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Determination of Changes in Process Management within Industry 4.0

Authors:
Andrea Benešová at University of West Bohemia
Andrea Benešová
Martin Hirman at University of West Bohemia
Martin Hirman
Frantisek Steiner at University of West Bohemia
Frantisek Steiner
Jiri Tupa at Faculty of Electrical Engineering, University of West Bohemia, Pilsen, CZE
Jiri Tupa
  • Faculty of Electrical Engineering, University of West Bohemia, Pilsen, CZE
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Abstract and Figures

The company’s ability to adapt to rapid market changes will be among the key factors for Industry’s competitiveness within Industry 4.0. The basic of flexibility is quick respond to customer requirements and well-set and controlled production processes. Processes of Industry 4.0 will be different from existing processes, not only in terms of using new technologies such as digitization or augmented reality, but also in terms of management and support processes. The main aim of the article is possible changes determination of process management within Industry 4.0. For that, the current production process will be compared with the process in Industry 4.0. The described changes within Industry 4.0 will also have an impact on the organization architecture of company. The changes will be also in the production environment and in the supply chain. The described changes in the process management will also have an impact on the company risks. The main risks of changes within Industry 4.0 will be summarized in the article.
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ScienceDirect
Available online at www.sciencedirect.com
Procedia Manufacturing 38 (2019) 1691–1696
2351-9789 © 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the Flexible Automation and Intelligent Manufacturing 2019 (FAIM 2019)
10.1016/j.promfg.2020.01.112
10.1016/j.promfg.2020.01.112 2351-9789
© 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientic committee of the Flexible Automation and Intelligent Manufacturing 2019 (FAIM 2019)
Available online at www.sciencedirect.com
ScienceDirect
Procedia Manufacturing 00 (2019) 000–000
www.elsevier.com/locate/procedia
2351-9789 © 2019 The Authors, Published by Elsevier B.V.
Peer review under the responsibility of the scientific committee of the Flexible Automation and Intelligent Manufacturing 2019
29th International Conference on Flexible Automation and Intelligent Manufacturing
(FAIM2019), June 24-28, 2019, Limerick, Ireland.
Determination of Changes in Process Management within Industry
4.0
Andrea Benešováa, Martin Hirmana, František Steinera, Jiří Tupaa*
University of West Bohemia, Department of Technologies and Measurement, Univerzitní 8, Pilsen 301 00, Czech Republic
Abstract
The company's ability to adapt to rapid market changes will be among the key factors for Industry's competitiveness within
Industry 4.0. The basic of flexibility is quick respond to customer requirements and well-set and controlled production processes.
Processes of Industry 4.0 will be different from existing processes, not only in terms of using new technologies such as
digitization or augmented reality, but also in terms of management and support processes. The main aim of the article is possible
changes determination of process management within Industry 4.0. For that, the current production process will be compared
with the process in Industry 4.0. The described changes within Industry 4.0 will also have an impact on the organization
architecture of company. The changes will be also in the production environment and in the supply chain. The described changes
in the process management will also have an impact on the company risks. The main risks of changes within Industry 4.0 will be
summarized in the article.
© 2019 The Authors, Published by Elsevier B.V.
Peer review under the responsibility of the scientific committee of the Flexible Automation and Intelligent Manufacturing 2019
Keywords: Digitization; Industry 4.0; Process Management; Risk Management; Smart Factory
1. Introduction
One of the important goals of each business is its competitiveness, i.e. the promotion of a particular business. The
same goal will be important also for smart factories in the future. Competitiveness of companies depends on their
Available online at www.sciencedirect.com
ScienceDirect
Procedia Manufacturing 00 (2019) 000–000
www.elsevier.com/locate/procedia
2351-9789 © 2019 The Authors, Published by Elsevier B.V.
Peer review under the responsibility of the scientific committee of the Flexible Automation and Intelligent Manufacturing 2019
29th International Conference on Flexible Automation and Intelligent Manufacturing
(FAIM2019), June 24-28, 2019, Limerick, Ireland.
