The Internet Connected Production Line: Realising the Ambition of

Cloud Manufacturing

Chris Turner

and Jörn Mehnen

Surrey Business School, University of Surrey, Guildford, Surrey GU2 7XH, U.K.

Dept. of Design, Manufacture & Engineering Management, University of Strathclyde, Glasgow, G1 1XJ, U.K.

Keywords: Cloud Manufacturing, Industry 4.0, Industrial Internet, Cyber Physical Systems, Internet of Things, Cloud

Computing, Redistributed Manufacturing.

Abstract: This paper outlines a vision for Internet connected production complementary to the Cloud Manufacturing

paradigm, reviewing current research and putting forward a generic outline of this form of manufacture. This

paper describes the conceptual positioning and practical implementation of the latest developments in

manufacturing practice such as Redistributed manufacturing, Cloud Manufacturing and the technologies

promoted by Industry 4.0 and Industrial Internet agendas. Existing and future needs for customized

production and the manufacturing flexibility required are examined. Future directions for manufacturing,

enabled by web based connectivity are then proposed, concluding that the need for humans to remain ‘in the

loop’ while automation develops is an essential ingredient of all future manufacturing scenarios.

1 INTRODUCTION

The Internet and its supporting technologies have had

a profound impact on society and business around the

world over the past 20 years. The business models of

companies in service industries such as finance, retail

and the media have seen fundamental change in

response to the opportunities offered by the web and

increasing acceptance of this communication channel

by customers. The possibilities being realized in

industries such as banking and retail are only just

starting to filter through to potential realization in a

manufacturing setting. Internet connected production

provides an infrastructure for the opportunities

offered by both Cloud and redistributed forms of

manufacturing through the utilisation of internet

protocol and web data description formats.

Through a combination of new technology and

consumer demand for novel ‘tailored’ products there

has been a move away from classic mass production

manufacturing models towards mass customization

and mass personalization. Mass customization relates

to the production of products which may be

customized to the individual consumer needs

(Mourtzis and Doukas, 2014); the automotive

industry is a good example where a customer may

select options to modify a mass produced vehicle

variant. Major initiatives that promote Internet

connected (or at least network connected) production

lines, such as Industry 4.0 (Federal German

Government, 2016) and the Industrial Internet

(Posada et al. 2015), espouse the primacy of

interconnected machines and intelligent software

forming cyber physical manufacturing entities. In

addtion manufacturing paradigms such as Cloud

Manufacturing (Zhang et al. 2014) and Redistributed

Manufacturing (Moreno and Charnley, 2016) (Ellen

Macarthur Foundation, 2013) leverage digital

connectivity in realising their aims of remote and

geographically dispersed production entities.

The Internet of Things (IoT) forms a vital part of

the infrastructure, enabling internet conncted

production through the ubiquitous presence and

availability of network connected sensors and the

efficient near to real time processing the data they

collect. It is suggested that instead of thinking of the

aforementioned initiatives and connectivity as

separate and even competing there is a level of

convergence that can be expressed in the delivery of

the Internet connected production line as both a

physical and digital entity.

Turner, C. and Mehnen, J.

The Internet Connected Production Line: Realising the Ambition of Cloud Manufacturing.

DOI: 10.5220/0006894001370144

In Proceedings of the 14th International Conference on Web Information Systems and Technologies (WEBIST 2018), pages 137-144

ISBN: 978-989-758-324-7

137

2 VISION OF THE INTERNET

CONNECTED PRODUCTION

LINE

A common theme to all of the aforementioned

subjects outlined in the preceding section of this

paper is the ubiquity of Internet technologies and their

use in connecting once disparate entities to form a

digital “nervous” system for manufacturing. An

important facilitator of the required interconnectivity

is provided through the concept of Servitization in

combination with Webservices technology expressed

through a Service Oriented Architecture (SOA)

(Tzima and Mitkas, 2008). When products and

services are sold as bundled offerings (Servitization)

the digital interactions between different company

entities and even the customer may be conveyed

through web based applications on a request and

provision based protocol (Baines et al., 2009; Wu et al.,

2015). Much of the richness in the digital

communication now possible is provided by semantics,

whereby meta-data to describe manufacturing

parameters and other data points promote a meaningful

exchange between engaged parties.

