Smart Manufacturing & Manufacturing as a Service

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INFORMATION TECHNOLOGIES IN LOGISTICS
SMART MANUFACTURING
Claudia Gomez
Kamila Hurkova
Merve Nur Taş
Tommi Veromaa
Academic Master Study Programme
“Logistics and Supply Chain Management”
Supervisor of the Study Work: Assoc. prof. Andrejs Romānovs, Dr.sc.ing.
Riga, May 2017

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Contents
List of Figures ............................................................................................................................ iii
List of Abbreviations................................................................................................................... iii
1 Introduction......................................................................................................................... 1
2 Basic IT Technologies......................................................................................................... 3
2.1 Programmable Logic Controller (PLC) ......................................................................... 3
2.2 Intelligent Sensors ....................................................................................................... 3
2.3 Radio Frequency Identification (RFID) ......................................................................... 3
2.4 Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) ................ 4
2.5 Enterprise Resource Planning (ERP)........................................................................... 4
2.6 Manufacturing Execution System (MES)...................................................................... 4
3 Internet of Things................................................................................................................ 5
3.1 Internet of Things in Smart Manufacturing ................................................................... 5
3.2 Scientific annotations................................................................................................... 6
3.3 Industry example ......................................................................................................... 7
3.4 Video ........................................................................................................................... 7
4 Cloud Computing ................................................................................................................ 7
4.1 Description and scientific annotations .......................................................................... 7
4.2 Graphic illustration ......................................................................................................10
4.3 Video & Industry examples .........................................................................................10
5 Advanced data analytics ....................................................................................................11
5.1 Advanced data analytics in Manufacturing ..................................................................11
5.2 Scientific annotations..................................................................................................12
5.3 Industry example ........................................................................................................12
5.4 Graphic illustration ......................................................................................................13
5.5 Video ..........................................................................................................................13
6 Conclusions .......................................................................................................................14
7 References ........................................................................................................................15

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List of Figures
Figure 1:Smart manufacturing – Manufacturing as a Service structure ...................................... 2
Figure 2: Programmable Logic Controller................................................................................... 3
Figure 3. Framework ERP - MES............................................................................................... 5
Figure 4: Cloud based data handling – layers ...........................................................................10
Figure 5: Advanced Data Analytics Technologies .....................................................................13
List of Abbreviations
BI Business Intelligence
CAD Computer Aided Design
CAM Computer Aided Manufacturing
CMfg Cloud Manufacturing
ERP Enterprise Resource Planning
GUI Graphical User Interfaces
IaaS Infrastructure as a Service
IoT Internet of Things
MaaS Manufacturing as a Service
MES Manufacturing Execution System
NIST The National Institute of Standards and Technology
PaaS Platform as a Service
PLM Product Lifecycle Management
SaaS Software as a Service

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1 Introduction
The industry is going through its fourth industrial revolution. The first one occurred in 18th century and is
characterized by introducing water and steam power into production. The second one started using
electrical power and aimed for mass production, near the start of 20th century. The third one spread
electronics, robotics and information technologies to automatize production. Since the middle of the 20th
century, the fourth industrial revolution is ongoing which brings a new digital era full of innovations.
(Schwab, 2016)
The industry 4.0 is about big data, large storage capacities, connectivity, computational power, advanced
analytics, artificial intelligence, augmented reality, advanced robotics, decentralized decision making,
access to knowledge and much more. (Baur & Wee, 2015) This evolution has both clear advantages and
some challenges to be confronted (Marr, 2016): among the drawbacks, there is a risk of data security,
required high degree of reliability and stability for cyber-physical communication, possible large impacts on
production in case of technical problems, high costs of investments, and fear of losing the need for human
manpower. Nevertheless, the benefits are greater and include improvement of health & safety working
conditions, higher level of control, more efficient production, quicker responses to customer needs and
consequently higher service levels etc.
All these aspects directly influence the entire supply chain; markets are becoming more global,
transportation and communication costs are continually decreasing, all operations are becoming more
effective, products are gaining on quality thanks to increasing shared expertise and customers are more
satisfied. “A key trend is the development of technology-enabled platforms that combine both demand and
supply to disrupt existing industry structures [...]”. (Schwab, 2016). Consequently, all parts of logistics are
impacted as well: purchasing, manufacturing, distribution, inventory and warehouse management, and
transportation fields benefit from all the innovations. Smart manufacturing will be in focus as a subject of
this report.
The US National Institute of Standards and Technology (NIST) defines Smart Manufacturing as systems
that are “fully-integrated, collaborative manufacturing systems that respond in real time to meet changing
demands and conditions in the factory, in the supply network, and in customer needs.”.
(ManufacturingTomorrow, 2017) The component of meeting customer needs in real time requires special
attention. Managing customers’ needs is a challenging but necessary task of any organization. Meeting
their requirements is one of the main criteria of all quality theories and is also the main objective of
successful supply chain management; and smart manufacturing is a big step ahead how to achieve
customer satisfaction while doing all operations efficiently.
Manufacturing as a service (MaaS) offers customers high level of customization when purchasing
different types of products. “The transition from mass production to personalized, customer-oriented and
eco-efficient manufacturing is a promising approach to improve and secure the future competitiveness of
[...] manufacturing industries [...]. One precondition for this transition is the availability of agile IT systems,

