Industry 4.0 – Preparing for the Future

This study discusses the benefits, challenges and emerging trends of Industry 4.0. It establishes a linkage between theories and practices of operations management. The study also identifies how Industry 4.0 will influence the design of service and manufacturing systems.

Running head: INDUSTRY 4.0
Operational Management and Business Analytics
[Industry 4.0 – Preparing for the Future]
Name of the student:
Name of the university:
Author note:

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Executive summary
The main purpose of this study is to discuss the fourth industrial revolution which is receiving a
significant attention from academic scholars. The study finds that academic scholars are very
passionate about the Industry 4.0. They have already predicted a long list of benefits from it. The
paper also discusses the relevant emerging trends. On the other side, the study also establishes a
linkage between theories and the practices. Arguments of operations management in the context
of theoretical & practical elements have also been discussed.

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Table of Contents
1. Introduction..................................................................................................................................3
2. Discussion....................................................................................................................................3
2.1 Contribution of 4th industrial revolution................................................................................3
2.2 An overview of Industry 4.0..................................................................................................5
2.3 The need for Industry 4.0.......................................................................................................6
2.4 An overview of what Industry 4.0 can do..............................................................................7
3. Case study: Siemens....................................................................................................................8
3.1 The application of Industry 4.0..............................................................................................8
3.2 Lessons learnt from the case study........................................................................................8
4. Conclusion...................................................................................................................................9
References......................................................................................................................................11

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1. Introduction
Industry 4.0 is a topic of elevated enthusiasm and interest. This is not just due to benefits
it imparts to the manufacturing industry but also for complexities involved in big data. The
manufacturing industry that has faced challenges in the form of meeting the mass production and
efficiency can logically move into a much better place with Industry 4.0 (Saarikko, Westergren
& Blomquist, 2017). Industry 4.0 provides a platform where a huge amount of data can be
accessed to; however, the challenge lies in to reap the benefits from it. The supply chain
operation and the manufacturing process can be made technologically advanced by connecting
each and everything over a single platform IoT. The industry 4.0 can bring the revolution by
providing the insights into market trends, maintenance cycles, targeted business decisions and
the customer buying patterns. Firms can also be able to lower down the cost of production
(Wang et al., 2016). However, all such benefits need to control and mitigate the probable
challenges with Big Data, the Internet of Things (IoT) environment and the revolutionary
Industry 4.0. The possible challenges will include an appropriate utilization of a large data, the
complexity of IoT, complexities of the interconnected environment and the increasingly growing
needs for partnering business to produce the innovative solutions (Waller & Fawcett, 2013).
The purpose of this study is to identify how the Industry 4.0 will influence the design of
service and manufacturing systems.
2. Discussion
The one of the questions in concern is “Q3: How does Industry 4.0 implementation
influence the design of manufacturing and service systems, and the workplace?”
2.1 Contribution of 4th industrial revolution
An appropriate utilization of a large-scale data is a potential challenge and barrier to the
success of Industry 4.0. Few factories in the world have now become the smart factories by
implementing the Industry 4.0; however, there are still very fewer clues on how to handle such a
large scale data. The complexity as according to Zhong et al. (2016) is resolvable. The authors
Zhong et al. (2016) claim that one such technology which can help to appropriately utilize the
data has been developed by Apache Hadoop. Hadoop is an open-source software framework