Determination of Changes in Process Management within Industry
4.0
Andrea Benešováa, Martin Hirmana, František Steinera, Jiří Tupaa*
University of West Bohemia, Department of Technologies and Measurement, Univerzitní 8, Pilsen 301 00, Czech Republic
Abstract
The company's ability to adapt to rapid market changes will be among the key factors for Industry's competitiveness within
Industry 4.0. The basic of flexibility is quick respond to customer requirements and well-set and controlled production processes.
Processes of Industry 4.0 will be different from existing processes, not only in terms of using new technologies such as
digitization or augmented reality, but also in terms of management and support processes. The main aim of the article is possible
changes determination of process management within Industry 4.0. For that, the current production process will be compared
with the process in Industry 4.0. The described changes within Industry 4.0 will also have an impact on the organization
architecture of company. The changes will be also in the production environment and in the supply chain. The described changes
in the process management will also have an impact on the company risks. The main risks of changes within Industry 4.0 will be
summarized in the article.
© 2019 The Authors, Published by Elsevier B.V.
Peer review under the responsibility of the scientific committee of the Flexible Automation and Intelligent Manufacturing 2019
Keywords: Digitization; Industry 4.0; Process Management; Risk Management; Smart Factory
1. Introduction
One of the important goals of each business is its competitiveness, i.e. the promotion of a particular business. The
same goal will be important also for smart factories in the future. Competitiveness of companies depends on their
1692 Andrea Benešová et al. / Procedia Manufacturing 38 (2019) 1691–1696
2 Author name / Procedia Manufacturing 00 (2019) 000–000
competitive advantages. The competitive advantage of a company is represented by various factors such as property
ownership, technology, resources, highly qualified employees, but in the first place by flexibility. The company's
ability to adapt to rapid market changes will be among the key factors for Industry's competitiveness within Industry
4.0. [1] The development and implementation of digitization and new technologies into production have caused
changes in the industry, referred to as Industry 4.0. Industry 4.0 marks a new industrial revolution based on
connection of virtual and real world. The main vision of Industry 4.0 is the emergence smart factories. [2] In the
smart factory a machines will be connected to the Cyber-physical systems (CPS). This system will allow the
communication and also cooperation of independent units (sensors, machines). The units will be able to decide
independently, manage the assigned technological units and become an independent and full-fledged member of
complex production processes. [3] The building blocks of smart factory are the nine foundational technologies –
Autonomous robots, Internet of Things (IoT), Big data, Simulation, Horizontal and Vertical system integration,
Cloud computing, Cybersecurity, Additive manufacturing and Augmented reality.
These nine technology trends will transform production into a fully integrated, automated and optimized
production flow. Production processes must be connected to production planning, supply and customer processes.
The timely analysis of the obtained data (Big Data) from the production processes wi ll be important for planning
resources, maintenance and managing of the flexible production. [4] Therefore, the company is forced to constantly
adapt its business to the external influences of the market. Hammer and Champy describe these impulses as the
"3C". Each C then represents one impulse - customers, competition and change. [5] The basic of flexibility is quick
respond to customer requirements and well-set and controlled production processes. This is mainly related to
business process management (BPM). Process management is characterized as a systematic activity that includes
identification, description, measures, management, evaluation and improvement of processes. Different systems,
methods, tools are used for this systematic activity. Management thus contributes the creation of new value in the
production process. But the Industry 4.0 introduces a new approach to organizing and managing production.
One research question is linking with the future of the process management and its implementation for concept
Industry 4.0. This paper tries to find answer for mentioned research question based on review of suitable
technologies and methods for BPM and risk management implementation.
2. Literature review
Currently various unique process control and optimization solutions are used for process management. These
solutions combine Internet of Things (IoT) technology and advanced process control methods based on
mathematical modeling, predictive control or neural networks. Mathematical method is used to model a controlled
system in detail and propose optimal settings for an existing management system for higher efficiency, higher
quality, or resource reduction. [6] Furthermore, IoT technology is also used to measure process performance itself or
only certain desired factors (pressure, temperature or humidity). [7] This industrial revolution will affect not only
changes in manufacturing processes (implementation of new technologies) but also have an impact on the
management of processes (Lean 4.0), related processes (supply chain), the organization architecture of company and
Human Resources. [8] The Lean principles will be changed by the integration of specific Industry 4.0 tools and
methods. The application of modern information and communication technologies (ICT) into Lean Production
Systems can improve the performance of Lean Productions Systems by gaining more efficient production and
logistics processes. [9]
2.1. Business process management (BPM)
Business Process Management (BPM) is a set of activities that relate to planning and performance monitoring of
company´s processes. These activities are design, modeling, execution, monitoring, and optimization. Management
is done over time and in the following steps – identification of process, established of goals, determination of the
control algorithm, organization, decision and control. [10] The basis is the model of the process itself. In the Fig. 1,
there is description of the production process.