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capable of supporting this level of flexibility on the production network layer, as well as on the factory and
process levels.” (Meier, Seidelmann, & Mezgar, n.d.) This production based on demand has many
advantages for companies such as no need for large inventories and consequently lower costs or better
satisfaction of customers’ needs which leads to higher competitiveness on the global market.
The process required for MaaS could be divided into three main stages: customer interaction, information
processing and finally the manufacturing process. This is visualized in Figure 1.
Figure 1:Smart manufacturing – Manufacturing as a Service structure
Firstly, the company receives real-time information about customer’s requirements and can respond to this
demand with specific product proposal. The technologies used are internet of things, all smart devices incl.
smart phones and cloud enabling the interaction with customers. Then, all received information is saved,
processed and analyzed using cloud based data handling, advanced data analytics and/or Enterprise
Resource Planning (ERP) and Manufacturing Execution System (MES) software. In the final stage, the
manufacturing process itself is facilitated using many innovative technologies such as programmable logic
converter, Computer Aid Design and Manufacturing (CAD/CAM), Intelligent Sensors and RFID, Wireless
sensor network, cyber-physical systems, etc. The information from these operations is also collected and
analyzed using advanced data analytics.
The main components of this process, Internet of Things, Cloud Computing and Advanced Data Analytics,
will be examined in detail in the following chapters.

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2 Basic IT Technologies
There are basic components and technologies essential to develop a smart manufacturing process. They
will not be the object of this report; however, they are necessary to understand the functioning of a Smart
facility.
2.1 Programmable Logic Controller (PLC)
The programmable logic controller is a microprocessor-based control computer that is connected to devices
including switches, small motors, relay, etc. and it allows to control machines and processes during
manufacturing. Overall, the PLC can perform a variety of tasks including: relay-switching tasks, conduct
counting and calculations regarding process values. (Salah)
Figure 2: Programmable Logic Controller
2.2 Intelligent Sensors
Intelligent or Smart sensors allow machines to connect to intelligent networks along the entire value chain.
The trend is to miniaturize and increase processing power of these sensors, embedding a wide variety of
functions that increase responsiveness and efficiency of the processes. Intelligent sensors increase
machines’ productivity by taking on specific functionality and logical loops that before required a PLC.
Moreover, this technology makes available the recording, safety, traceability, quality control, predict
equipment failure and trigger maintenance protocols. (Panoramic Power, 2016) & (Hannaby, 2016)
2.3 Radio Frequency Identification (RFID)
RFID is an automatic identification technology that could be implemented for a variety of objects. It allows
tracking and tagging items and record information (with no need for direct contact with the object). In case
of manufacturing as a service, RFID guarantees that the customer order is executed correctly and receives
the product demanded.

