Product Reliability Management throughout the Life Cycle on

Transition to Industry 4.0

Irina Makarova

, Eduard Mukhametdinov

, Vadim Mavrin

and Assema Duisenbayeva

Kazan Federal University, Syuyumbike prosp., 10a, 423822, Naberezhnye Chelny, Russian Federation

Kazakh-British Technical University, Tole bi Street, Almaty, Kazakhstan

Keywords: Reliability Management, Digitalization, Test Planning.

Abstract: The article deals with the actual problem: ensuring the reliability of technical systems in the digital era and at

the transition to Industry 4.0. The authors reviewed the existing positive international experience and

opportunities for digitalization, as well as particular qualities of the factories of the future and production

during the transition to Industry 4.0. It is shown that focusing on customer needs can create problems in the

field of ensuring the technical systems reliability. In addition, in such conditions it is important to shorten the

duration of various processes. As a particular example, the authors consider the process of conducting strength

tests. To reduce the time of testing, the article authors developed a DSS for document management in a central

strength laboratory of an automotive company. Although the authors investigated a specific example, but this

technique is universal and can be used for similar processes and for other sectors of the economy.

1 INTRODUCTION

The main trend in the economy and society

development, with which reasonable and rational

management and development of all activity areas,

including the automotive industry, is currently

associated is intellectualization. Technologies that

experts consider the most promising provide a

transition to the digitalization era. In the rapid

development conditions of technique and technology,

processes digitalization and intellectualization, it is

necessary to apply new management methods.

The internet penetration in all activity areas, the

methods emergence for finding optimal sustainable

solutions, is associated with the fourth industrial

revolution, which is the main trend in the automotive

industry development.

The high motorization level and markets

globalization are forcing automakers to search for

new solutions, constantly improving both the vehicles

design and production technology, as well as new

ways to attract customers.

https://orcid.org/0000-0002-6184-9900

https://orcid.org/0000-0003-0824-0001

https://orcid.org/0000-0001-6681-5489

https://orcid.org/0000-0002-3596-4841

Internet development, sustainable communication

channels, cloud technologies and digital platforms, as

well as information “explosion” of data, provided a

transition from enterprises’ local automation to open

information systems and global industrial networks

which go beyond the individual enterprises'

boundaries for cooperate with each other.

Such systems and networks transfer industrial

automation to a new, fourth, industrialization stage.

Digital technologies will make factories more

efficient, intelligent, flexible and dynamic.

Breakthrough developments in areas such as artificial

intelligence, nanotechnology, and others lead not

only to the creation of new market segments, but also

to a fundamental change in existing business models.

The combination of increased Internet

penetration, mobile devices, data analysis, the

“Internet of things” and machine learning change the

expectations and demands of consumers.

Digitalization helps to focus on the customer, so mass

production of a new type allows industrial production

of an individual product.

Makarova, I., Mukhametdinov, E., Mavrin, V. and Duisenbayeva, A.

Product Reliability Management throughout the Life Cycle on Transition to Industry 4.0.

DOI: 10.5220/0007899706710678

In Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2019), pages 671-678

ISBN: 978-989-758-374-2

671

2 STATE OF THE PROBLEM:

INTELLECTUALIZATION OF

MANAGEMENT AT ALL

STAGES OF THE PRODUCT’S

LIFE CYCLE

2.1 Digitalization as One of the Fourth

Industrial Revolution Directions

Economy’s digitalization is a new, real and objective

world trend that is replacing the previous “society’s

informatization”, which is positioned as

“technonomy” - the result of an electro-calculation

coup and technological breakthroughs of the late

twentieth and early twenty-first centuries. People use

global digital platforms to train, search for work,

showcase their talent and create personal networks. In

the globalization digital era, large companies can

manage their international operations in a more

economical and efficient way, as digital platforms

contribute to the labor market globalization. Data

streams open up for economy the ideas, research,

technology, talent, and best practices from around the

world. Many countries have developed national

digitalization strategies that highlight the transition

challenges to a digital economy. Each country in the

such programs framework determines its priorities.

