=Paper=
{{Paper
|id=Vol-3149/paper9
|storemode=property
|title=Technology in the Industrial Revolutions and its Impact on the Key Performance Indicators of Organizations
|pdfUrl=https://ceur-ws.org/Vol-3149/paper9.pdf
|volume=Vol-3149
|authors=Oleg Zaritskyi,Oleksandr Ponomarenko
|dblpUrl=https://dblp.org/rec/conf/ttsiit/ZaritskyiP22
}}
==Technology in the Industrial Revolutions and its Impact on the Key Performance Indicators of Organizations==
https://ceur-ws.org/Vol-3149/paper9.pdf
Technology in the Industrial Revolutions and Its Impact on the Key
Performance Indicators of Organizations
Oleg Zaritskyia and Oleksandr Ponomarenkob
a
National Aviation University, Liubomyra Huzara Ave, 1, Kyiv, 03058, Ukraine
b
Professional College of Engineering and Management of the National Aviation University, Metrobudivska str., 5a,
03065, Kyiv, Ukraine
Abstract
Context. The article considers topical issues of interconnection of life cycles of business, production,
product models of enterprises and key indicators of their activities through the prism of the evolution
of information systems, their impact on the pace of development of enterprises and the role that infor-
mation systems play in industrial revolutions. Objective. The overall objective of the study is to assess
the impact of information systems on the development of enterprises in the process of industrial rev-
olutions by assessing the key performance indicators covering all perspectives (functional areas of
enterprise development) of the balanced scorecard as one of the progressive systems of strategic man-
agement of organizations. Method. Formalization of key performance indicators through the mathe-
matical apparatus was carried out using the methodology of strategic management of the enterprise -
the balanced scorecard. Results. The study formalized the types of basic IS and software, their impact
on the development processes of enterprises during the industrial revolutions by assessing certain key
performance indicators in terms of the balanced scorecard methodology, which was first described
using a mathematical apparatus. Conclusions. The main directions of transformation of modern pro-
duction are defined by three global technological trends: network integration, intellectualization and
flexible automation. The development and widespread implementation of IP has doubled the pace of
technological revolutions and, in fact, determines all further enterprise development strategies within
these technological trends. The next (fifth) industrial revolution will be defined exclusively by the
development of data science and the transition to new system architectures of computing systems or
their combinations, for example using neurosynaptic and quantum computers, which will allow using
all the possibilities of industrial Internet of Things and Digital twin’s concepts in almost real time.
Keywords1
PLM, CALS, CAD, ERP, KPI, industrial revolutions, industrial Internet of things, balanced score-
card.
1. Introduction
Industrial technology has come a long way from the first attempts to mechanize production using mechan-
ical devices and steam engines during the first industrial revolution in the XVII century to creating a single
information environment using the industrial Internet of Things in the XXI century. (Fig.1).
All the efforts of the engineers of the first industrial revolution were aimed at developing machines and
devices that helped workers to mechanize standard operations in order to increase productivity.
The second industrial revolution was marked by the use of electricity, the development of the assembly line
and the transition to mass production. At that time the first schools of management were beginning to emerge,
and F. Taylor published his seminal work "Principles of Scientific Management" (1911).
The basis of the third industrial revolution was the rapid development of IS and computers, which allowed
the transition from mechanization to automation of production with the use of special software for product
design and production resource management. The era of computer systems began, which quickly took a leading
Emerging Technology Trends on the Smart Industry and the Internet of Things, January 19, 2022, Kyiv, Ukraine
EMAIL: oleh.zaritskyi@npp.nau.edu.ua (O. Zaritskyi); ponomarenkos_200@ukr.net (A. Ponomarenko)
ORCID: 0000-0002-6116-4426 (O. Zaritskyi); 0000-0002-6538-0468 (A. Ponomarenko)
©️ 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
87
position in the automation of enterprise management processes. At the same time, there was an intensive de-
velopment of various schools of management and corresponding concepts of management of organizations,
organizational management, which studies the fundamental principles of the functioning of organizations, be-
came popular. The first concepts of enterprise management were emerged.