2.1 Internet Connected Production

Line Goal

While the question must be asked ‘what is the goal

for Internet Connected Manufacturing?’ Building on

Wu et al. (2015) vision for Cloud Manufacturing, the

following aim can be formed for an Internet

connected production line:

‘To provide an outline template for distributed

production benefiting from autonomous decision

making based on a real time view of the organisation

and its environment’. An additional question that

may also be asked regarding the location of the

production facilities if all parts of the manufacturing

process are digitally linked. Along with trends in

economics and geopolitics, re-distributed

manufacturing can provide part of the answer to the

question of production line location. Along with

trends in economics and geopolitics, re-distributed

manufacturing can provide part of the answer to the

question of production line location. In a monolithic

factory producing products such as automobiles, the

main production assembly resides in one factory with

subsidiary factories producing substantial

components such as engines and gearboxes (the

subsidiary factories are normally wholly owned

facilities of the parent company or first tier suppliers).

In geographically distributed factory facilities, the

notion of centralised production recedes as assembly

of a product may be localised in a region of a country

or at the retail end point or perhaps even in the

customer’s home. It is certainly true that the amount

of Big Data created has increased at an exponential

rate (Kambatla et al., 2014) and this is providing new

opportunities for the development and production of

new products. Among the points made by Li et al.

(2015) regarding Big Data and Product Lifecycle

Management (PLM) the following are particularly

relevant to the operation and use of Internet

connected production lines Li et al. (2015):

• Lifetime prediction for parts

• Access to product design data inside and

outside a company – improving design

• Access to production line data

• Monitored products and product service

systems

• IT integration and connection with

production line sensors and CPS

• Prediction of customers’ needs and demand

level for products

• Supplier performance measurement and

prediction

• Smart maintenance of production lines

• Controlling energy consumption and

enabling emission reduction in

manufacturing

Cloud Manufacturing is a term that has been gaining

in popularity in recent years. As described by Xu

(2014), Cloud Manufacturing is a concept that aims

to describe how Cloud technologies could be used to

link distributed production facilities with both

customers and suppliers. Dynamic scalability of

production is possible with Cloud manufacturing

where products can be produced using generic non-

specialised tooling (Wu et al., 2013).

2.2 Security

Research is underway on ways to establish trust,

transparency and legal liability when it comes to

distributed and global Internet business connections.

The W3C (World Wide Web Consortium) highlight

that further research into IoT security highlighting the

need for methods to ensure end to end trust and

techniques to verify related metadata (describing data

provenance) and its context (W3C, 2017). Recent

research includes the use of Blockchain technology to

provide a means for secure transactions in

manufacturing supply chains. Abeyratne and

Monfared (2016) outline Blockchain use in

manufacturing highlighting its ability to allow two

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parties to trust and securely transact with each other

without the need for a 3rd party facilitator.

Blockchains use in IoT interconnectivity has been

outlined by Bahga and Madisetti (2016) who propose

a secure platform for IoT based on Blockchain,

allowing for connectivity between peers in a formally

trust-less network environment.

2.3 Metadata

It is also the case that systems for well-coordinated

linkages between production cells are still in

development. Wang (2015) describes the concept of

networked cellular manufacturing though raises a

number of concerns and mitigations for cyber security

issues. In addition the moves towards standardisation

on internet technology (and adoption of web

standards) for data transmission in a manufacturing

setting is still a work in progress. The W3C (World

Wide Web Consortium) likens the current situation in

IoT interoperability to the pre internet times and their

competing networking technologies often operating

with proprietary and incompatible standards (W3C,

2017). W3C (2017) envisage a future situation where a

combination of existing web standards are used with

semantic meta-data descriptions (perhaps based on

RDF (Resource Description Framework) and

LinkedData) to provide open interoperability between

physical and virtual entities associated with IoT.

3 SEMANTICS FOR

MANUFACTURING

In realising the fine grain interoperation of both

physical and virtual systems required by the internet

connected production line the use of semantics to

provide additional meaning and context to data

becomes essential. A number of metadata language

standards have recently developed for use in

manufacturing applications.