Within the global economic system, Germany and

Europe are located between the dominant digital

models of China and the USA. In the American

model, digital platforms with global reach are

becoming a new competition to existing companies.

On the contrary, the Chinese model relies on the

domestic market, which enjoys greater protection

from the state. From a European point of view, it

makes sense to copy some successful aspects of

models from the USA and China.

The American neo-industrialization model - the

industrial Internet model - is rigidly embedded in the

things existing order in new technologies and is

looking for solutions to compatibility and security

problems in the future (Bledowski, 2019). The USA

economy is being digitized quickly, but unevenly.

Geographically, digitization is everywhere, but its

progress varies greatly. The digital economy is

driving an unprecedented expansion in the

increasingly smart cities number with closer ties.

Ultimately, when the Internet is actively developing,

the Internet sector gives great hope to cities to use

technology for build a more inclusive, innovative

economy. It is becoming increasingly clear that cities

are leading the national innovation agenda.

The “Made in China 2025” program envisages

numerous initiatives ranging from the advanced

technologies development in the robotics field and the

industrial Internet to programs to modernize labor-

intensive industries through automation (Butollo,

2017). Thanks to the impact on the emerging IoT, as

well as parallel efforts to dominate the electric

vehicles industry, China’s 5G efforts are a

particularly serious problem for German and

Japanese automotive and semiconductor companies.

At present, the Chinese model is particularly

competitive from an economic point of view. The

Fortune Global 500 companies are increasingly

diversifying, particularly in Asia, while their number

in the United States is declining. This may indicate

that the Chinese business model based on secure

domestic markets and government subsidies with

global reach and relevance is more successful than the

US business model based on creative destruction. In

the technological competence area, including

standards, a further eastward shift is also clearly

visible. In September 2017, the UAE government

presented the UAE Strategy for the Fourth Industrial

Revolution, which focuses on key areas; some of

them are innovative education, artificial intelligence,

intelligent genomic medicine and robotic healthcare.

The European approach is based on global reach

and on the digital business models expansion which

based on future technologies and on European skills.

From a technology perspective, it is necessary to

master two critical requirements aspects for the future

of digital business in Germany and Europe. First, the

future technologies understanding and the experience

development in them: AS (Autonomous Systems),

AIoT (Artificial Intelligence of Things) and AR

(Augmented Reality), what by orders of magnitude

will expand technical capabilities and, therefore,

underlie constant technological changes. Secondly,

the emphasis is on safety and reliability, since it

provides a rationale for the European differentiating

factor and competitive advantage. From the European

point of view, it makes sense to copy some successful

aspects of the California and Chinese models. In

particular, they include a domestic markets

reassessment or the national programs of excellence

implementation, such as the DARPA in the United

States or the Talpiot program in Israel. Many

countries have created national initiatives and

campaigns to digitalization their industries and

economies. The best-known campaign Industrie 4.0

from Germany (Pfeiffer, 2016), which aims to

promote the new technologies development, the

typical factories creation and reference solutions, as

well as the standards definition. Due to the high level

LogiTrans 4.0 2019 - Special Session on Logistics and Transport in the Industry 4.0

672

of public and private investment and a participant’s

large number, its was able to achieve a significant

effect. Similar projects exist in other countries: Smart

Factory in the Netherlands, Usine du Futur in France,

High Value Manufacturing Catapult in the UK,

Fabbrica del Futuro in Italy. Poland, so far, ranks 23rd

out of 28 on the digital economy and society index in

the EU, significantly lagging behind in all areas: from

using social networks by enterprises (only 9 percent)

to subscribing to fast broadband access throughout

the country. However, the priorities and indicators set

for the Digital Poland program are in line with the EU

2020 Strategy and, in particular, the European digital

agenda. The program focuses on the high-speed

broadband deployment and the electronic services

development for public administration, and also

supports initiatives to improve the citizen’s digital

competence.