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which is being written in Java to process the large-scale data sets. The framework is being made
in such a way that it contains the several modules such as Hadoop Common, Hadoop YARN,
Hadoop MapReduce and the Hadoop distributed file system (HDFS). The modules have been
designed in such a way that it automatically detects the hardware failures which generally
occurs.
A number of significant advantages have been claimed by Hadoop based on few of its
successful applications in few of the most giant companies. Yahoo and Facebook are amongst
the few successful companies. In fact, a close to half of the Fortune 50 Big Data processing firms
has adopted the Hadoop software (Zhong et al., 2016). MapReduce is one of the essential
elements in Hadoop which is a programming paradigm. It enables the processing of data. Models
and algorithms play the critical roles in MapReduce by enabling to handle the Big Data. Few
such examples include scientific applications, web analytics applications and social networks.
Industry heavyweights such as IBM, Microsoft and Oracle are now supporting the Hadoop.
IBM SPSS Modeler is another extensive analytic platform which is capable to provide
the forecastable intelligence. The forecastable intelligence can assist in groups, individuals,
enterprise and systems to make decisions. The right and appropriate decisions can be made by
using a set of advanced algorithms, decision optimization & management and entity analytics.
The model is also being tested for its usefulness for both software and hardware perspectives in
regards to its embedded techniques on Big Data analytics. One of the chief goals is to use the
correct data for the appropriate customer during when it is needed the most (Zhong et al., 2016).
The “3C framework” which IoT currently follows does speak about both of opportunities
and the challenges for the worldwide industries. The three stages of the framework are namely as
‘Context’, ‘Configuration’ and ‘Capability’. The first stage which is the ‘Context’ speaks about
the current supply chain and the related facts like the driving forces, barriers and the key
missions. The second stage is ‘Configuration’ which describes the configuration pattern of the
supply chain that also includes the process structure, role structure and information architecture.
The third phase is the ‘Capability’ which discusses the key success factors of the existing supply
networks (Rong et al., 2015). However, the framework does not discuss a few important factors
and certainly leaves spaces for further improvements. An increasingly growing popularity of IoT
in the context of the supply chain network has certain gaps. ‘Cooperation’ is one of those gaps

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where co-evolving bodies fail to reach the common strategic objectives. Unless and until the
system, people and process are not controlled by effective cooperation, the challenges will
sustain. ‘Construct’ is another gap in the IoT affected supply network. Due to an incompetent
infrastructure, this will be challenging to appropriately utilize such a large scale data. The one
improvement which is required in the existing model for the supply network is in regards to the
change or co-evolution of the partnering businesses. The business ecosystem must be evolved or
changed, so that, the complexities of IoT influenced business environment can be reduced.
2.2 An overview of Industry 4.0
An industry 4.0 is a digitalised platform which supports a few of the following trends:
The digitalization of the manufacturing process: Cyber-Physical Systems (CPS), 3-D
Printing, Robots etc. are few of the emerging trends in recent times. CPS is expected to
positively impact the manufacturing and service sector. 3-D Printing is expected to elevate the
speed of manufacturing of products and reduce the total production cost. Robots are expected to
speed up the process such as picking of orders and packing of products. Operations, especially in
warehouses, will be faster and the operational cost will also be impacted (Longo, Nicoletti &
Padovano, 2017).
E-operations: E-operation is booming for various reasons as listed below (Wang, Törngren &
Onori, 2015):
 Increased investments for the warehouse management
 Big Data
 The efficiency of the supply chain in terms of speed is increasing
 Revolution is happening in the Business Processes
 Use of a fully integrated EDI to increase the efficiency is increasing
Outsourcing: Outsourcing will be a part of the future of manufacturing. Outsourcing is
happening due to following several reasons (Marques et al., 2017):
 Data Security
 Cloud Computing
 The digitalization of traditional communication

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 Use of Artificial Intelligence through robots
 Growth in freelancing works
The manufacturing industry is one of the industries which will have a larger impact on
Industry 4.0. As argued by Lee, Kao & Yang (2014), there is still a very minimal research on the
skills required to support the fourth industrial revolution. The majority of studies have been over
the manufacturing technologies only. Those studies have not covered the role of managers in
identifying and implementing the Industry 4.0 advancements. None of those studies were
focused upon the knowledge management which is one of the core requirements for the fourth
industrial revolution. Additionally, it also does not cover the service sector which is an
indispensable part of value chains. Nonetheless, value chains do also face several issues such as
smart working, mass customization, smart service, digitalization and others. The primary focus
of all those studies has always been in the manufacturing sector. Hence, there is a need for
appropriate management practices in regards to evaluating their tactics for innovative capability
and knowledge management.
As opined by Preuveneers & Ilie-Zudor (2017), the feasibility of Industry 4.0 will depend
a lot on managerial decisions to mitigate the expected challenges. One of those challenges will
be predicting and identifying the reasons for failure. Since, Industry 4.0 is all about operating in
a network. Hence, this will be challenging to remain cost-effective and mitigating the unknown
risks due to external influences. There have been very limited studies on finding the negative
consequences of improper decisions and how all that can affect the cost of automation. Industry
4.0 will also be tested for effectively identifying the areas of feasibility for the automation. Since,
Industry 4.0 will generate large-scale data from customers, systems and other assets. Hence,
feasibility will also be checked for whether the transition to Industry 4.0 is ethical or not. A
purposeful manipulation of the data will need to answer the purpose behind such trade-offs.
2.3 The need for Industry 4.0
The Industry 4.0 is a potential move for the global manufacturers and service sectors
provided that its limitations are effectively controlled. The fact is justifiable as according to
Suthar & Singh (2008), Industry 4.0 can potentially raise the efficiency of manufacturing and
service sectors. Industry 4.0 is a fact which will revolutionize the manufacturing and service