The fourth industrial revolution introduced the concepts of "smart factory", "smart production", "factory of
the future". Currently, these terms are used interchangeably, although "factory of the future" is a broader con-
cept that includes "smart manufacturing" but using a single digital space and virtualization of workplaces.
The U.S. National Institute of Standards and Technology (NIST) defines Smart Manufacturing as fully
integrated corporate manufacturing systems capable of responding in real time to changes in production con-
ditions, the requirements of logistics networks and to meet consumer needs [1]. Thus, the transition to digital
production at all its stages in a single information environment (the ideology of previous years CALS) with
full integration with suppliers and consumers to implement rapid real-time feedback in response to any changes
in the internal and external environment is assumed.
The intensive development of IS and software, which started during the third industrial revolution, has also
passed its evolutionary way from two-dimensional design systems (CAD(2D)) to three-dimensional
(CAD(3D)) and product data management systems (PDM, PLM) in terms of development and design manage-
ment. Another development is enterprise resource management (ERM), where the starting point was the de-
velopment of MRP-class systems in the late 1960s, which ended in the 2000s with the realization of full inte-
gration of organizations in a unified information environment based on ERP, CRM and SCM-class systems.
Further development of IS was aimed at combining the design and development environment (PDM, PLM)
with enterprise resource planning (ERP) environments using intermediate integration IS, for example in the
construction field of BIM-class systems [2].
Capabilities of PLM and ERP class systems in terms of data operation, digital product models during their
life cycle, access to customer supply chains in one environment, allowed to approach the implementation of
the BSC concept developed in the 1990s, as one of the most effective systems of strategic management.
Thus, the basis of the industrial revolutions was the development of science and technology as a result of
the discovery of fundamental physical laws in the late XVII and XVIII centuries and the development of in-
formation technology in the IX - XX centuries. The concepts of enterprise management have also undergone
an evolutionary path of development, taking into account the state of technology and data availability, and
since the XXI century, usually involve a combination of production and business strategies.
The object of research is the concept of industrial revolutions, which occur due to the development of
various branches of natural sciences, data science and their practical implementation through information tech-
nology, transforming technological processes from a purely technical plane to an intellectual, virtual plane,
combining real objects with their digital twins.
The subject of research is information technology and key performance indicators of the enterprise within
the evolution of production models during the industrial revolutions.
The aim of research is to formalize the types of IP, their relationship and impact on the KPI of enterprises
in the transition from mechanization to virtualization of production.
2. Problem Statement
Modern systems of strategic management of enterprises are presented in the studies of many schools of
management and are in constant improvement and development, specifying the key aspects and metrics by
which it is possible both to plan activities and to monitor the implementation of strategies. The most thorough,
in the opinion of the authors of this article, is the BSC concept developed by Harvard University professors R.
Kaplan and D. Norton [3].
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Figure 1: Evolution of technological revolutions and information system
The basis of BSC is the measurement and assessment of its effectiveness by a set of optimally selected
indicators (KPI), characterizing all aspects of the company's activities, in terms of four perspectives, by their
decomposition to the level of operational control (Fig.2).
Figure 2: BSC concept
Accordingly, the model for the i-th scenario with k possible formalized strategies Si can be written in gen-
eral (1 – 4):
Si f PNi , i 1...k , Ni Fi , Oi , Di , Ci (1)
f : GNmi PN i Fi ,Oi , Di ,C1 , m 1 5 (2)
m
f : I ,R , A
Ni
m
Ni
m
Ni G m
Ni , (3)
lim I m
Ni G m
Ni (4)
R Nmi rmax , ANmi t min
The content of the formulas (1 - 4) can be interpreted as follows: the timely implementation of initiatives
in minimum time allows to fulfill the key performance indicators at the target (maximum) level, which leads
to the implementation of the strategy. The main task of the implementation of the function (3) in achieving the
89
objectives is to achieve the corresponding extreme values R Nmi rmax , ANmi t min by elements of the equation due
to automation with the use of IS and virtualization of the production environment.