In Russia, the program “Digitization of the

economy” includes six federal projects (The

Digital…, 2019): (1) digital environment normative

regulation; (2) personnel for the digital economy; (3)

digital technologies and projects; (4) information

infrastructure; (5) information security; (6) digital

state. Similar programs were adopted by Belarus and

Kazakhstan (About…, 2019), which identified key

areas: (1) digitization of industries; (2) transition to a

digital state; (3) implementation of the digital Silk

Road; (4) human capital development; (5) an

innovation ecosystem creation.

2.2 Smart Factories: Problems and

Prospects

Enterprises based on the principle of Industry 4.0 are

needs-oriented production, i.e. must respond directly

to consumer demand. Thanks to the data collected, it

will be possible to predict user behaviour and

integrated this data into a production information

environment, including human resource planning

(Brettel, 2014). Artificial intelligence will allow you

to control the entire product life cycle - from the

demand marketing study, production and operation,

to utilization. Industry 4.0 implies the use of the

Internet of Things (IoT) (Zawra, 2018) and Big Data

(Santos, 2017) in production, when any components

of the system are interconnected with the help of the

World Wide Web, and also independently find ways

to reduce costs. At the same time, it is very important

that the production processes do not become more

expensive: by connecting all elements through the

network, it becomes possible to find the optimal, non-

costly way to realize orders. Industry 4.0 assumes the

rational use of natural and technical resources, the

most efficient energy saving, the all waste recycling

and the receipt of new goods, raw materials or energy

from them. It is assumed that intelligent materials and

devices will help reduce equipment downtime and the

need for maintenance personnel, increase level of

equipment use, which will lead to technological and

logistic processes optimization and increase

production efficiency. In addition, Industry 4.0.

suggests the concept of digital twins. For example,

the creation of a virtual process and its connection

with the actual physical process in the enterprise

(Żywicki, 2018) allows you to explore the processes

parameters by exchanging data between the virtual

and real processes, saving time and money on

assembly and commissioning. In addition, the

creation of a system of virtual simulators will allow

staff to work out the actions that need to be taken in

the system in virtual workplaces.

The concept of an intellectual factory is based on

a highly automated, and at the same time flexible,

cyber-physical manufacturing system, which is

characterized by quick response to customer

requirements. This requires innovative and intelligent

solutions not only in terms of objects (for example,

the Internet of Things), but also for processes (for

example, Knowledge Based Engineering) (Górski,

2016). That is why smart design and production

control must be necessary elements of an intelligent

factory of the future, capable of implementing a mass

customization strategy (Zawadzki, 2016; Mueller,

2012). Only then it will be possible to fully use the

production potential of the company, which owns

modern technical resources, in accordance with the

concept of Industry 4.0 (Gorecky, 2014).

The production stage of the life cycle is one of the

most important, because exactly at this stage ideas

and projects turn into finished products. Besides, the

quality of the product depends on the quality of

manufacturing. It means that at this stage it is

determined if the targeted audience is large enough,

if the product is competitive in the market, how

effective and safe are the stages of operation and

service. The main goals to Industry 4.0 transition are

process optimization by reducing losses and customer

focus. The need for production systems’ constant

adjustment to customer variable requirements ensures

the introduction of new methods within the

framework of process organization or production

control (Trojanowska, 2011). Transformation into

industry 4.0 requires highly efficient and flexible

production planning processes. Automated

production processes require complex computer

planning processes. These are the so-called CAx

systems (computer technologies), such as CAD

Product Reliability Management throughout the Life Cycle on Transition to Industry 4.0

673

(computer-aided design), CAM (computer automated

production), TLM (tool life cycle management),

DNC (distributed numerical control), CAPP

(computer automated process planning) and functions

- such like process modeling. The production strategy

implementation in accordance with the individual

customers’ needs is also a serious problem for the

production planning organization and control

processes. Therefore, it is necessary to apply optimal

solutions for each of the processes: production

planning, monitoring material flow or decision

support. This is necessary for the effective use of

available resources while meeting the individual

needs of clients (Kujawińska, 2016; Żywicki, 2017;

Trojanowska, 2017; Gangala, 2017;

Rewers, 2017).