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2.4 Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM)
Computer Aided Design
CAD is the use of computer systems (or workstations) to aid in the creation, modification, analysis, or
optimization of a design (Narayan & Lalit, 2008). CAD software replaces manual drafting with an automated
process.
Computer Aided Manufacturing
CAM can be defined as the use of computer systems to plan, manage and control the operations of a
manufacturing plant through either direct or indirect computer interface with the plant’s production
resources. (Groover & Zimmers, 1983)
CAD and CAM are linked together for streamlined manufacturing and highly automated machinery use.
This technology has been used in production for years and lead to great success, but its major benefits to
the MaaS / CMfg industry can be summarized as:
✓ In a manufacturing-as-a-service environment, the CAD and CAM, allows improve the efficiency of
the process of designing, managing, testing and simulation customized designs.
✓ Detects and eliminate mistakes, reduce scraps and decrease related costs
✓ Increase productivity CAD/CAM software it makes it easier to synthase, analyze, and document
the design. CAD data can easily be documented, stored, standardized. CAD software allow the
designer to create a database for future use or reference. Thus, control and implementation of
engineering is significantly improved by computer-aided design (Groover & Zimmers, 1983). This
feature of CAD helps Manufacturing-as-a-Service Concept to be flexible.
2.5 Enterprise Resource Planning (ERP)
ERP is business management software that integrates the flow of information between business functions.
In manufacturing as a service environment, an ERP alone is not adequate, since it is based mainly on the
strategic decision making level, it is not suitable for real-time shop-floor data and processing. (Zhong, Dai,
Qu, Hu, & Huang, 2013)
2.6 Manufacturing Execution System (MES)
MES is software application used to manage shop-floor operations including: tracking, scheduling, control,
equipment status, material delivery and overall manufacturing progress. It provides real-time data and can
be integrated with other ERP systems, Programmable Logic Controllers and advanced analytics tools to
provide a complete overview of the process. (Zhong, Dai, Qu, Hu, & Huang, 2013)

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Figure 3. Framework ERP - MES
The figure above shows the framework for ERP and MES.
3 Internet of Things
World today is rapidly developing in information technologies and allows the development of internet of
things(IoT) technology systems. IoT can be for example in households, cities, wearables, healthcare,
automotive and manufacturing plants. All of these examples include gathering and handling of data to
provide valuable information; e.g. to optimize energy consumption of a household room based on data of
whether the room is occupied by a person or not. (Meola, 2016)
3.1 Internet of Things in Smart Manufacturing
Smart manufacturing is the outcome of enabling IoT in manufacturing. IoT allows flow optimizations, real
time inventory tracking, asset tracking, employee safety, predictive maintenances and firmware updates.
(BI_Intelligence, 2016)
Key benefits for IoT in manufacturing: (Burrus, n.d.)
• Machines take corrective actions to prevent equipment failure and keep production running.
• Machines track and constantly measure or “inspect” the produced material to prevent loss of
quality.

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• Machines can phone operators beforehand if i.e batteries of the machine are experiencing decay
and will soon stop production.
• Machines work sequencing, flexible manufacturing systems automatically adjust most efficient
work sequences based on customer orders made online through cloud data handling.
o “Dialogue” between machines inside the manufacturing and packaging area.
o Computer algorithms run constantly optimizing work sequencing as orders of different
products come from customers and manufacturing settings are changed.
• Data during the smart products stay in the manufacturing area and can be easily shared using
RFID among all parties (customer, manufacturer, transporter, distributor)
• Replacement of paper-based and manual processes with simplified information mashups of
different IT’s (PLM,CAD,ERP) or other industrial tools.
Challenges of IoT in manufacturing and in general (Thingworx, 2017)
• Data security, viruses or malicious outside access to smart manufacturing systems can be very
detrimental and costly.
• Data problem: in recent years IT products or “things” created more data than humans.
➢ Lack of bandwidth.
➢ Price of bandwidth and communication is decreasing at slower pace than storage and
computing power.
3.2 Scientific annotations
“Internet of Things (IoT) is an integrated part of Future Internet including existing and evolving Internet and
network developments and could be conceptually defined as a dynamic global network infrastructure with
self-configuring capabilities based on standard and interoperable communication protocols where physical
and virtual “things” have identities, physical attributes, and virtual personalities, use intelligent interfaces,
and are seamlessly integrated into the information network.” (Friess & Vermsan, 2011, p. 16)
“At the heart of the Internet of Things is the Industrial Internet. It provides the underlying infrastructure that
supports connected machines and data. The term, which is generally attributed to manufacturing giant
General Electric(GE) refers to the integration of machines with sensors, software, and communications
systems that enable the Internet of Things. The industrial Internet pulls together technology and processes
from fields such as big data, machine learning, and M2M connectivity.” (Greengard, 2015, p. 51)
“As a result, the envisioned ideas can be realized so that benefit for several members of the product lifecycle
chain is possible. However, the large amount of different data formats and types and the challenges to
create a semantic super-structure for cross-domain machine-to-machine communication thwarts the
current dissemination of such technologies. In this research field more work has to be done (and is currently