3. Review of the Literature
A large number of works are devoted to the topic of assessing the production efficiency and the role of
information systems in production, but it should be noted about the coverage, as a rule, a narrow, specialized
range of issues within local problems.
In [4] the results of research on the effectiveness of the lifecycle stages of the industrial products are pre-
sented. That is, only one of the three lifecycle functional areas of the enterprise is considered: business model,
production and product. Mathematical models of the management efficiency function and management index
were developed. Functional, informational and simulation modeling of production processes was carried out.
By the example of technological preparation of production, the methods of quality assessment and efficiency
management are presented. The application of the developed models, methods and composition of the new IS
is shown.
The standard [5] is designed to assess the key technical and economic indicators of manufacturing opera-
tions management. The standard contains a conceptual framework for the definition, formation, use and ex-
change of key technical and economic indicators for manufacturing operations/process management (MOM),
regardless of the specific branch of production for industrial sectors with continuous, batch and piece produc-
tion. A feature of the standard is the consideration of all levels of the functional model of production, in which
indicators are defined as strategic results, subjected to quantitative assessment of measurements, characterizing
the most important factors for the success of the enterprise, are very important for understanding and improving
the production indicators both in terms of the prospects for creating economical production in recycling, and
to achieve strategic corporate objectives. The standard does not consider the connection with the IS in achiev-
ing the goals of the enterprise.
Most fully the question of the relationship of the three components of modern production in terms of a
combination of business and production model considered in the standard [1], but also without assessing the
impact on these models by the IS dynamics.
In [6] the author presents the definition of the Third Industrial Revolution as a complex multidimensional
phenomenon. Its key features are specified: transition to renewable energy sources and raw materials, mass
implementation of additive technologies and network production systems, digital basis for fixation and transfer
of information, formation of horizontal production and consumer structures, solidary forms of economic rela-
tions. The basic groups of innovations in the energy, raw materials, technological, organizational and economic
sections are characterized.
4. Materials and Methods
4.1. Key Performance Indicators
Organizational management has passed a thorny path from the ideas of scientific organization of labor in
the early twentieth century to modern complex multistage models of organizational development and formed
for each stage the main indicators of activity. The basic models of development of the organizations which
have found wide circulation now consider development of the organization through a prism of 5–7 crises in
model L. Greiner, or 10 states of the organization from origin up to elimination on model I.Adizes [7].
Taking into account the 11 models of basic strategies [8] and 10 states (stages of development) of the
organization, its strategy can be one of the 10x11 matrix elements, in turn, each element has certain KPIs.
The article proposes to identify baseline indicators in four perspectives, which allow to assess the impact
of IS on the implementation of the strategy. To do this, Figure 3 shows the complex life cycle of the product,
production and business logic (operating activities of the enterprise), the corresponding IS and integration links
between the three models of LC with the corresponding performance indicators (Table 1).
90
Table 1
Key performance indicators
IS, which
m Measure- Key abil- affects
Indicator I Ni
PN Description of the indicator
ment unit ity SMS the indi-
cator
Production I F1i
Produc- Number of products per unit time CAM,
un. /h. Fi
productivity tivity (hours, change, day, …) ERP
Overall Calculated as the product of
I F2i
Produc- CAM,
equipment dim. -less Fi three components: availability,
tivity ERP
efficiency productivity, quality.
The amount of resources (elec-
Resource ef- I F3i
кVt/un., Fi
Produc- tricity, steam, gas, water, etc.) re- CAM,
ficiency l./un., м3/un. tivity quired to produce a unit of out- ERP
put.