If constant adjustment of changes is necessary, then

production planning can be called Fast Dynamic

Scheduling (Kujawińska, 2009).

Options preparation for the material flow is one of

the elements that should be considered when

planning. This will determine the most efficient

product flow that meets the expected criteria, for

example, optimizing production resources or

reducing delivery time. In order to realize customer

requirements, accepted planning methods should take

into account the production resources availability,

allowing to organize the necessary goods production.

This allows you to respond quickly if there are new

orders or new factors that make it impossible to use

resources. This requires careful integration of

production planning and control with product design

(Makarova et al., 2018a; Szuszynski, 2015; Żywicki,

2017, Makarova et al., 2018b).

A completely new engineering approach - digital

enterprise models, the so-called “factories of the

future”, involve the integration of computing,

networks and physical processes. At the same time,

built-in computers and networks monitor and control

physical processes, with feedback, where physical

processes influence calculations and vice versa.

In Smart Factory, production processes will be

organized differently: whole production chains - from

suppliers to logistics to product lifecycle management

- are closely linked between corporate boundaries.

Separate production steps will be easily connected.

Impact processes will include: (1) factory and

production planning; (2) product development; (3)

logistics; (4) Enterprise Resource Planning (ERP); (5)

Management of Production System (MES); (6)

Management Technologies; (7) separate sensors and

actuators in the field. Despite the fact that there are

numerous software platforms for processes’

intellectualization throughout the product life cycle,

however, there are a number of difficultly formalized

processes associated with the information search and

processing. The duration of such processes depends

on the “human factor”, therefore the task of their

intellectualization remains relevant. These tasks are

specific to each company, so the software is designed

individually for a particular case.

Despite the presence of a large number of

enterprise management systems, there are still the

processes that, for various reasons, are difficult to

formalize within the existing management systems.

Therefore, it is necessary to develop special software

modules that are “embedded” in the management

system that exists in the enterprise. This may be a

DSS for any enterprise’s department, receiving

information from both the external circuit and other

enterprise’s departments itself.

3 RESULTS AND DISCUSSION

Competitiveness issues are solved at all product’s life

cycle stages. To a large extent, competitiveness

depends on the speed of updating the model range and

the products reliability during operation. Since the

vehicle is a complex technical system consisting of

many parts, its reliability depends on how reliable

these parts are. Although there are not so many details

limiting reliability, however, the issues of increasing

their reliability and predicting possible replacement

periods are relevant. Therefore, there is a need to test

both at the new product design stage, and in the case

of finding out the reasons for repeated failures. The

requests' execution speed to testing depends on many

reasons, among which the human factor plays a

significant role. Therefore, the best solution to the

problem could be the DSS.

The product reliability is the competitiveness

basis, since it involves trouble-free and safe

operation. In the transition to Industry 4.0, this

direction becomes more actual, since in product

uniqueness case, made for a specific customer,

problems may arise with reliable statistical

information about failures of units and parts of a

complex product during operation. The situation is

aggravated by the presence of a large number of

suppliers of components and spare parts, which in

varying degrees provide durability and product

maintainability. In such conditions, the product

manufacturer should be able to plan and conduct

additional tests more quickly. These processes are

accompanied by a large number of heterogeneous

documentation, which differs both in its source and in

its purpose. This determines the type of information.

The processing of such data, the search for documents

LogiTrans 4.0 2019 - Special Session on Logistics and Transport in the Industry 4.0

674

and their design is performed manually. To speed up

the workflow processes in such cases is possible with

the help of a special intelligent system.

To identify the time reserves, the data flows’

processes diagrams (Figure 1, 2) of new vehicles

models’ designing, testing, starting mass production

and operation beginning were constructed.

In the activities of the scientific and technical

center of PC KAMAZ, an important place is occupied

by the Central Strength Laboratory (CSL). Here, all

the tests of vehicles detail and aggregates,

applications for which are received from other

departments, are performed.

The problem with the requisitions processing and

tests planning is that a large part of the information

necessary for work is on different information media.