Andrea Benešová et al. / Procedia Manufacturing 38 (2019) 1691–1696 1693
Author name / Procedia Manufacturing 00 (2019) 000–000 3
Process
Plan
Do
Check
Act
Fig. 1. Description of production process
2.2. General production process model
Each process is determined using these attributes:
• Inputs and Outputs of process
• Resources of process
• Process boundaries
• Owner
• Supplier/Customer
The customer is important for the analysis of the company's basic processes. The company must produce
products that respond to the customer's requirements. For this reason the production process must be flexible,
adaptable and varied. The customer may be external or internal. An external customer is a consumer who pays for
the final product (output). An internal customer is a customer within the organization or the organization itself (the
organization is a customer for its supplier). Another important attribute is the process resources, resources are
further divided into human, financial, information and infrastructure. Regulators are the various laws, standards and
internal regulations that affect the process. Deming or PDCA cycle is a management method used for control and
continuous improvement of processes. This method has four step – plan (planning the intended improvement), do
(implementation of plan), check (verification of the result of the implementation compared to the original plan) and
act (implementation of improvements to practice).[11][12] This is a description of the general production process
model by the BPM that will compare with Industry 4.0 production process model.
3. Comparison between current production process with the process of Smart factory
3.1. Production process of smart factory
The smart factory production process can be defined as connected and flexible manufacturing system. The
devices will be connected by the Cyber-physical systems (CPS) and Internet of Things (IoT). A machine to machine
communication (M2M) will be created. This manufacturing system will be continuous stream of data (Big Data)
from production devices to learn and adapt production process to new demands. [13] The digital image of factory
will be obtained in real time from the visualization of this data. This digital twin of factory will be necessary for
manage the production process. Using a digital twin, companies can experiment, monitor, predict, simulate, and
decide different situations in production. So companies can fine-tune all the details, but also any device errors
Input
Output
Regulators
Resources
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4 Author name / Procedia Manufacturing 00 (2019) 000–000
without the risk of time or financial loss. [14] This is also related to Cybersecurity, the data from production and
products will represent know-how of company. As a result, the number of cyberattacks is expected to increase. The
attacks will be mainly aimed at disrupting the company's production process. This also will relate to changes in
process regulators. The input of process will be a RFID sensor, QR code or barcode that will contain all information
about product. Each product will be unique to the customer's requirements. The machine will communicated with
sensors and on the basis of the necessary information from the sensor, adapt the production of the product and
supply chain management. The IoT sensors can be used for communication between machine and supplier of
material. At present, the process owner was responsible for the process. In most cases, the process owner is a person,
for example company owner or employee. The process owner in smart factory can be a machine, system or a person.
Because the machines and the system will customize the production process on the basis of the previously collected
data. But the person who manages the entire system can also be the process owner. Changes in organizational
structure and human resources also relate to this issue. It is expected that some professions will be replaced because
only qualified employees will be able to control the new technologies. Companies will primarily need employees
with skills and knowledge in information technology, for example to Cybersecurity and Data analysis sector. [15]
3.2. SIPOC (Supplier, Input, Process, Output, Customer)
The abbreviation SIPOC is a composite of the first letters of the English words: Supplier, Input, Process, Output
and Customer. It is another method used to describe the process and it is a tool for process improvement. Also we
use this method for description of general production process for compare with production process of smart factory.
In the Table 1., there is a description of general and simplified production process by SIPOC. The brainstorming
with experts was used for creation of this SIPOC.