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done) by research and industry partners to create new and to migrate existing applications to become
Industry 4.0 aware.” (Jeschke, Song, Brecher, & Rawat, 2017, p. 226)
3.3 Industry example
In oil and gas industry, the IoT is used to optimize oilfield productions with having sensors in each oil well
that tracks different activities; this data from sensors is then distributed to a server that will create an
analysis of all oil wells in the area. This allows identifying opportunities for improvement of production such
as increasing production level. The scale of the improvement can be put into perspective when the company
says it can lose up to 500$ each hour if an oil well is not in operation. The company says that after
implementing IoT with sensors installed in oil wells, they can save up to 145,000$ every month in repair
expenditures. (Bolen, 2015)
Technical overview:
Primary data source: Sensors on oil injector wells
Number of Monitored Devices: 21,000
Primary Activities Monitored: Oil extraction temperatures, rates and well pressure (10 activities in total)
Frequency of Readings: 90 x day x activity
Scale of Data Collected: ~18,900,000 daily readings
3.4 Video
The video chosen for this technology presents how component manufacturer Intel utilizes IoT in their
production. Basically, everything is connected with sensors and RFID’s to work in harmony. This is created
by data centers that make data analysis of everything in the factory and distributing the opportunities found
in the data analysis to customers and machines that create products. (Intel, 2014)
URL: https://www.youtube.com/watch?v=5OQQZ9eWF-4
4 Cloud Computing
4.1 Description and scientific annotations
The National Institute of Standards and Technology (NIST) defined cloud computing as ‘‘a model for
enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing
resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and
released with minimal management effort or service provider interaction.’’ (Mell & Grance, 2009) In a Cloud

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Computing environment, decentralized consumers are provided by flexible and measurable service from
the resource pool.
Cloud Computing includes both the software service delivered to the user and the systems and hardware
that are able to provide the service in need. The former is defined as Software as a Service (SaaS) and the
later as IaaS (Infrastructure as a Service) and PaaS (Platform as a Service). Under the broad concept of
Cloud, everything is treated as a Service (XaaS). (Wang, Vincent, & Xu, 2013, pp. 1-22)
Cloud Manufacturing (CMfg) is proposed after cloud computing, and cloud computing is a core enabling
technology for CMfg (Tao, Zhang, Venkatesh, Luo, & Cheng, 2012). Xu, suggests a cloud manufacturing
system framework, which consists of four layers, manufacturing resource layer, virtual service layer, global
service layer and application layer (Xu, 2012, pp. 75-86). This framework is inspired from a classical
manufacturing environment, with an implementation of cloud technologies.
Application Layer
The application layer is the part where reached to end-users/customers. The user is able to access the
virtual applications/resources via Graphical User Interfaces (GUI).
Global Service Layer
Platform as a service feature is deployed at this layer. Physical devices/machines and products are
connected by internet-related technologies, i.e. RFID, intelligent sensors and wireless sensor networks.
Virtual services, for instance computing capability, manufacturing capabilities, partial infrastructure
management and configurations, should be delivered to this layer to fulfil customer requests.
For both Application and Global Service layer should contain error-tolerance mechanisms in case of
software/hardware faults in order to keep the service working. With a built-in error-tolerance mechanism
the user can be provided by solution advices or alternative options.
Virtual Service Layer
The key functions of this layer are to (a) identify manufacturing resources, (b) virtualize them, and (c)
package them as Cloud Manufacturing services. (Xu, 2012, pp. 75-86)
Manufacturing Resource Layer
This layer includes both physical manufacturing resources, e.g. materials, machines, equipment, devices,
and manufacturing capabilities existing in the software/hardware environment, e.g. product document,
computing capability, simulation/analysis tools and etc. At this layer, resources are modelled in a
harmonized information technology and ready to be delivered and re-used. Storage Cloud is also used in
this layer to guarantee portability and longevity of product and project data, and backups.
Cloud-Based Manufacturing (CBM) refers to a networked manufacturing model that exploits on-demand
access to a shared collection of diversified and distributed manufacturing resources to form temporary,
reconfigurable production lines which enhance efficiency, reduce product lifecycle costs, and allow for
optimal resource allocation in response to variable-demand customer generated tasking. (Wu, Greer,
Rosen, & Schaefer, 2013) & (Wu, Thames, Rosen, & Schaefer, 2012)