Produc- Characterizes the efficiency of
Money circle I F4i day Fi ERP
tivity cash flow (formula 5)
The pace of
CAD,
new product I O1 i
Adapta- The time required to bring a new
day Oi CAE,
develop- bility product to market is estimated
CAM
ment
Response
time to The time required for the transi-
changes in I O2i
Adapta- tion to new technologies or envi-
day Oi ERP
external and bility ronmental conditions is esti-
internal con- mated
ditions
Execution of
The percentage of time during CAM,
the produc- I O3i
Adapta-
% Oi which production maintains the ERP,
tion sched- bility
production schedule. SCM
ule
Characterizes the efficiency of
Production production taking into account CAM,
I O4i
Adapta-
cycle effi- dim. -less Oi losses according to the methodol- ERP,
bility
ciency ogy of lean production (formula SCM
6)
The indicator describes the prod-
uct (goods) in terms of three
Indicator of
components: characteristics
consumer Ci , ERP,
I C1 i dim. -less Quality (functionality, quality, price, ser-
value of Di CRM
vice life), image of the manufac-
proposition
turer, staff qualifications (availa-
bility, feedback), (formula 7)
Recycling as a concept of lean
Possibility of
I O5i dim. -less Oi Stability production and environmental -
recycling
protection
The efficiency of production in
СО2 emis- I O6i
terms of environmental pollution
tona Oi Stability
sions and environmental safety is as-
sessed
91
The efficiency of logistics is con-
Efficiency of I O7i
кVt/un., sidered in terms of the use of re-
Oi Stability SCM
logistics l./un., м3/un sources for transportation per
unit of output.
The number of hours worked by
man.hour./ Produc-
Productivity I D1 i Di workers required to produce a ERP
unit tivity
unit of output.
All indicators can be divided into four major groups, which correspond to the prospects of the BSC. It
should be noted that in each perspective the number of indicators is constantly increasing, due to their im-
provement and detail, which becomes possible with the introduction of IS and access to data that may previ-
ously have been unattainable in real time.
Figure 3: Product, production and business logic life cycle
However, from a practical point of view, to simplify the study, it is possible to use basic complex indicators,
for example, for the financial perspective, it may be - the cash cycle, which characterizes the effectiveness of
cash management (5):
TMC Т MI Т IP Т CR ;
, (5)
lim TMC min
Т MI min, Т IP 0 ,Т CR TMI
The production perspective, or the perspective of internal processes, can be assessed using the manufactur-
ing cycle efficiency (6):
ТP
MCE ;
Т P Т QC Т MP Т D / S Т ER , (6)
lim MCE 1
Т QC ,Т MP ,Т D / S ,Т ER 0
The customer perspective is described by a complex indicator of the consumer value of the proposition (7):
f : FP , QP , PP , S L , SQ , AE , Т R I C1 i
, (7)
lim I C1 i max
FP , Q P ,Т R , S Q , AP , max, PP ,Т P min
It should be noted the large number of studies and standards in performance management, for example, in
manufacturing is widely used standard [5], detailing each stage of production in terms of its efficiency.
Thus, the considered key indicators allow us to assess both the effectiveness and efficiency of production
and enterprise management.
To analyze the impact of IS on performance, it is necessary to consider modern production, which is based
on CALS technologies and the concept of the Internet of Things.
92
4.2. Smart Manufacturing and IS Support
"Smart production", along with the "Industrial Internet of Things", is the basis of Industry 4.0 (Industrie
4.0) - the German government's program for the development of high technology [9]. The main key indicator
of the implementation of the program Industry 4.0 - one hundred percent transition to automated production,
in which the management of product LC is carried out in real time, taking into account internal and external
influences.
As the concept of "smart manufacturing" is at the stage of formulation and formalization, there are also
non-standardized classifications of its types, one of which is used in the seventh framework program of the
European Council for Scientific and Technical Cooperation, dividing factories into three main types [10]:
1. Digital production. The main tasks of digital production are the development of product models using
digital design and modeling tools, which begin to be used in the initial stages of research and development,
and end with the creation of "digital layout" (Digital Mock-Up, DMU), "digital twin" [11], a prototype, the
release of a small series or individual products customized to customer requirements. The main information
systems and technologies of digital production include computer-aided design (CAD): CAD / CAM / CAE;
PDM; PLM product lifecycle management software; machines with numerical program control; equipment for
3D production and other additive technologies.