Table 1. SIPOC of general production process
S
I
P
O
C
Customer Order Receipt of the order
Product
specification Production
Production
Product
specification Preparation of production plan Production plan Manager of production
Manager of production Order Order of materials (warehouse)
Delivery of
material Assembly line
Assembly line
Material
Production of the product
Product
Quality Department
Quality Department Product Quality control Final product
Separation of
packaging
Separation of
packaging Final product Packaging of the product Packaged product Supplier
Supplier Packaged product Delivery Packaged product Customer
If we compare this simplified manufacturing process with the smart factory process, we will see that major
changes occur with the suppliers and customers of the process. The customer and order of the product remains the
same but the process of receipt of the order and output can be change. For the order, the customer can use the IoT
service and then the output will be a sensor that will contain all the specifications of product from the customer. The
customer of this sensor will be a machine that can communicate with this sensor and other devices in production.
Using this communication, the system itself will plan and set the production plan and production process. The
system and machine will contact the material supplier if necessary by the IoT sensors. The quality control of product
should be performed by an employee. Packaging of the product will be provided by the machine which then contacts
the supplier.
Andrea Benešová et al. / Procedia Manufacturing 38 (2019) 1691–1696 1695
Author name / Procedia Manufacturing 00 (2019) 000–000 5
3.3. Risk Management
The implementation of new technologies will impact not only Business Process Management but also to Risk
Management. To assess the risks, the company will be using the digital twin of production that will enable company
predict, simulate different situations in production and thus reduce the potential risk. Within Industry 4.0, new risks
are emerging for companies, the cybersecurity and Human Resources will be the biggest risks. The semi-quantitative
risk assessment method was used for evaluating risks. The analysis of risks was provide in four steps – identification
of risk, evaluation of probability, evaluation of impact and calculation of Risk value (RV). Risk identification was
conducted based on brainstorming with experts and literary research. Then, impact (I) and probability of occurrence
(PoO) were established for each risk. The value and levels of impact (I) are 1 (very low), 2 (low), 3 (medium), 4
(high) and 5 (very high). The value and levels of probability of occurrence (PoO) are 1 (rare), 2 (unlikely), 3
(possible), 4 (probable) and 5 (highly probable). [16]
  ()=    () ×  () (1)
The risk value (RV) is calculated according to the equation. The risks can be classified by the risk value into
several categories. The most common categories of risk value (RV) are 1 to 3 (low), 4 to 9 (medium), 10 to 15 (high)
and 20 to 25 (very high). [17]
Table 2. The list of identified and evaluated risks
Identified risk
Probability
Impact
Risk value
Lack of own financial resources
4
5
20
Subsidy from the state
4
3
12
Lack of qualified employees
4
5
20
Lack of Cybersecurity
4
5
20
Lack of knowledge about Industry 4.0
4
5
20
Improper maintenance of the machine
2
5
10
Power outage
3
5
10
CPS system failure
3
5
15
Poorly evaluated data (Big Data)
2
5
10
Loss of know-how
3
5
15
Loss of customers
2
5
10
Production of defective product
2
5
10
Non-innovative product
3
5
10
Damage of sensor with product specification
1
5
5
IoT network failure
3
5
15
Crash (fire, chemicals)
1
5
5
4. Conclusion
Based on a comparison of the general process with the intelligent manufacturing process, it was found that from
the point of view of the general description of the process no changes will occur. Process attributes also will be
inputs and outputs, resources, process boundaries and supplier/customer. Changes appear only within individual
attributes and division of processes. Because every smart factory production must to include the following
Autonomous robots, Cyber-physical system and Internet of Things (IoT). The autonomous robots, Cyber-physical
system and Internet of Things (IoT) will be a mandatory resources of production process. Cyber-physical system can
also be understood as a management process of smart factory production process. On the contrary, Internet of
1696 Andrea Benešová et al. / Procedia Manufacturing 38 (2019) 1691–1696
6 Author name / Procedia Manufacturing 00 (2019) 000–000
Things (IoT) can be understood as a supporting process. Another change is in attribute of customer, customer can be
person but also machine or manage cyber-physical system (CPS). In the production process will appear new inputs
in the form of sensors or codes (QR code, barcode) that will contain product specifications. Another change in terms
of Business Process Management will be in the regulators of production process. As a result of the expected increase
of cyber attacks, laws and standards will need to be updated. Similarly, standards relating to place the product on the
market or customer protection. Horizontal and Vertical system integration will occur another change in terms of
enterprise architecture. This will remove hierarchical levels to ensure a better flow of information. Temporary
parallel structures will also be introduced such as project or realization teams. Following a risk analysis, it was
found that very high risk of Industry 4.0 are lack of own financial resources, lack of qualified employees, lack of
Cybersecurity and lack of knowledge about Industry 4.0. Corrective measures should be established for these risks.