This method of storing information increases the

access time to it and complicates its analysis, since

the search for the necessary document, as a rule, is

carried out in manual mode. In addition, the risks

associated with information corruption and loss of its

integrity is increasing. Creating a DSS, which allows

structuring this information and creating a common

information space, is an important task to speed up

and optimize the testing process. The DSS structure

provides a consolidated documents database will be

filled in by the staff of the CSL department. DSS will

display information from the database in

predetermined forms that are convenient for work and

analysis. This will allow analyzing the test results in

the event of problems at the stages of the product life

cycle, as well as with the appearance of new vehicles

models and modifications of its components and

aggregates. In addition, the acceleration of the testing

process and access to information through the

creation of a common information space will provide

an opportunity for a deeper analysis of the reliability

of vehicle parts and assemblies, for example,

choosing a better vehicle parts supplier.

The following information should be stored in the

database: (1) Grounds for work (requests for tests and

schedules); (2) Data on parts and components

entering the tests; (3) Data on requests in the invoices

form required to obtain parts from the warehouse and

their further write-off after testing; (4) Test reports

(summary of test results); (5) Acts of write-offs

(contain information about the details written off).

The conceptual DSS scheme is depicted in Figure

3. Working documents contain information coming

from external sources, as well as emerging from the

various technological processes implementation.

Document types are listed above. With the help of an

Interpreter, requests that are formulated by the staff

of the CSL find the information necessary for the

tests. The speed of query execution depends on the

adequacy of the interpreter of documents. To store

information about documents, a relational data

structure is used, for implementation of which

Microsoft SQL Server is chosen. The interpreter is

implemented as a software module in Delphi 7. The

user interface of the module contains several tabs and

buttons for switching to data entry forms in the

database. There are different program windows for

data entry, as data in related documents can be

entered at different times (Figure 4). Convenient

search and filtering of data in the database provides a

simple and intuitive interaction with DSS, which

allows its use even to unprepared users.

Test examples were created for DSS verification.

To verify the DSS work correctness, information was

searched for the parts obtained for testing in

accordance with the request entered in the Database.

The necessary request and the invoice for receiving

the details found by the request results confirmed the

correctness of the program’s work. Further, it was

necessary to check the DSS use effectiveness for

speeding up the processes in the CSL. This can be

done using a virtual experiment on a simulation

model. To do this, we determine the time spent on

conducting similar test before and after the

implementation of the DSS using the developed

simulation models.

Figure 1: Data flows’ processes diagrams of new vehicles models’ designing, testing, starting mass production.

Product Reliability Management throughout the Life Cycle on Transition to Industry 4.0

675

Figure 2: Diagram of data flows of the process “Testing of units and vehicle parts”.

Figure 3: Conceptual scheme of DSS.

Figure 4: User interface for data entry and search.

4 CONCLUSIONS

The article shows that the use of DSS for document

management in the central strength’s laboratory of an

automotive company reduces the total time spent on

testing. The processes model after DSS introduction

is shown in Figure 5. Figure 6 shows a graph of the

performance assessment of the DSS. As a result of the

conducted simulation experiments, it was obtained

that the testing process duration before DSS using is

from 20 to 36 days, and after - from 18 to 34 days.

The schedule of time spent on testing is shown in

Figure 7. Thus, the DSS usage allows to speed up the

testing processes, by accelerating the work with the

documents during the preparation of the tests and after

their completion. This method is universal and can be

used for similar processes and for other sectors of the

economy.

LogiTrans 4.0 2019 - Special Session on Logistics and Transport in the Industry 4.0

676

Figure 5: Process model a) before b) after DSS introduction (the record in the DB is distinguish with thick lines).

Figure 6: Graphs working time.

Product Reliability Management throughout the Life Cycle on Transition to Industry 4.0

677

Figure 7: Performance evaluation of the DSS.

REFERENCES

About the Program. URL: https://digitalkz.kz/en/about-the-

program/ last accessed 2019/01/21

Bledowski, K., 2015. IoT in Germany and America. URL:

https://www.mapi.net/blog/2015/08/iot-germany-and-

america, last accessed 2019/01/21

Brettel, M. et al., 2014. How virtualization decentralization

and network building change the manufacturing

landscape: An Industry 4.0 Perspective. Int. J. of Science

Engineering and Technology 8(1), 37-44.