3.1 Semantic Standards

The development of the AutomationML (2017)

standard has in part been one response to this need for

interoperability between industrial automation plant

and computer systems (Drath et al., 2008). As an

XML (Extensible Markup Language) compliant

language AutomationML has the ability to model

information at different levels allowing for lossless

data transfer between entities and, according to Luder

et al. (2010), enhanced parameter traceability (for

possible reconstruction of an audit trail for automated

decision making). The OPC UA (OPC Unified

Architecture) is a standard for industrial system

intercommunication that is platform neutral (OPC

Foundation, 2017). This standard while comprehensive

in its specification can be complex and expensive for

an organisation to implement. The work of Henßen and

Schleipen (2014) examines the role that the

AutomationML mark-up language can play in

simplifying the use of OPC UA models with existing

data sets and streams expressed in XML. According to

Henßen and Schleipen (2014) use of OPC UA directly

is a complex task, utilising AutomationML mapping to

OPC UA opens up the opportunity of streamlined

connectivity with OPC UA compliant systems and

manufacturing systems. The Industry 4.0 vision states

that individual parts of a manufacturing operation such

as machinery, IT systems and deployed sensors can be

described as autonomous discrete components each

with their own semantic descriptions (Grangel-

Gonzalez et al. 2016). The work of Grangel-González

et al. (2016) highlights the use of RDF (Resource

Description Framework) in the provision of an

administrative shell for Industry 4.0 components. RDF

facilitates a semantic description of data to be

exchanged. In the context of Grangel-González et al.

(2016) it is used in concert with the Reference

Architecture Model for Industry 4.0 (RAMI 4.0).

VDI/VDE (2015) is the model used to describe product

lifecycle, IT systems and manufacturing plants in a

holistic shared context of Industry 4.0. Mazzola et al.

(2016) detail the CDM-Core manufacturing ontology

for production and maintenance, citing its use in sensor

stream annotation and web services as key benefits. An

additional commentary on semantic service

composition is provided by Mazzola et al. (2017) in

which the authors outline a pattern based approach to

the service oriented implementation of manufacturing

plans. Big Data is a core asset of Industry 4.0

implementations with its intelligent processing

providing the potential for context aware autonomous

operation and self-adaptation.

3.2 Data Requirements and Context

Golzer et al. (2015) investigate the data processing

requirements of Industry 4.0 and deduce seven broad

categories composed of four data requirements and

three processing requirements. The content of the

data in the opinion of Golzer et al. (2015) divides into

four categories: Product data; Process data; Business

data; Sensor data. In the processing of data three main

categories of processing are put forward by Golzer et

al. (2015): Decision processing; Knowledge

The Internet Connected Production Line: Realising the Ambition of Cloud Manufacturing

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processing; Real-time processing. It is possible that

two additional categories can be put forward that of

context related meta-data and the further contextual

processing of that meta-data related to the

aforementioned categories.

4 AN EXAMPLE OUTLINE OF

AN INTERNET CONNECTED

PRODUCTION LINE

One of the main motivations behind an Internet

connected production line is the use of TCP/IP

(Transmission Control Protocol/Internet Protocol) to

provide ubiquitous networking capabilities

throughout the manufacturing operation, moving

beyond proprietary networking solutions. In addition

to addressing the changing nature of consumer

demand, from generic products through mass

customization towards a future of products designed

for a market of one (mass personalization), it is the

potential for automation and autonomous operation of

distributed manufacturing facilities that the Internet

connected production line framework can contribute

a holistic template. Figure 1 displays an outline of an

Internet connected production line as a system. It can

be seen in Figure 1 the production line itself is

composed of 3 entity types; production line

machinery; production line robots; 3D printing. All

entity types are capable of providing data streams and

exposing control interfaces. In terms of the first two

types CPS systems may be inherent in providing the

possibility for distributed intelligence (and local

decision making on the shop floor) combined with

central control within the business intelligence layer

of the semantic sandwich. Production line decision

making may even be componentized as an agent

based representation within the business intelligence

layer. Both inbound logistics (the supply chain) and

outbound logistics are shown in Figure 1.