Acknowledgements
This research has been supported by the Ministry of Education, Youth and Sports of the Czech Republic under
the RICE – New Technologies and Concepts for Smart Industrial Systems, project No. LO1607 and by the Student
Grant Agency of the University of West Bohemia in Pilsen, grant No. SGS 2018-016 "Diagnostics and Materials in
Electrical Engineering" and by the Technology Agency of the Czech Republic under the project Software platform
to accelerate the implementation of management systems and process automation — project No. TH02010577.
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... Changes in the organization, management. There will be a need to adjust the organizational process at the enterprise, which requires time and effort [32]. It is also necessary to eliminate Data silos -a phenomenon when different information systems are separated and cannot effectively work together. ...
The Strategy of Digital twin Implementation at the Cyber-Physical Enterprises
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Full-text available
  • Jul 2024
The strategy of digital twin (DT) implementation at the cyber-physical enterprises is proposed, which is due to the need for high-quality and effective reconstruction of Ukraine. The TISM and MICMAC methods are used to study the factors that influence the strategy of DT implementation to support the guaranteed functioning of a cyber-physical system. 13 main factors, that determine strategy success, were identified, and a model of strategy realization was built in the form of a six-level interaction diagram (digraph). The factors are classified by influence and dependence. The substantiated conclusion is that the state support of DT implementation strategy, the availability of design standards, data protection methods, legislative regulation and implementation experience by other enterprises are key factors to the success of strategy for DT implementation at the enterprise.
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... Każdy proces jest definiowany za pomocą następujących atrybutów: [5] wejścia i wyjścia procesu; -zasoby procesowe; -ograniczenia procesu; właściciel; -dostawca/klient. Klient jest ważny dla analizy głównych procesów firmy. ...
TEORETYCZNE PODSTAWY ZARZĄDZANIA PROCESAMI W WARUNKACH ZMIANYTHEORETICAL FOUNDATIONS OF PROCESS MANAGEMENT IN THE CONDITIONS OF CHANGE
Article
Full-text available
  • Jun 2024
W artykule zbadano teoretyczne podstawy zarządzania procesowego w warunkach zmian oraz znaczenie wiedzy w zarządzaniu procesowym dla podmiotu gospodarczego. Trafność tego opracowania stanowi ważny element współczesnego podejścia do procesu zarządzania zmianami, wiedzą i procesami biznesowymi, kształtując nie tylko zrozumienie wszystkich procesów zachodzących w strukturze zarządzania, ale także wykorzystanie cyklu Deminga jako procedury wdrażanie systemu zarządzania i zarządzanie procesami w organizacji. Określono znaczenie stosowania podejścia procesowego, ponieważ umożliwi ono ocenę, monitorowanie procesów biznesowych, wprowadzanie zmian poprzez optymalizację lub reengineering, zarządzanie wiedzą i zmianami w organizacji, zgodność systemu rachunkowości zarządczej w procesach biznesowych obniży koszty rozwoju bazy informacyjnej, integracji różnych systemów informatycznych i ich wsparcia. Udowodniono, że ważnym aspektem zmiany jest teoria zmiany Lippitta, która uwzględnia świadomość potrzeby zmiany; rozwój relacji pomiędzy systemem a agentem zmiany; identyfikacja problemów zmian; ustalenie celów zmian, planu działania dla ich osiągnięcia; wdrażanie zmian; personel postrzega zmiany jako takie; stabilizacja; przedefiniowanie relacji agenta zmiany z systemem. Uzyskane badania teoretyczne wykazały, że z punktu widzenia badań rozwoju metodologicznego zastosowanie prezentowanego przez nas narzędzia w procesach biznesowych jest ważnym elementem procesu wdrażania zmian i zarządzania wiedzą w podmiocie gospodarczym. Jednocześnie ustala się, że procesy biznesowe i formalizacja, wiedza o uwarunkowaniach zmian, skuteczne zarządzanie nimi, to podstawowa technologia współczesnego zarządzania, a opisane procesy biznesowe służą jako mapa drogowa dla zainteresowanych stron, właścicieli firm. organizacja i proces biznesowy dotyczący wdrożenia przeprojektowania procesu, jeśli to konieczne, lub na różnych etapach, jest całkiem przydatny zarówno dla interesariuszy, jak i samej organizacji. Dlatego zarządzanie zmianami i wiedzą odgrywa ważną rolę w procesach biznesowych i zarządzaniu nimi, a wybrany temat artykułu jest istotny w aspektach naukowych i stosowanych.
