terms BI and BPM it is stated
that BI discloses a technology used to access, analyze and inform about
relevant business information. It covers a wide range of software
solutions, including ad hoc queries, OLAP, dashboards, scorecards, today
usually as modules for various BI suite. BPM is characterized as BI +
strategic planning, which means that the convergence of BPM and BI
planning is on a unified platform, cycle of planmonitoring-analysis.
Processes that include BPM are not new: they exist in every medium to a
larger organization, and BPM provides a framework for the integration of
processes, methodologies and metrics of other systems into a single
solution. Term BPM refers to the processes, methodologies, metrics and
technologies used by the company to scale, monitor and manage business
performance. BPM is a continuous set of processes which, if properly
carried out affects the entire organization from top to bottom. For BPM
it is important to synchronize the entire company: it helps users to
achieve goals that help the execution of the strategy and the adoption
of value for all the stakeholders [40].
3. Emergence of new technologies and trends--big data and embedded
analytics
The reasons for considering the development and potentials of ERP
systems and the entire BPM system and big data are reflected in the new
capabilities-functionalities of a broader business software system and
exploiting new trends and technologies, as outlined below. A large
amount of big data is unstructured, such as audio, video, text, and
developed techniques of storage, processing and analysis of data types
have evolved, there is a possibility that ERP is stored in and managed
by a new system, the "knowledge hub" where the knowledge from
inside and surrounding environment of the company will be stored and
formalized integrally [41].
Taking into account the big data sphere allows analysis of data and
transactions that are created outside the company. In this way, all
stakeholders, customers, partners, suppliers are connected and the
development of applications which will integrate data, models from
different business perspectives is expected. Therefore, big data
provides the ERP system with additional features and characteristics in
relation to other systems, such as cRM and SCM, and thus adds value to
the business.
Using data from big data sphere will also initiate some issues of
privacy and data security and protection, and the movement of data will
need to be constantly monitored and take into account the latest
regulations. Also, the quality of the data in the ERP system can be
disrupted after the integration of big data, so it is necessary to set
some filters when downloading raw data.
These considerations relating to the question of the position of
the ERP system and the expansion of the boundaries of the data relevant
for the company, with the development of new technologies certainly
point to new opportunities for additional functionality of ERP systems
and consequently the entire BPM, whose potential can be achieved if new
issues which may occur by using big data types are also considered.
Problems and changes are mostly related to the processing, storage,
analysis and quality of big data. Also, the integration of ERP systems
and big data will require some changes in the activities of the stages
in the life cycle of ERP systems as a change of requirements to be met
by the ERP [41].
As the pace of business accelerates steadily, the company realizes
that it is not enough just to analyze the data but the activities that
are being imposed on the basis of the results of the data analysis must
also be operationalized as soon as possible. This means that the aim is
to exploit and share not only data, but also the results of the
analysis, and exchange them through the business processes and
applications, in order to shorten the time of decision making.
Operationalizing and embedding analytics consists of gaining
insight on the necessary actions within the business processes that can
be automated or provide support for decision-making. Analytics of
different functionality and complexity are usually built into the
dashboards, applications, devices, systems, databases and the like.
Operational analytics are in the background of logistics applications,
some CRM modules, authorizations and detection of faults, the system
recommended content and products for each customer and the like.
Embedded analytics, while not entirely a new technology today is
especially being taken into account due to the occurrence of big data,
and the possibility of operationalization of decisions through programs
is very effectively. Also, sharing analytics through a variety of
applications allows them to be received and potentially exploited by a
large number of stakeholders, which is certainly an added value [17].