4.1 Metadata and the Internet

Connected Production Line

Real time digital connectivity with both entities is

essential for the Internet connected production line.

The meta-data rich communication utilizing internet

protocols provides an ‘open’ format for data

exchange. Central to the framework outlined in

Figure 1 is the ability to transform messages produced

in one part of the system for processing and analysis

in another. Physical production line equipment is

likely to describe parameters in a particular format

which may be proprietary. The use of XML languages

such as AutomationML and XML compatible meta-

data frameworks such as RDF, along with suitable

ontologies, allows for meta-described data produced

by a variety of heterogeneous machines to be

captured and then transformed into other XML

dialects suitable for business systems and intelligent

decision support applications (see also section 3.1 of

this paper for standards). In addition to meta-data

transformation it is necessary to employ intelligence

within the semantic sandwich illustrated in Figure 1

to establish the order in which parameters are being

produced, processed and then acted upon (decision

making). In this way the context of semantics is also

processed by the business intelligence layer as well as

providing decision making assistance.

4.2 Webservices and the Internet

Connected Production Line

This framework also helps to componentize the

production line and business systems of a

manufacturing organization. In effect each

component could conceptually become a service and

be exposed in the form of a Webservice. The work of

Vergidis et al. (2015) provides a method for

assembling discrete Webservices to form actionable

and optimized processes. Such a technique could be

applied to the Internet connected production line

whereby manufacturers would be able to dynamically

assemble new production processes from existing

components exposed as services.

Once such a process has been assembled the

individual components (such as robots and machines)

may be virtually represented as agents in terms of

enacting intelligent decision making and control

activities (in the live production phase). Automated

negotiation would also assist in the selection and

integration of services (Fatima et al., 2015). While

offering manufacturing services for clients outside the

organization a manufacturer may also wish to utilize

this mode to reconfigure current operations. Short run

production of highly customized products could

benefit from an increased understanding of the current

production facilities that a framework as presented in

Figure 1 could facilitate. Supply chain integration may

also be streamlined via such a framework through the

provision of a standard API (Application Programming

Interface) and common XML based message passing

language. Even without full compatibility in the supply

chain the functionality of the framework affords a level

of adaptation difficult to achieve in a manufacturing

environment composed of disparate systems and

traditional administrative silos.

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Figure 1: The Internet Connected Production Line as a system.

Figure 2: The Semantic Sandwich.

The Internet Connected Production Line: Realising the Ambition of Cloud Manufacturing

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4.3 Decision Making in the Internet

Connected Production Line

At this point it should not be forgotten that decision

making regarding the overall operation of the Internet

connected production line still requires the human in

the loop (potentially even in a limited oversight role

when operating in a future fully autonomous mode).

In this respect the value of information presentation

and interface design becomes paramount when

developing dashboard applications to facilitate

human interaction in the control of the manufacturing

operation. Along with enhanced interface design

metaphors the way a user will view manufacturing

processes will change too. A process flowchart

metaphor may have added value in such an

environment whereby many, once disparate systems

are brought together and viewed as a networked

cooperating ecosystem (Fayoumi, 2016). The use of

simulation systems used with Virtual Reality and

Augmented Reality headsets/ viewing devices may

also be beneficial in the human interaction with future

manufacturing operations (Turner et al., 2016).

4.4 The Semantic Sandwich

The semantic sandwich (shown in Figure 2) is

composed of the following sub layers:

• Data stream filtering and analysis layer – This

layer will filter both existing data sets and data

streams produced by production line sensors,

• Semantic / ontology / context layer – This

layer will describe discrete data points with semantic

descriptions that will indicate the context in which the

data has been collected and its potential relevance.

This layer is central to the concept of the Internet

connected production line in that all data is

semantically described (via metadata) for

presentation to users in a human readable format

• The Business intelligence layer – This layer

will employ algorithms to process semantically

tagged data in combination with business rules drawn

from ERP (or similar) business systems to provide

decision making capability. This layer is in effect the

computational intelligence layer where centralised

decision making takes place (although distributed and

localised intelligence of CPS may also be embodied

in production line machines and robots).