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Key Benefits:
Implementing cloud services comes with a series of benefits to the industry:
• Cost Effective:
Cloud systems require lower initial investments compared with on-premise systems. In contrary to
on-premise systems, one does not have to buy and set infrastructure like servers and data centers.
All kinds of maintaining, configuring, updates are realized by the provider, thus saves the buyer
from having a relatively larger IT team and related costs.
• Easy and Quick Set-up:
Unlike on-premise infrastructure, when using cloud, one does not need to install extensive software
or set the underlying hardware. What required is having a network/internet connection, registering
and making configuration adjustments.
• Easy and Automatic Updated:
With traditional licensed software, you will usually have to wait for the next release to benefit from
the latest upgrades, security patches and features. Moreover, even when the software is updated,
because of the complexity of upgrading to a new system, downtimes, and high cost, businesses
usually could not favor from the new updates, or wait for longer periods to avoid this challenges.
• Universality:
Cloud computing services are platform independent; they are accessible across all devices with
internet connection. This increased mobility not only ensures the access to information anywhere
but also is the core of the cloud manufacturing concept, by enabling access to users to the
manufacturing resources.
• Scalability:
Cloud services are flexible, and scalable with your needs because with the cloud, a business can
grow at its own pace, by only purchasing its needs. With on-premise systems it is not possible to
have this kind of scalability, because it requires capital, set-up times, and simultaneously growing
IT team.
Key Challenges:
• Security and Confidentiality:
Because that the essential services are usually provided by a third-party, maintaining data integrity
and privacy is the primary concern of cloud services.
• Internet /Network Reliance:
Access to the network is essential for utilizing cloud services. Even in the presence of internet,
delivery of complex services is clearly impossible if the network bandwidth is not adequate.
• Integration:
Another important consideration for the businesses is the integration of cloud applications with
already existing data structures and on-premise software.

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4.2 Graphic illustration
Figure 4: Cloud based data handling – layers
4.3 Video & Industry examples
Types of Cloud Computing Services – IaaS, PaaS, SaaS
In this video, cloud computing and its three delivery configurations (SaaS, PaaS, and IaaS) are explained
in detail. SaaS is defined as the service which has an independent platform and provides on-demand use
of the application software for users. PaaS is a service which provides an environment where users can
build, compile and run their programs without the need of underlying hardware. IaaS is a service that offers
all the computing resources in a virtual environment in order to enable access for multiple users. Video also
provides example products and services for three kinds of delivery models: Examples for Saas are Microsoft
(Microsoft Office 365), Google (Gmail, Google Drive), SalesForce. Amazon (Amazons’s AWS), Microsoft
(Windows Azure), Google (Google App Engine) are examples for PaaS; and examples for IaaS are Amazon
(EC2), GoGrid, Rockspace.com. (EcourseReview, 2017)
URL: https://www.youtube.com/watch?v=36zducUX16w&t=52s (Published on Apr 5, 2017)