Butollo, F., Lüthje, B., 2017. Made in China 2025: Intelligent

Manufacturing and Work, In book: The New Digital

Workplace, 42-61.

Gangala, C. et al., 2017. Cycle Time reduction in deck roller

assembly production unit with value stream mapping

analysis. Rec. Adv. in Inf. Sys. and Tech. 571, 509–518.

Gorecky, D. et al., 2014. Human-machine-interaction in the

industry 4.0 era. In: Int. Conf. on Indust. Inf., 289–294.

Górski, F. et al., 2016. Knowledge based engineering as a

condition of effective mass production of configurable

products by design automation. J. Mach. Eng. 16, 5–30.

Kujawińska, A., 2009. The data model of production flow

and quality control system. Studia Inf. 30, 109–126.

Kujawińska, A., Rogalewicz, M., Diering, M., 2016.

Application of expectation maximization method for

purchase decision-making support in welding branch.

Manag. Prod. Eng. Rev. 7 (2), 29–33.

Makarova, I. et al., 2018. Automotive Enterprises Flow

Production Improvement Based on the Management

Process Intellectualization. In: World Symp. on Digital

Intelligence for Systems and Machines, 115-118.

Makarova, I., Shubenkova, K., Pashkevich A., 2018.

Improving Reliability Through the Product’s Life Cycle

Management. In: 23rd Int. Conf. on Methods & Models

in Automation & Robotics, 154-159.

Mueller, W. et al., 2012. Virtual prototyping of cyber-

physical systems. In: 17th Asia and South Pacific Design

Automation Conference, 219–226.

Pfeiffer, S., 2016. Robots, Industry 4.0 and Humans, or Why

Assembly Work is More than Routine Work. Societies

6(2), 16–42.

Rewers, P. et al., 2017. Production leveling as an effective

method for production flow control—experience of

polish enterprises. In: Proc. Eng. 182, 619–626.

Santos, M.Y. et al., 2017. A Big Data system supporting

Bosch Braga Industry 4.0 strategy. Int. J. Inform.

Manage. 37, 6, 750-760.

Szuszynski, M., Żurek, J., 2015. Computer aided assembly

sequence generation. Manag. Prod. Eng. Rev. 6, 83–87.

The Digital Economy national program was officially

presented at the Open Innovations forum. URL:

http://ac.gov.ru/en/events/018558.html.

Trojanowska, J. et al., 2017. The tool supporting decision

making process in area of job-shop scheduling. In: Adv.

in Int. Sys. and Comp. 571, 490–498.

Trojanowska, J., Żywicki, K., Pająk, E., 2011. Influence of

selected methods of production flow control on

environment. In: Golinska, P., Fertsch, M., MarxGomez,

J. (eds.) Inf. Tech. in Env. Eng., 695–705.

Zawadzki, P., Żywicki, K., 2016. Smart product design and

production control for effective mass customization in

the Industry 4.0 concept. Manag. Prod. Eng. Rev. 7(3),

105–112.

Zawra, L.M. et al., 2018. Utilizing the internet of things (IoT)

technologies in the implementation of industry 4.0. Adv.

Intel. Sys. Compu. 639, 798-808.

Żywicki, K. et al., 2018. Virtual reality production training

system in the scope of intelligent factory. Adv. Intel. Sys.

Compu. 637, 450-458.

Żywicki, K., Rewers, P., Bożek, M., 2017. Data Analysis in

Production Levelling Methodology. In: Rec. Adv. in Inf.

Sys. and Tech. 3, 460-468.

Żywicki, K., Zawadzki, P., Hamrol, A., 2017. Preparation

and production control in smart factory model. In: Adv.

in Inf. Sys. and Tech., 519–527.

LogiTrans 4.0 2019 - Special Session on Logistics and Transport in the Industry 4.0

678