4. Research Methodology
In order to show the development of individual concepts through
four five-year periods chosen were two science databases (Web of
Science, Core Collection, Science Direct), and the database Google
Scholar, which shows a large number of scientific and professional
publications regardless of the quality to compare dynamics of incidence
and popularity. The last columns for each database show the Tendency,
which results in a number 1 and the up arrow if constant growth was
recorded, and otherwise 0 and arrow down. Attached you can find Table 3
obtained by meta analysis of research (for research details see under
[17] which shows the current representation of the use of data types,
analytical platforms and types of analytics and prediction for the next
three years based on the responses of 300 respondents). The table was
sorted by three categories from top to bottom by the intensity of
shades: the first category represents concepts that are anticipated to
decline in use over the next three years, and the remaining two
categories indicate concepts that will be moderately more and much more
used in the next designated period.
5. Research results and discussion
Table 1 is the representation of individual frequencies. In the
first two databases the decline is shown only by the concept of Business
Process Reengineering, while in the Google Scholar database more than
one category shows a decline in participation in publications. Each
topic was searched with quotation marks.
Table 2 shows the representation of publications that at the same
time mention all the concepts related to the operator and a base WoS
Core Collection is this time omitted due to the low number of
publications for the first three five-year periods. Table 2 shows that
in the Science Direct and Google Scholar databases the representation of
the publications in which the selected concepts appear together is
constantly growing to this day. This also shows the connection between
ERP and other large systems with selected concepts, but in the last five
years there has also been a noticeable expansion of considerations
related to big data and embedded analytics.
The following text includes Table 3 obtained by meta analysis of
research (for details see research in Halper, 2015) [17], sorted by
categories from the top down: the first category represents concepts
that are anticipated to decline in use over the next three years, and
the remaining two categories indicate concepts that will be moderately
more and much more used in the next designated period. This view of
emerging trends and technologies is clearly essential for reflection and
selection of a combination of business software suite, BI modules and
types of analytics, platforms and ways of implementation in order to
exploit the potential of each and the total potential of all of them in
a given company.
6. Conclusion
This paper presents an overview of the field with the aim of
systematically linking important areas, approaches and philosophies
whose understanding is necessary for successful implementation of ERP
and/or SCM and/or CRM system and the establishment of effective systems
for decision making support. During operation described are some issues
related to the non-exploitation of potentials and non-integration of
large systems, sub-optimal use of analytical tools, lack of new
technological solutions, lack of understanding the phenomena big data
and the like. The paper therefore proposes a set of guidelines and tips
that to bypassed or eliminate these problems.
Chapter which systematically presents the concept of the affected
area from selected literature has been upgraded in content around the
central concept of the ERP systems. Gradually upgrading with other terms
and concepts is done through their description and interpretation of the
function and connecting them in relation to the ERP system. Each concept
has been given a position, purpose and relationship with other areas in
a continuous system of managing business performance. In doing so some
guidance in terms of using new technology storage, retrieval and
analysis of data has been given.
Also presented was the frequency of appearance which may also be
used to monitor the development and character of processed concepts, and
thus demonstrating the need to also consider their mutual relations and
not just the concepts individually. Meta analysis has produced graphs
that describe the anticipated development of certain technologies, types
of data, analytical tools, and highlights those that are anticipated to
decline in popularity and the ones that will be more and more used in
the next few years. This concise view of future trends is also important
in the assessment criteria for the selection of software, tools and
types of implementation. This paper is intended to gain insight on the
complexity of the field of business management and exploitation of the
full potential of software packages that are offered today in the market
in order to find the best combination for a given company. The further
course of research would go into setting up a comprehensive framework
which sets the basic structure and configuration of the business suites
and after that to create a support system in the selection of such a
configuration.
DOI: 10.2507/27th.daaam.proceedings.047
7. Acknowledgments
The research has been conducted under the project "Extending
the information system development methodology with artificial
intelligence methods" (reference number 13.13.1.2.01.) supported by
the University of Rijeka (Croatia).