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5 Advanced data analytics
Advanced analytics refers to the autonomous or semi-autonomous processing of data that uses tools that
go beyond business intelligence (BI) to forecast future events, predict the effects for potential changes in
business strategies and generate recommendations. Advance analytics is a broad concept which includes
fields such as predictive analytics, simulation, machine learning, data mining, big data analytics, neural-
network techniques and location intelligence. (TechTarget - Search Business Analytics, n.d.) It is used in
many fields including financial, marketing, risk management but also manufacturing. (Gartner, n.d.)
5.1 Advanced data analytics in Manufacturing
In an ever more complex industrial environment, the amount of data generated transformed the traditional
approach of data analytics. In manufacturing, advanced data becomes an essential support of technologies
such as the Internet of Things. With the use of multiple sensors companies are now able to monitor and
gather real time data of each machine and manufacturing process. Nevertheless, it is also necessary to
store the data, filter, analyze it and deliver useful information.
Key benefits (Columbus, 2014)
• Increasing accuracy, quality and yield of production
• Accelerating the integration of IT, manufacturing and operational systems making possible smart
manufacturing. Ex: Big data is used for optimizing production schedules based on supplier,
customer, machine availability and cost constraint.
• Better forecasts of product demand and productions, understanding plant performance across
multiple metrics and providing service and support to customers faster
• Greater visibility into supplier quality levels, and greater accuracy in predicting supplier
performance over time.
• Measuring compliance and traceability to the machine level: using sensors on all machinery
• Selling only the most profitable customized or build-to-order configurations of products that impact
production the least (in terms of machine scheduling, staffing and shop floor)
• Quantify how daily production impacts financial performance with visibility to the machine level
Key Challenges (Nedelcu, 2013)
• Need to improve storage and computing power capability
• Shortage of deep analytical expertise to manage level of complexity: determining what data to use
for different business decisions and finding optimal ways to organize infromation
• Data security: privacy issues with consumers information
• Policy Making challenges in privacy related issues

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5.2 Scientific annotations
Big Data Analytics for Physical Internet-based intelligent manufacturing shop floors
The paper elaborates on the development of physical internet based intelligent manufacturing shop floors
by using big data analytics. The solution is build upon the deployment of RFID readers, tags and wireless
communication networks. The extension of the implementation goes from the machines to smart pallets.
With the RFID Big Data collected from the shop floors, a Big Data Analytics architecture allows the user to
visualize the logistics trajectory and to evaluate the efficiency of logistic operations and operations through
the defined behaviors and KPIs. (Zhong, Xu, Chen, & Huang, 2015)
Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment
Intelligent analytics and cyber-physical systems are teaming together to realize a new thinking of production
management and factory transformation. Using appropriate sensor installations, various signals such as
vibration, pressure, etc. can be extracted. In addition, historical data can be harvested for further data
mining. Communication protocols, such as MTConnect and OPC, can help users record controller signals.
When all the data is aggregated, this amalgamation is called “Big Data”. The transforming agent consists
of several components: an integrated platform, predictive analytics, and visualization tools. The deployment
platform is chosen based on: speed of computation, investment cost, ease of deployment and update,etc.
(Nedelcu, 2013)
5.3 Industry example
One example of the use of advanced data analytics in manufacturing can be found in the production of
biopharmaceuticals, an industry that deals with the production of vaccines and hormones. The
manufacturing process is very complex due to the variability of genetically engineered cells. There are more
than 200 variables in the production flow. The company explains how two identical processes of two
different batches can result in a variation between 50 and 100 percent. This variability highly affects the
yield and quality. In this regard, many important biopharmaceutical producers use advanced analytics, by
segmenting its entire process in clusters (depending on production relationship).
By using big data and statistical analysis, a project team could determine the influence of different process
parameters. Being able to identify specific process targets, the manufacturer was able to increase its
vaccine yield by more that 50 percent, which translates in 5-10$ million in saving for a single substance of
hundred they produces. (Auschitzky, Hammer, & Rajagopaul, 2014)

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5.4 Graphic illustration
Figure 5: Advanced Data Analytics Technologies
5.5 Video
The presented video illustrates the features and functioning of a big data platform specifically designed for
the manufacturing process. It collects information about components, processes and quality coming from
vary different sources such as: machines, sensors, camera, PLCs barcode scans among many others. It
highlights the need to process real time data to monitor and uncover problems and opportunities in the
manufacturing process. Moreover, it allows to develop retrospective and predictive analysis. (SightMachine,
2015)
URL: https://www.youtube.com/watch?v=x8hOqzBFkRk