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sectors in the future. The impact is already happening especially in developed countries.
Germany and Japan has been the effective implementer of Industry 4.0. The use of robotics is
increasing in Japan and Germany. Robots are occupying the places in warehouses. The United
Kingdom also has potentials to respond to the fourth industrial revolution. Emerging nations
such as China and India can also be benefitted from the service automation.
However, its associated limitations must also be taken into account to actually make this
happen. Effective communication between the shared businesses is important which will require
an appropriate maintenance of the shared network. Moreover, the shared objectives in this
Industry 4.0 environment must be fulfilled by networked businesses to yield the anticipated
benefits. As opined by Mendes & Neto (2015), the use of industrial robots in Germany and Japan
just tells the changing future of the manufacturing sector in the next few years. Germany, in
particular, will expectedly have the higher benefits and will also be able to re-establish its hardly
affected manufacturing sector.
2.4 An overview of what Industry 4.0 can do
As argued by Hofmann & Rüsch (2017), there is no fundamental definition of Industry
4.0. It is just the logic being used by firms to enhance their operational competencies. The
digitalization is a shift of manufacturing process towards a gradually more decentralized and
self-regulated approach for value creation. Technologies such as IoT, additive manufacturing,
smart factories and CPS have encouraged a progression towards the changed manufacturing
process. The transition of manufacturing logic is to meet the future requirements of mass
production. In the context of the supply chain, demand assessment can be made. A shortened
cycle time is also expected. The list of potential benefits also includes a highly integrated &
transparent supply chain and improvements in the production planning. The major implication in
supply chain operation is expected to be much on the logistics operations. This is also expected
to affect the real-time information flows, improvements in flexibility and along the length supply
network transparency. This can prove to be effective in enabling companies to optimize value-
creation. According to the authors, the potentials of Industry 4.0 can be judged only by analyzing
its efficiency in different circumstances. This is due to the complexities involved in logistics
operation.

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According to Brettel et al., (2014), developed countries such as Germany have struggled
to reduce the gap between the product quality and its pricing. Hence, German manufacturers are
significantly moving to other regions to reduce the identified gaps. Manufacturers especially
those belonging to the automotive, plant and machine industry have struggled to offer the quality
products at competitive pricing. According to the authors, manufacturers can become cost-
effective by intelligently identifying the areas for automation and the labor. Labor participation is
still going to be irreplaceable in many areas. However, Industry 4.0 can help to create a business
environment where raw materials and the other physical activities can be connected with IoT.
This will result in an improved communication and will also generate the real-time information.
However, a cooperative work will still remain a must do thing considering a fact that it is
challenging to appropriately and entirely utilize such a large-scale data.
3. Case study: Siemens
3.1 The application of Industry 4.0
Siemens is one of few companies to have involved largely in automating the service
system. Products such as TIA Portal v14 for a much improved cloud based data management,
MindSphere, machine tools, robots and establishment of digital plants are evidence of its fourth
industrial revolution. Siemens is not just supplying those digitalised products but also setting an
example of a manufacturer deploying digitalised technologies to improve its operations. For
example, Siemens at its PLC manufacturing plant located in Amberg, Germany has automated its
automation systems. The impact is huge as production quality rate has improved by a close to
100%. As mentioned by Siemens, 75% of production of PLC at Amberg is put to automation.
People do also play the critical roles. Siemens has effectively identified the areas which were
feasible to the automation (Automationworld.com, 2018). Siemens is supporting a fact that
digitalisation is realistic. Even the SMEs can also implement it.
3.2 Lessons learnt from the case study
The example of automation in Siemens gives a number of lessons to the global
manufacturers. There are a few theories which encourage a progressive trend for automation are
as follows:

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The Open System Approach: It says that organizations must adapt to complex changes to
remain competitive in the long-term (Smith, Maull & CL Ng, 2014). It validates the same fact as
generated by Siemens. Siemens not just as a supplier is digitalising the products but is also
making changes to its manufacturing facilities in one of its plant in Germany. The production of
PLC is being supported largely through automation whereas there are few people still playing the
critical roles.
Industry 4.0 is a set of complex things which needs to be effectively maintained by shared
businesses. Hence, if adaptability is there, productivity from Industry 4.0 will definitely prevail.
The Chaos Theory: The theory also advocates the importance of adaptability with complex
changes and institutionalization of learning through effective feedback systems (Pryor & Bright,
2014). Consequently, organizations and government must work on to develop the knowledge
base to effectively maintain the Industry 4.0 environment. Siemens has effectively understood
the automation and the related requirements. This is why it has not put the entire production to
automation rather a larger portion.
Contingency Theory: The theory says that traditional management approaches are no longer
sufficient and must be replaced with digitalisation to take the advantage of the fourth industrial
revolution. However, it suggests that different situations are unique from each other and require a
different approach every time from contemporary managers (Kim et al., 2015). It is, therefore,
indicating the fact being adopted and implemented by Siemens. Siemens had effectively
understood the areas to put into automation. It has been able to manage a balance between
automation and traditional systems.
4. Conclusion
In summary, it can be concluded that Industry 4.0 is at the moment a logical advancement
in technologies to enhance the efficiency of manufacturing and service operations. The changes
in the form of technological advancement are there to sustain. Developed countries are expected
to have the maximum benefits. Germany and Japan have both shown the successful
implementation. Manufacturing industries in Germany are expected to feel the revolution.
However, the SMEs need to learn the ways to take advantage of the Industry 4.0 environment.
There is a need to work upon to enrich the knowledge as learning will only resolve the possible

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barriers of Industry 4.0. However, the advancement is full of complexities which are the barriers
to a flourishing adoption of Industry 4.0 in a larger area. Despite having the supports of
government & organizations, there is still a long way to go to say anything about the future
prospect Industry 4.0. There are the areas which need to be productive in order to support the
fourth industrial revolution. Those areas include protection from security-related threats,
standardization of communication interfaces, work management, the SMEs and accessibility to
cognitive ability. A smooth business communication between the shared businesses is also its
limitation. The anticipated benefits can be attained if management practices are aligned with
relevant theories and the frameworks recommended in this study. The SMEs, in particular, need
to learn the ways to take the advantage of the change. A learning environment will educate
managers on the ways to identify the potential advancement. This will also educate employees
on how to survive and flourish in a changed and challenging environment.

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References
Automationworld.com. (2018). Siemens Positions Itself as an Industry 4.0 Example | Automation
World. Retrieved from https://www.automationworld.com/siemens-positions-itself-
industry-40-example
Baena, F., Guarin, A., Mora, J., Sauza, J., & Retat, S. (2017). Learning factory: the path to
industry 4.0. Procedia Manufacturing, 9, 73-80.
Bauer, W., Hämmerle, M., Schlund, S., & Vocke, C. (2015). Transforming to a hyper-connected
society and economy–towards an “Industry 4.0”. Procedia Manufacturing, 3, 417-424.
Brettel, M., Friederichsen, N., Keller, M., & Rosenberg, M. (2014). How virtualization,
decentralization and network building change the manufacturing landscape: An Industry
4.0 Perspective. International Journal of Mechanical, Industrial Science and
Engineering, 8(1), 37-44.
Faller, C., & Feldmüller, D. (2015). Industry 4.0 learning factory for regional SMEs. Procedia
CIRP, 32, 88-91.
Hofmann, E., & Rüsch, M. (2017). Industry 4.0 and the current status as well as future prospects
for logistics. Computers in Industry, 89, 23-34.
Kim, M. J., Chung, N., Lee, C. K., & Preis, M. W. (2015). Motivations and use context in mobile
tourism shopping: Applying contingency and task–technology fit theories. International
Journal of Tourism Research, 17(1), 13-24.
Lee, J., Ardakani, H. D., Yang, S., & Bagheri, B. (2015). Industrial big data analytics and cyber-
physical systems for future maintenance & service innovation. Procedia CIRP, 38, 3-7.
Lee, J., Kao, H. A., & Yang, S. (2014). Service innovation and smart analytics for industry 4.0
and big data environment. Procedia Cirp, 16, 3-8.
Longo, F., Nicoletti, L., & Padovano, A. (2017). Smart operators in industry 4.0: A human-
centered approach to enhance operators’ capabilities and competencies within the new
smart factory context. Computers & industrial engineering, 113, 144-159.