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This Publication has to be referred as: Suman, S[abrina] &
Pogarcic, I[van] (2016). Development of ERP and Other Large Business
Systems in the Context of New Trends and Technologies, Proceedings of
the 27th DAAAM International Symposium, pp.0319-0327, B. Katalinic
(Ed.), Published by DAAAM International, ISBN 978-3-90273408-2, ISSN
1726-9679, Vienna, Austria
Table 1. The frequency of appearance of selected topics
from 1991-2016
Web of Science Core Collection--
Search by Topic
Topic 1991-1995 1996-2000 2001-2005
Decision Support 1980 2857 3823
Enterprise Resource 2 54 264
Planning
Business Performance 0 0 2
Management
Business Process 6 31 91
Management
Business Process 73 159 90
Reengineering
Supply Chain Management 22 243 1028
Customer Relationship 0 19 224
Management
Business Intelligence 6 26 104
Data Warehouse Or 7 247 503
Data Warehousing
Data Mining 27 944 3924
Text Mining 0 33 330
Web Mining 0 14 155
Business Analytics 0 0 3
Knowledge Management 42 447 1783
Knowledge Discovery 48 441 880
Big Data 0 0 0
Web of Science Core Collection--
Search by Topic
Topic 2006- 2011- Tendency
2010 present
Decision Support 6505 11303 [up arrow] 1
Enterprise Resource 350 380 [up arrow] 1
Planning
Business Performance 6 9 [up arrow] 1
Management
Business Process 172 309 [up arrow] 1
Management
Business Process 60 54 [down arrow] 0
Reengineering
Supply Chain Management 2337 3592 [up arrow] 1
Customer Relationship 381 520 [up arrow] 1
Management
Business Intelligence 184 462 [up arrow] 1
Data Warehouse Or 524 682 [up arrow] 1
Data Warehousing
Data Mining 5751 8962 [up arrow] 1
Text Mining 777 1687 [up arrow] 1
Web Mining 173 185 [up arrow] 1
Business Analytics 13 99 [up arrow] 1
Knowledge Management 2470 3111 [up arrow] 1
Knowledge Discovery 897 1183 [up arrow] 1
Big Data 26 3946 [up arrow] 1
Science Direct- Search by
Title, abstract, keywords
Topic 1991-1995 1996-2000 2001-2005
Decision Support 1022 1137 1456
Enterprise Resource 0 11 122
Planning
Business Performance 0 0 1
Management
Business Process 0 7 32
Management
Business Process 25 104 57
Reengineering
Supply Chain Management 4 78 446
Customer Relationship 0 7 104
Management
Business Intelligence 2 5 52
Data Warehouse Or 32 481 1565
Data Warehousing
Data Mining 10 190 962
Text Mining 0 2 66
Web Mining 0 1 29
Business Analytics 0 0 1
Knowledge Management 28 149 541
Knowledge Discovery 6 108 208
Big Data 0 0 0
Science Direct- Search by
Title, abstract, keywords
Topic 2006- 2011- Tendency
2010 present
Decision Support 2471 4545 [up arrow] 1
Enterprise Resource 137 217 [up arrow] 1
Planning
Business Performance 0 7 [up arrow] 1
Management
Business Process 65 186 [up arrow] 1
Management
Business Process 29 44 [down arrow] 0
Reengineering
Supply Chain Management 876 1458 [up arrow] 1
Customer Relationship 137 223 [up arrow] 1
Management
Business Intelligence 68 338 [up arrow] 1
Data Warehouse Or 2176 3963 [up arrow] 1
Data Warehousing
Data Mining 1889 3772 [up arrow] 1
Text Mining 233 644 [up arrow] 1
Web Mining 64 82 [up arrow] 1
Business Analytics 5 50 [up arrow] 1
Knowledge Management 648 1107 [up arrow] 1
Knowledge Discovery 310 454 [up arrow] 1
Big Data 4 1689 [up arrow] 1
Google Scholar- Search all
excluded citations and patents
Topic 1991-1995 1996-2000 2001-2005
Decision Support 18600 33200 92000
Enterprise Resource 132 3160 14900
Planning
Business Performance 30 109 860
Management
Business Process 266 1270 7310