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6 Conclusions
There is a video which summarizes well all technologies needed to manufacture “smartly” a wine holder.
(Hexagon Manufacturing Intelligence, 2017) The manufacturing cell utilizes industry 4.0 concepts and
shows in detail how manufacturing as a service works in this case: (1) customizing product using data sent
via smart phone, tablet or other devices (2) internet of things, customer is sent a QR code for pick-up (3)
external platform used for data transmission to production system  cloud based data handling, internet of
services (4) robot receives signal from PLC to initiate the process (5) single pieces of materials identified
with RFID recorder  the product itself carries information about production (6) cyber-physical
communication between software and operating machine (7) piece identification by photo-electric position
sensor  decentralised manufacturing (8) robot forwards the piece to an RFID reader to obtain unique
customer parameters and then forwards the model where required and identifies which programme should
run  smart product, machine to machine communication (9) all measurement results are recorded and
sent to statistical software platform which controls and manages production capability (10) user inputs the
QR code into the reader which detects the piece and laser engraves the information used for production
for future traceability (11) 100% customised product is delivered.
URL: https://www.youtube.com/watch?v=Ropu4FwMEHo (retrieved on 21st May 2017)
In summary, the fourth industrial revolution influences the entire supply chain. In smart manufacturing and
manufacturing as a Service, the internet of things, cloud computing, and advanced data analytics are the
most important technologies which are supported and complemented by others. It is important to ensure
data security during all processes.
Since the satisfaction of customer requirements is in the focus of all business, manufacturing as a service
is gaining more and more importance and enables companies to offer the unique selling proposition to their
clients. Also, with new innovations, the cost of investment into these technologies will decrease step by
step so in near future, it will be used by far more companies. It is applicable and can be easily implemented
to different industries. The ultimate objective is to achieve a complete automation of processes by
continuous improvement.

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7 References
Auschitzky, E., Hammer, M., & Rajagopaul, A. (2014, July). McKinsey&Company. Retrieved
from http://www.mckinsey.com/business-functions/operations/our-insights/how-big-data-
can-improve-manufacturing
Baur, C., & Wee, D. (2015, June). McKinsey&Company. Retrieved from
http://www.mckinsey.com/business-functions/operations/our-insights/manufacturings-
next-act
BI_Intelligence. (2016, August). Business Insider. Retrieved from
http://www.businessinsider.com/iot-ecosystem-internet-of-things-forecasts-and-
business-opportunities-2016-2?IR=T
Bolen, A. (2015). SAS - The Power to Know. Retrieved from
https://www.sas.com/en_us/insights/articles/big-data/3-internet-of-things-examples.html
Burrus, D. (n.d.). Retrieved from The Internet of Things Is Far Bigger Than Anyone Realizes:
https://www.wired.com/insights/2014/11/the-internet-of-things-bigger/
Columbus, L. (2014, November). Gorbes. Retrieved from Ten ways big data is revolutionizing
manufacturing: https://www.forbes.com/sites/louiscolumbus/2014/11/28/ten-ways-big-
data-is-revolutionizing-manufacturing/#2ce7d4b2ce16
EcourseReview (Director). (2017). 3 Types of Cloud Computing Services - IaaS PaaS SaaS
Explained [Motion Picture].
Friess, P., & Vermsan, O. (2011). Internet of Things - Global Technological and Societal Trends.
Aalborg: River Publishers.
Gartner. (n.d.). Retrieved from http://www.gartner.com/it-glossary/advanced-analytics/
Greengard, S. (2015). The internt of things. Cambridge: Massachusetts Institute of Technology
Press.
Groover, M., & Zimmers, E. (1983). CAD/CAM: computer-aided design and manufacturing.
Pearson Education.
Hannaby, D. (2016, October). Smart Machines & Factories. Retrieved from
http://smartmachinesandfactories.com/news/fullstory.php/aid/17/Sensors_look_smart_fo
r_Industry_4.0.html
Hexagon Manufacturing Intelligence. (2017). Retrieved from
https://www.youtube.com/watch?v=Ropu4FwMEHo
Intel (Director). (2014). Transform Manufacturing with the Internet of Things [Motion Picture].
Jeschke, S., Song, H., Brecher, C., & Rawat, D. B. (2017). Industrial Internet of Things -
Cybermanufacturing Systems. Springer.
ManufacturingTomorrow. (2017). Retrieved from
http://www.manufacturingtomorrow.com/article/2017/02/what-is-smart-manufacturing--
the-smart-factory/9166/
Marr, B. (2016, June). Forbes. Retrieved from
https://www.forbes.com/sites/bernardmarr/2016/06/20/what-everyone-must-know-about-
industry-4-0/#42b47c48795f
Meier, M., Seidelmann, J., & Mezgar, I. (n.d.). ManuCloud. Retrieved from
http://www.manucloud-project.eu/index.php?id=186#c489