12INDUSTRY 4.0
Marques, M., Agostinho, C., Zacharewicz, G., & Jardim-Gonçalves, R. (2017). Decentralized
decision support for intelligent manufacturing in Industry 4.0. Journal of Ambient
Intelligence and Smart Environments, 9(3), 299-313.
Mendes, N., & Neto, P. (2015). Indirect adaptive fuzzy control for industrial robots: a solution
for contact applications. Expert Systems with Applications, 42(22), 8929-8935.
Preuveneers, D., & Ilie-Zudor, E. (2017). The intelligent industry of the future: A survey on
emerging trends, research challenges and opportunities in Industry 4.0. Journal of
Ambient Intelligence and Smart Environments, 9(3), 287-298.
Pryor, R. G., & Bright, J. E. (2014). The Chaos Theory of Careers (CTC): Ten years on and only
just begun. Australian Journal of Career Development, 23(1), 4-12.
Rong, K., Hu, G., Lin, Y., Shi, Y., & Guo, L. (2015). Understanding business ecosystem using a
6C framework in Internet-of-Things-based sectors. International Journal of Production
Economics, 159, 41-55.
Saarikko, T., Westergren, U. H., & Blomquist, T. (2017). The Internet of Things: Are you ready
for what’s coming?. Business Horizons, 60(5), 667-676.
Schmidt, M., Merklein, M., Bourell, D., Dimitrov, D., Hausotte, T., Wegener, K., ... & Levy, G.
N. (2017). Laser based additive manufacturing in industry and academia. CIRP
Annals, 66(2), 561-583.
Schuh, G., Gartzen, T., Rodenhauser, T., & Marks, A. (2015). Promoting work-based learning
through industry 4.0. Procedia CIRP, 32, 82-87.
Shamim, S., Cang, S., Yu, H., & Li, Y. (2017). Examining the feasibilities of industry 4.0 for the
hospitality sector with the lens of management practice. Energies, 10(4), 499.
Smith, L., Maull, R., & CL Ng, I. (2014). Servitization and operations management: a service
dominant-logic approach. International Journal of Operations & Production
Management, 34(2), 242-269.

13INDUSTRY 4.0
Suthar, S., & Singh, S. (2008). Feasibility of vermicomposting in biostabilization of sludge from
a distillery industry. Science of the total environment, 394(2-3), 237-243.
Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a
revolution that will transform supply chain design and management. Journal of Business
Logistics, 34(2), 77-84.
Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016). Big data analytics in
logistics and supply chain management: Certain investigations for research and
applications. International Journal of Production Economics, 176, 98-110.
Wang, L., Törngren, M., & Onori, M. (2015). Current status and advancement of cyber-physical
systems in manufacturing. Journal of Manufacturing Systems, 37, 517-527.
Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: state of the art and future
trends. International Journal of Production Research, 56(8), 2941-2962.
Zhong, R. Y., Newman, S. T., Huang, G. Q., & Lan, S. (2016). Big Data for supply chain
management in the service and manufacturing sectors: Challenges, opportunities, and
future perspectives. Computers & Industrial Engineering, 101, 572-591.
Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent manufacturing in the
context of industry 4.0: a review. Engineering, 3(5), 616-630.