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... Employee resistance to change [37]. Lack of qualified employees to manage and maintain IIoT systems [38]. ...
Cost Effectiveness of the Industrial Internet of Things Adoption in the U.S. Manufacturing SMEs
Article
Full-text available
  • Apr 2024
This research paper explores the financial adoption challenges of the Industrial Internet of Things (IIoT) in industry. Previous studies have mainly concentrated on designing affordable IIoT devices, reducing operational costs, and creating conceptual frameworks to assess the financial impact of IIoT adoption. The objective of this paper is to investigate whether IIoT adoption's financial benefits outweigh the initial costs in small and medium-sized enterprises (SMEs). The data from the Industrial Assessment Centers (IAC) database were analyzed, focusing on 62 U.S. manufacturing SMEs across 10 states and 25 Standard Industrial Classifications (SICs), evaluating projected IIoT implementation costs and anticipated cost savings. Results from the analyses reveal that statistically, the difference between implementation costs and savings is significant at a 95% confidence level. Practically, this indicates that SMEs, despite facing high initial costs, can expect these investments to be counterbalanced by substantial savings. From an engineering perspective, this finding raises awareness among SMEs that, beyond overcoming financial barriers, IIoT technologies serve as a strategic enhancement to operational efficiency and competitive positioning. This study acknowledges the limitations including reliance on estimated projections and a narrow industry focus. Future research should broaden the sample and explore the lifecycle costs of IIoT.
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... The successful transition to Industry 4.0 depends on numerous variables, each posing its unique set of challenges and opportunities. Recognizing these factors can better equip industries to prepare for this transformative shift, leading to enhanced efficiency, productivity, and sustainability (Benešová et al., 2019). The insights and strategies derived from this research review can serve as valuable guidelines for enterprises aiming to navigate the transition to Industry 4.0 successfully. ...
Technological Enablers and Prospects of Project Management in Industry 4.0: A Literature Review (Habilitadores tecnológicos y perspectivas de la gestión de proyectos en la Industria 4.0: una revisión de la literatura)
Article
Full-text available
  • Jul 2023
This study provides an overview of the 'technological enablers,' which include technologies, methods, and organizational strategies, critical for transitioning to Industry 4.0. The shift is propelled by numerous challenges faced by modern companies, including global competition, economic inequality, and educational gaps, to name a few. To illuminate these complexities, we conducted a critical literature review, emphasizing perspectives related to industrial projects and 4.0 factories. This approach helped us identify notable trends within Industry 4.0 and discuss potential benefits and obstacles pointed out by various researchers in the field. This paper presents an updated summary of Industry 4.0 features, trends, and implementation factors, aiding readers to identify possible opportunities and challenges in their projects or work environments. As Industry 4.0 increasingly integrates companies with users across creative, design, and major business decisions, the need to adjust competitive strategies for gaining advantages becomes paramount. Received: 9 April 2023 / Accepted: 12 June 2023 / Published: 5 July 2023
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Using Computer Vision to Improve SME Performance
Chapter
  • Aug 2023
The versatility of customer demand in addition to globalization involves companies to focus on how to improve their global performance. Industry 4.0 concepts are used by large companies for increasing their performance. Despite the success of these concepts, SMEs are reluctant to exploit them for their digital transformation. Nevertheless, human-machine collaboration by combining sustainability aspects with these new technologies corresponds to a solution for their performance improvement. This paper focuses on the use of computer vision and mobile robots for optimizing a production manufacturing through lean manufacturing methodology exploitation. Indeed, in a production line, products or raw materials transportation corresponds to a waste. A literature review based on organizational methods, industry 4.0 and vision including artificial intelligence tools allows to find concepts that will contribute to develop the solution for SMEs. This paper proposes a sustainable methodology, supported by an intelligent tool to reduce in SMEs, this motion waste and increase the operator’s well-being at work. An illustration based on an electronics card SME is presented to validate the concepts and tool that have been elaborated.KeywordsPerformance optimizationComputer VisionArtificial intelligenceRoboticsLean manufacturing