16
Mell, P., & Grance, T. (2009). Perspectives on cloud computing and standards. Information
Technology Laboratory.
Meola, A. (2016, December). Business Insider. Retrieved from
http://www.businessinsider.com/what-is-the-internet-of-things-definition-2016-8
Narayan, & Lalit, K. (2008). Computer Aided Design and Manufacturing. New Delhi: Prentice
Hall of India.
Nedelcu, B. (2013). About Big Data and its Challenges and Benefits in Manufacturing. Database
Systems Journal, Vol.IV. Retrieved from http://www.dbjournal.ro/archive/13/13_2.pdf
Panoramic Power. (2016, January). Retrieved from http://www.panpwr.com/blog/5-ways-smart-
sensors-will-impact-the-future-of-manufacturing
Salah, M. (n.d.). Retrieved from http://www.msalah.com/A/PLC%20Overview.pdf
Schwab, K. (2016, January). World Economic Forum. Retrieved from
https://www.weforum.org/agenda/2016/01/the-fourth-industrial-revolution-what-it-means-
and-how-to-respond/
SightMachine (Director). (2015). Manufacturing Analytics Overview [Motion Picture]. Retrieved
from https://www.youtube.com/watch?v=x8hOqzBFkRk
Tao, F., Zhang, L., Venkatesh, V. C., Luo, Y. L., & Cheng, Y. (2012). Cloud manufacturing: A
computing and service-oriented manufacturing model. Institute Mechan.Eng., Part B, J.
Eng. Manufacture, vol 225, no 10, 1969-1976.
TechTarget - Search Business Analytics. (n.d.). Retrieved from
http://searchbusinessanalytics.techtarget.com/definition/advanced-analytics
Thingworx. (2017). Retrieved from https://www.thingworx.com/ecosystem/markets/smart-
connected-operations/smart-manufacturing/
Wang, Vincent, X., & Xu, W. W. (2013). ICMS: a cloud-based manufacturing system. London:
Springer.
Wu, D., Greer, M. J., Rosen, D. W., & Schaefer, D. (2013). Cloud manufacturing: strategic
vision and state-of-the-art. J Manuf.Syst.
Wu, D., Thames, J. L., Rosen, D. W., & Schaefer, D. (2012). Towards a cloud-based design and
manufacturing paradigm: looking backwards, looking forwards. Proceedings of the
ASME 2012 international design engineering technical conference & computers and
information in engineering conference (IDETC/CIE12), (pp. Paper Number: DETC2012-
70780). Chicago.
Xu, X. (2012). From cloud computing to cloud manufacturing. Robotics and computer-ingerated
manufacturing.
Zhong, R. Y., Dai, Q. Y., Qu, T., Hu, G. J., & Huang, G. Q. (2013). RFID-enabled real-time
manufacturing execution system. Robotics and Computer-Integrated Manufacturing 29,
283–292. Retrieved from http://or.nsfc.gov.cn/bitstream/00001903-
5/75123/1/1000006633331.pdf
Zhong, R. Y., Xu, C., Chen, C., & Huang, G. Q. (2015). Big Data Analytics for Physical Internet
based intelligent manufacturing shop floors.

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