Management
Business Process 1130 6350 9840
Reengineering
Supply Chain Management 1480 9340 26400
Customer Relationship 468 2840 16400
Management
Business Intelligence 614 2150 12700
Data Warehouse Or 47 2460 5920
Data Warehousing
Data Mining 4010 19300 169000
Text Mining 63 827 8670
Web Mining 16 461 5850
Business Analytics 11 32 423
Knowledge Management 3820 16000 127000
Knowledge Discovery 1430 10400 25800
Big Data 0 0 721
Google Scholar- Search all
excluded citations and patents
Topic 2006- 2011- Tendency
2010 present
Decision Support 224000 154000 [down arrow] 0
Enterprise Resource 20400 17600 [down arrow] 0
Planning
Business Performance 2410 3590 [up arrow] 1
Management
Business Process 17000 17700 [up arrow] 1
Management
Business Process 13100 14100 [up arrow] 1
Reengineering
Supply Chain Management 57900 57500 [down arrow] 0
Customer Relationship 20100 26200 [up arrow] 1
Management
Business Intelligence 16600 23400 [up arrow] 1
Data Warehouse Or 9250 10400 [up arrow] 1
Data Warehousing
Data Mining 463000 238000 [down arrow] 0
Text Mining 19300 20900 [up arrow] 1
Web Mining 12300 16300 [up arrow] 1
Business Analytics 1590 10400 [up arrow] 1
Knowledge Management 205000 102000 [down arrow] 0
Knowledge Discovery 50900 53600 [up arrow] 1
Big Data 2060 32200 [up arrow] 1
Table 2. The frequency of simultaneous appearances of combination
of topics from 1991-2016
Science Direct-Search by
Title, abstract, keywords
Combination of topics 1991-1995 1996-2000 2001-2005
"ERP" and "Decision 0 4 37
support"
"ERP" and "Business -- -- --
performance management"
"ERP" and "Business 0 0 0
process management"
"ERP" and "Business 0 0 8
process reengineering"
"ERP" and "Supply chain 0 3 45
management"
"ERP" and "Customer 0 4 66
relationship management"
"ERP" and "Business 0 1 33
intelligence"
"ERP" and "Business 0 0 16
intelligence" and
"Decision support" and
"Supply chain management"
and "Customer
relationship management"
"ERP" and "Business 0 0 0
performance management"
and "Business
intelligence" and
"Decision support" and
"Supply chain management"
and "Customer
relationship management"
"ERP" and "business -- -- --
process reengineering"
and "business
intelligence" and
"decision support" and
"supply chain management"
and "customer
relationship management"
"ERP" and "big data" -- -- --
"ERP" + "embedded 0 0 0
analytics"
Science Direct-Search by
Title, abstract, keywords
Combination of topics 2006- 2011- Tendency
2010 present
"ERP" and "Decision 67 89 [up arrow] 1
support"
"ERP" and "Business -- -- --
performance management"
"ERP" and "Business 0 2 [up arrow] 1
process management"
"ERP" and "Business 9 52 [up arrow] 1
process reengineering"
"ERP" and "Supply chain 49 63 [up arrow] 1
management"
"ERP" and "Customer 71 106 [up arrow] 1
relationship management"
"ERP" and "Business 39 68 [up arrow] 1
intelligence"
"ERP" and "Business 21 52 [up arrow] 1
intelligence" and
"Decision support" and
"Supply chain management"
and "Customer
relationship management"
"ERP" and "Business 1 [up arrow] 1
performance management"
and "Business
intelligence" and
"Decision support" and
"Supply chain management"
and "Customer
relationship management"
"ERP" and "business -- -- --
process reengineering"
and "business
intelligence" and
"decision support" and
"supply chain management"
and "customer
relationship management"
"ERP" and "big data" -- -- --
"ERP" + "embedded 13 [up arrow] 1
analytics"
Google Scholar- Search all excluded
citations and patents