2. "Smart" production. "Smart" productions carry out serial production of products, maintaining the maxi-
mum flexibility of production due to the high level of automation and robotics of production. Automated con-
trol systems of technological and production processes (ASTPP, PLM) are widely used. Industrial Internet of
Things technologies ensure the interaction of equipment in production processes. Production and technological
equipment, systems and technologies of enterprise management are equipped with sensors and means of com-
munication that work according to Internet exchange protocols, capable of producing products with minimal
or no participation. In connection with the transition to automated control systems for technological and pro-
duction processes with extensive use of sensors and communications, there is a need for systems of accumu-
lation and "intelligent" information processing, structured and unstructured data of large volumes and diver-
sity, which is solved by tools and technology concepts (Big Data, BD) [12]. Key ISs and smart manufacturing
technologies include process control systems; advanced planning and management of production orders (Ad-
vanced Planning and Scheduling, APS); Manufacturing Execution System (MES); Industrial Internet of Things
(IIoT); Big Data technologies.
3. Virtual production. A virtual factory is a network of digital and "smart" industries, which includes sup-
pliers of inventory, services and customers, consumers of enterprise products. To perform direct and reverse
integration management functions: supply and distribution chains, as well as planning and distributed produc-
tion, virtual enterprises are undergoing reengineering and implementation of automated enterprise manage-
ment systems ERP, MRP, MRPII, CRM, SCM, ECM (Enterprise Content Management) - management content
(content, data, information) of the enterprise, WF (Work Flow) - workflow management (task management
systems), DF (Document Flow) - data flow management (electronic document management systems). With a
high level of integration in the implementation of these systems, they allow you to design and use a virtual
model (DT) of all organizational, technological, logistics, production and other processes of the enterprise,
including interaction with suppliers and consumers.
5. Experiments
To analyze the impact of IS on enterprise development by assessing key performance indicators, the main
complex indicators were considered under four perspectives (табл.2).
The first industrial revolution increased labor productivity 10-20 times through discoveries and inventions
in various industries: weaving and spinning machines in light industry, lathes and milling machines in metal-
lurgy, agricultural machines.
The mechanization of formerly manual production reduced the time to create one car from 400 to 75 man-
hours during the second industrial revolution.
93
Table 2
Dynamics of key performance indicators in the fourth industrial revolution
Indica- Components of the indica- The dynamics of the indicator on aver- IS
tor tor age
I O4 i
ТP ↓ (30%) PLM + ERP
Т QC ↓ (7%) IoT
Т MP ↓ (40%) PLM + ERP
ТD/S ↓ (25% простої, 35% запаси) ERP, SCM, CRM
Т ER ↓ (50%) PLM
I 2
Fi
AE ↑ (25-40%) PLM
I F1i ↑ (31%) PLM + ERP
QP ↑ (24%) PLM + IoT
I 1
Ci
QP ↑ (57%) PLM+IoT+CRM
PP ↓ (25%) PLM + ERP
ТR ↓ (34%) PLM
I F4i Т MI ↑ (20-40%) PLM + ERP
I D1 i - ↑ (50%) PLM + ERP
I 7
Oi - ↓ (80%) SCM + PLM
I 1
Oi - ↑ (50%) CRM+CAD|CAE
Conveyor production has allowed factory owners to reduce the hiring of low-skilled workers. For example,
the reorganization of the Ford factory in 1921 allowed him to lay off 25% of workers and replace them with
conveyor processes.
The concept of the third revolution shifted the profit center from the stages of production to R&D centers,
development and design. A classic example was the uneven formation of added value in the chains of design
- creation and marketing - assembly. There was an increase in labor productivity and, as a consequence, a
reduction in blue-collar workers directly employed in production. From the point of view of corporate manag-
ment, traditional centralized business models are being replaced by distributed structures with horizontal in-
teraction and project management principles. The results of the impact of IS and information concepts - the
main drivers of the fourth industrial revolution, such as the industrial Internet of Things, digital twins, on the
performance of enterprises are presented in Table 2 [13].