• Decision Making / Monitoring Dashboards –

A range of web delivered interfaces will be capable

of detailing every activity within the system, in

practice a sub set of the data will be provided by the

semantic sandwich layer via friendly interfaces for

decision making. The concept of ‘Human in the loop’

is reinforced within this framework through

streamlined access to decision making and the ability

to mine audit trails of decisions (and the reasoning

behind decisions) and activity that have occurred

within the Internet connected production line.

• Enterprise Resource Planning System(s) –

Although not officially part of the semantic sandwich

this layer is in effect the interface and API for 3rd

party ERP software. ERP systems may be linked in to

the Internet connected production line and may access

data and re-create its decision making and monitoring

dashboards through APIs (Application Programming

Interfaces) or simply access data streams from the

production line.

• Data Resources – data storage resources will

be utilised by the Internet connected production line.

There will be a necessity to capture and store data

streams from the production line and audit trails of

decision making within the semantic sandwich layer

and monitored activities within the system.

4.5 Meta–heuristic Feedback

Meta-heuristic feedback functionality is used within

the semantic sandwich to identify and maintain a set

of heuristics or mining rules that would help in

identifying features and patterns in data at the sensor

fusion layer level. This feedback would be produced

from the analysis of data from the ‘shop floor’ and

from ERP and business systems. These rules would

be at the level of guidance to detect broad patterns.

This information is then utilised by the audit trail

wrapper to detail the decisions made in human

readable format.

4.6 Audit Trail Wrapper

An audit trail process may be tied to an individual

product or a particular manufacturing process taking

place within the production line. Event based process

chains comprising the audit trail may contain meta-

data descriptions to help establish provenance of the-

data. Decisions made by the semantic sandwich layer

can also be described in the audit trail. This notion of

audit trail use originates in the field of cyber security

and with this in mind the wrapper may act as an

invaluable addition to the security protocol within

internet based manufacturing in the future. It is also

the case that for humans to play an active role in the

development of automated and eventually

autonomous production there is a need to understand

at some level how and why decisions are made by

machines. The audit trail may provide insights in that

particular direction

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5 A RESEARCH AGENDA FOR

THE FUTURE

With a move to mass customization and mass

personalization the need to rapidly adapt and tailor

products to individual customers’ exact requirements

will grow. The Internet connected production line is

one response to the provision of manufacturing

flexibility that will be required to meet this

challenging expansion in demand. It is the case that

more research needs to be conducted into security

issues surrounding both data transmission and the

safe completion of digital contracts. A future where

autonomous production is a reality will also require

accurate descriptions and capture routines for data.

The use of semantic technologies is vital to provide

both fine grain control over production and greater

understanding of products, customers and the

manufacturing operation as a whole. In such a world

informated products may be able to take an active part

in their construction while on the production line and

then report back to the consumer and the

manufacturer about such factors as their health status

when in use. Such levels of data necessitate the

further development of decision support systems and

their automation, though the users should always be

able to view the complete production system and

question its operation.

6 CONCLUSIONS

This paper has outlined the potential components of

an Internet connected production line. This concept

addresses the need for more flexible manufacturing to

meet the demand for customised and personalised

products. In adopting a redistributed view of

manufacturing the Internet connected production line

demonstrates how large factory manufacturing can

evolve into regional production facilities near the

customer. Both Industry 4.0 and the Industrial

Internet visions popularise a wide variety of

technologies and approaches to manufacturing, the

concept in this paper illustrates how such

technologies can be implemented within a production

line and outlines the advantages for doing so.

Central to the Internet connected production line

is the use of open standards web technology and

networking protocols. Along with this is the use of

semantic descriptions for data and intercommunica-

tion with the factory and outside suppliers, logistics

providers and customers. It is in the harmonisation of

data descriptions that holds the best route to

interoperability between systems in organisations and

human understanding of increasingly complex

manufacturing processes and a rapidly changing

business environment. The integration of machine

intelligence and coordination of multiple CPS

systems, promoted by the Internet connected

production line, may also lead to the realisation of an

overall information and control system necessary for

future automation efforts. The ability for humans to

remain in the loop while automation develops to a

future state of maturity is an essential ingredient in all

future manufacturing scenarios.

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