Combination of topics 1991-1995 1996-2000 2001-2005
"ERP" and "Decision 124 999 5690
support"
"ERP" and "Business 2 9 171
performance management"
"ERP" and "Business 24 189 2330
process management"
"ERP" and "Business 14 544 2710
process reengineering"
"ERP" and "Supply chain 95 1450 9780
management"
"ERP" and "Customer 33 590 6130
relationship management"
"ERP" and "Business 22 310 3220
intelligence"
"ERP" and "Business 1 18 328
intelligence" and
"Decision support" and
"Supply chain management"
and "Customer
relationship management"
"ERP" and "Business 0 0 10
performance management"
and "Business
intelligence" and
"Decision support" and
"Supply chain management"
and "Customer
relationship management"
"ERP" and "business 0 7 70
process reengineering"
and "business
intelligence" and
"decision support" and
"supply chain management"
and "customer
relationship management"
"ERP" and "big data" 0 70 129
"ERP" + "embedded 0 0 6
analytics"
Google Scholar- Search all
excluded citations and patents
Combination of topics 2006- 2011- Tendency
2010 present
"ERP" and "Decision 10400 15300 [up arrow] 1
support"
"ERP" and "Business 639 806 [up arrow] 1
performance management"
"ERP" and "Business 6750 10200 [up arrow] 1
process management"
"ERP" and "Business 3870 4170 [up arrow] 1
process reengineering"
"ERP" and "Supply chain 15200 16800 [up arrow] 1
management"
"ERP" and "Customer 10000 13200 [up arrow] 1
relationship management"
"ERP" and "Business 6400 11300 [up arrow] 1
intelligence"
"ERP" and "Business 585 965 [up arrow] 1
intelligence" and
"Decision support" and
"Supply chain management"
and "Customer
relationship management"
"ERP" and "Business 65 86 [up arrow] 1
performance management"
and "Business
intelligence" and
"Decision support" and
"Supply chain management"
and "Customer
relationship management"
"ERP" and "business 110 140 [up arrow] 1
process reengineering"
and "business
intelligence" and
"decision support" and
"supply chain management"
and "customer
relationship management"
"ERP" and "big data" 226 5180 [up arrow] 1
"ERP" + "embedded 34 50 [up arrow] 1
analytics"
Table 3. Data Types, Platforms and Analytics Used for
Operationalizing Analytics
Data Types Used for Operationalizing Statistics Will use
Analytics of today's for 3
use years from
now
Structured data(tales, records) 90% 8%
App logs 24% 36%
Real time messages 30% 46%
Web logs and clickstreams 21% 40%
Machine generated data 20% 39%
Data from graph databases 15% 32%
Social media data 22% 47%
Streaming, real time continous data 16% 46%
Unstructered data(audio video, text) 15% 48%
Analytics Used for Operationalizing Statistics Will use
and Embedding of today's for 3
use years from
now
Reporting 89% 9%
visualisation 69% 26%
Optimisation 28% 51%
Predictive analytics 28% 59%
Geospatial analytics 17% 37%
What-if simulations 22% 57%
Social media analytics 17% 46%
Prescriptive analytics 18% 51%
Text analytics 15% 45%
Platforms Used for Operationalized Statistics Will use
Analytics of today's within 3
use years
Data warehouse 74% 19%
Other databases or data marts 73% 14%
BI and analytics platforms 73% 24%
Transaction systems 59% 16%
Operational data stores 53% 23%
In-memory analytics platforms 31% 38%
Open source platforms 22% 25%
Data appliances 19% 32%
Public cloud platforms 19% 39%
Native mobile platforms 15% 29%
Hadoop 15% 39%
In-memory grids 13% 27%
Event stream processing engines/ 12% 29%
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