6. Results
The main goal in the first and second industrial revolutions was labor productivity, which increased in the
range of 500–1000%, which was caused by radical changes in production technologies due to fundamental
discoveries in science and technology (Fig. 4, item 1). The third industrial revolution shifted the focus to re-
search and development, based on IS and specialized software. The increase in labor productivity is due to the
reduction of production time, which becomes automated and integrated with the design and calculation sys-
tems, but it should be noted a decrease in the intensity of productivity growth to 100% (pos.2).
94
Figure 4: The pace of industrial revolutions
The transition to the fourth industrial revolution also led to increased productivity, but at a slower pace.
This trend can be explained by the strategies that prevailed during the industrial revolutions, so if the first and
second revolutions were focused on the number of products with quality control and service life, the third
industrial revolution has already begun to consider the consumer not only in terms of satisfaction its need for
the product as such, but also in terms of functionality. The fourth revolution has retained this strategy, but with
greater capabilities for data processing, virtualization, rapid restructuring and quality control through artificial
intelligence, allows you to take into account almost all the wishes of consumers, receiving real-time feedback.
Formalized performance indicators (Table 2) can be used as a basis for assessing the dynamics of enterprise
development and the impact on this dynamic of both existing and future IS. The acceleration of the industrial
revolutions is obvious: almost a century passed between the first and second revolutions, the same period of
time was needed for the third revolution, and the term "fourth industrial revolution" began to be used in scien-
tific and business circles 50 years after the third. The widespread use of IS was the impetus for accelerating
the pace. The next revolution will be the result of the transition to radically new principles in computational
processes and the widespread use of neurosynaptic and quantum systems.
7. Discussion
The influence of IS on the pace of industrial revolutions is interesting from a practical point of view. The period of 250-
270 years, during which three and the fourth industrial revolutions took place, can be divided into two major stages: the
period before the emergence and intensive development of IS, which is characteristic of the first and second industrial revo-
lutions, and the period from the third revolution - the era of computer science and computer technology. It is obvious that
the development of IS, advances in artificial intelligence have twice accelerated the pace of new industrial revolutions. The
pace of the first two revolutions was determined by breakthroughs in the basic sciences of the time: the invention of the
steam engine, electricity and the possibility of its transmission, etc., which took place slowly. The era of computer technology
has dramatically changed the state of affairs, and the first were CAD, CAE, CAM systems, which accelerated the design and
development, and in combination with PDM and MRP systems allowed to increase overall productivity (Fig. 4, item 3).
The development of the CALS and PLM concept allowed to combine all production processes in a single information
environment, and the combination with ERP systems allowed to develop enterprise management strategies based on the triad
Business – Process – Product (Fig. 4, item 4). The concepts of Digital twins and the industrial Internet of Things allowed to
bring to a new level of data management in almost real time and significantly increase performance (Table 2).
The advent of the fifth technological revolution will obviously be determined exclusively by advances in IP, namely
artificial intelligence and big data in order to respond quickly to changes in production and business environments. Perhaps
the fifth industrial revolution will be a precursor to the technological singularity.
8. Conclusions
The results of research and analysis of trends in enterprise performance (Table 2, Figure 4) allow us to make
conclusions about doubling the pace of industrial revolutions since the emergence and widespread introduction
of IP while reducing the growth rate of productivity.
95
The main directions of transformation of modern production are determined by three global technological
trends: network integration, intellectualization and flexible automation.
The next technological revolution will be determined solely by the development of data science and the
transition to new principles of data processing, such as the use of neurosynaptic and quantum computers.
Scientific novelty of the study lies in the formalization of key performance indicators of enterprises, math-
ematical formalization of the system of balanced scorecard and the definition of IS, which affect the key per-
formance indicators and the nature of this impact. It should be noted that for the first time the following ele-
ments are considered on the same plane:
Models product, business logic, production processes LC
Key performance indicators to assess the effectiveness of these models
IS at all stages of models LC
The relationship between these elements
The practical value of the study lies in the possibility of implementing a balanced score card system in
relation to IS and models LC, which are integrated into a single management system.
Prospects for further research. Obviously, defining the indicators of the fifth industrial revolution, the main
trends and concepts that will determine it – an open question, and can logically develop the ideas and results
of the study.
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