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Implications of Cyber-Physical Production Systems on Integrated Process Planning and Scheduling
Article
Full-text available
  • Jan 2019
Cyber-physical Production Systems (CPPS) have become popular in the context of Industry 4.0. CPPS are related to interlink the entities of the production system (e.g. machines) as well as to decentralized production control. Decentralized production control means that the work pieces schedule themselves and determine their own production process in the production system. Thus, different production processes can even process two identical parts. The concept of decentralization is discussed frequently in research. However, decentralized production control has implications on process planning. Process planning is conducted before scheduling to define the production process, the machine tool, as well as the tools. Hence, process planning determines the degree of freedom for the subsequent scheduling. Until now, the discussion of Industry 4.0 focused mostly on scheduling. However, to make use of the full potential of Industry 4.0 and CPPS, this contribution investigates their implications on process planning, as this step is necessary for scheduling. Hereto, first the concept of Industry 4.0 is analyzed and the resulting changes in CPPS. After a general analysis, it is investigated which of the resulting changes impact process planning. This investigation is necessary as process planning is essential for a decentralized production control. Based on this, it is investigated how these changes can be used for a methodology for integrated process planning and scheduling.
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Industry 4.0 technologies: Implementation patterns in manufacturing companies
Article
Full-text available
  • Jan 2019
  • INT J PROD ECON
Industry 4.0 has been considered a new industrial stage in which several emerging technologies are converging to provide digital solutions. However, there is a lack of understanding of how companies implement these technologies. Thus, we aim to understand the adoption patterns of Industry 4.0 technologies in manufacturing firms. We propose a conceptual framework for these technologies, which we divided into front-end and base technologies. Front-end technologies consider four dimensions: Smart Manufacturing, Smart Products, Smart Supply Chain and Smart Working, while base technologies consider four elements: internet of things, cloud services, big data and analytics. We performed a survey in 92 manufacturing companies to study the implementation of these technologies. Our findings show that Industry 4.0 is related to a systemic adoption of the front-end technologies, in which Smart Manufacturing plays a central role. Our results also show that the implementation of the base technologies is challenging companies, since big data and analytics are still low implemented in the sample studied. We propose a structure of Industry 4.0 technology layers and we show levels of adoption of these technologies and their implication for manufacturing companies. Keywords: Industry 4.0; smart manufacturing; digital transformation; manufacturing companies.
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Knowledge Management – The Foundation for a Successful Business Process Management
Article
Full-text available
  • Jan 2018
In today’s dynamic business environment organizations need up-to-date knowledge to execute their business in the best way. Through the ongoing digitalization and linked communities, companies and businesses, changing parameters as well as varying business framework conditions must be analyzed fast as possible to optimize the processes and gain the best direction for the own company. Therefore, organization use Business Process Management (BPM) to model and manage the existing processes. To perform BPM and optimize processes, data and information there have to be developed a knowledge inventory regarding all processes in order to model the organizational processes together with the required resources. In the following paper, this necessary knowledge process will be analyzed as foundation to perform BPM regarding the known BPM Trends. In addition, the relevance of digitalization will be matched to the Knowledge Management and BPM approach to underline the importance of the correct data and information as BPM infrastructure. Therefore, the mandatory terms will be defined and described theoretically. This literature research will be the starting basis for the approach and following scientific research. To evaluate the KM-BPM Model a survey with different companies has been developed. The research results will contributed to a combined Knowledge and BPM approach for the application in the business in times of digitalization.
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Requirements for Education and Qualification of People in Industry 4.0
Article
Full-text available
  • Dec 2017
The vision of Industry 4.0 will be bring not only new approaches but also the methodologies and technologies, which will have to be introduced into companies. The transition to such a sophisticated production will not be possible immediately. The main reasons are high financial costs and the lack of qualified employees. This article deals with identification of job roles in the companies.
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Interdependencies of Industrie 4.0 & Lean Production Systems: A Use Cases Analysis
Article
Full-text available
  • Dec 2017
Lean has become a widely spread approach to gain high efficient processes in enterprises. Nowadays, Industrie 4.0 is one of the most promising approach to cope future challenges in the production environment. It is shown, that a process orientated organization and thus, Lean Production Systems might be an enabler towards a successful and sustainable implementation of Industrie 4.0 in the production environment. To enable a detailed analysis of interdependencies between Lean Production Systems (LPS) and Industrie 4.0, several Industrie 4.0 elements have been structured into technologies, systems and process related characteristics, based on 260 use cases of applied Industrie 4.0 technologies in the German industry. Afterwards, the use cases have been analyzed regarding interdependencies between Industrie 4.0 and principles of Lean Production Systems.
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Determination of Hazard in Captive Hotel Laundry Using Semi Quantitative Risk Assessment Matrix
Article
Full-text available
  • Jun 2016
This study is to identify the critical risk in cleaning process at the laundry area by using Semi-Quantitative Risk Assessment Matrix. In the identification of hazard, observation at the workplace, semi-structure interviewed with four workers who are an expert in the respective area and reviewed for publication report from authorities’ bodies used as an input in risk assessment. The possibility of fingers caught into the flatwork iron (risk value: 9) has been identified the most critical risk. The appropriate risk control was isolation, engineering controls, administrative controls and personal protective equipment. This study may help employer be more proactive in ensuring the safety and health of workers.
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Verification of the Relationships between the Elements of an Integrated Model of Competitiveness of the Company
Article
Full-text available
  • Oct 2015
The process of globalization of the economy that has been made in recent years, requires a new perspective on the development of enterprises and a more professional management of these entities. Therefore, the concept of competitiveness has become particularly important for companies and at the same time also quite popular among entrepreneurs, managers and business organizations. The paper presents a variety of approaches to the competitiveness of enterprises in the current state of art, an integrated model of competitiveness of the company, verification the relationships between the elements of an integrated model of competitiveness of the company using a sample of 992 enterprise from Poland, Czech Republic and Slovakia.
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On the Role of Context in Business Process Management
Article
Full-text available
  • Oct 2015
  • INT J INFORM MANAGE
Business Process Management (BPM) has proven successful to help organizations improve and innovate, and its application has grown in scope and context. One essential problem related to this development is that the BPM body of knowledge does not account for a broader variety of business contexts. On the contrary, most approaches, methods, or models in BPM suggest one way forward, and we recognize that BPM projects following a one‐size‐fits‐all approach are prone to fail, since they do not sufficiently consider situational requirements. In this viewpoint article, we argue that BPM needs to be contextual in order for projects to be most efficient and effective. We observe a lack of research on how to identify and characterize business contexts relevant for tailoring the right BPM approach. Therefore, we examine contextual factors that influence BPM and propose a framework to identify the context in which BPM is applied. We define context in BPM as situational factors related to goal‐, process‐, organization‐, and environment‐dimensions. Our viewpoint article not only creates awareness for contextual BPM, it also intends to stimulate research on the role of context in BPM and to help practitioners better understand the specific business context in which BPM initiatives are applied.
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Smart Factory Systems
Article
Full-text available
  • May 2015
In recent years, German and US governments have established separate initiatives to accelerate the use of the Internet of Things (IoT) and smart analytics technologies in the manufacturing industries and, consequently, to improve the overall performance, quality, and controllability of manufacturing process. The smart factory is the integration of all recent IoT technological advances in computer networks, data integration, and analytics to bring transparency to all manufacturing factories. In this article, we review the most recent logistic decisions for taking smart factories from idea to reality and then describe the possible technologies for smart factories.
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