TIME RESPONSE STUDY FOR COMMUNICATION IN PRODUCT LIFECYCLE MANAGEMENT N.Norazlin*1, 2, A.Y. Bani Hashim1,
M.H.F.M.Fauadi1, Teruaki Ito2 *1Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia
Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka,
Malaysia 2Institute of Technology and Science Tokushima University, Minami-Josanjima 2-1, Tokushima-shi,
Tokushima, 77-8506, Japan DOI: https://doi.org/10.29121/IJOEST.v1.i1.2017.01 ABSTRACT The evolvement of Internet of thing (IoT) is undeniable by making the management process become more ease at lowest cost as possible. Product lifecycle management (PLM) is a best approach to be embedded the IoT for the entire manufacturing processes. Real cases reported for weak PLM implemented like late market entry faced by A380 while Toyota faced cost loses in repair, deals and market share from massive called made which effect on company reputations. In this paper, traceability becomes a factor among man, machine and management in order to make fast respond on the data retrieved. The term traceability is measured based on response time in real time system to track the information in just in time for one-to-one communication through JAVA programming and two different operating systems as an approach. The communication can be occurred in less than 20seconds within two different machines. The traceability time is a performance measure for just in time data process which the human behavior factor is neglected for this study. The fastest time response have a potential to optimize the manufacturing management, make more efficient and offer the traceability on product/project status beside improve the flexibility, maintainability, reusability as well as extensibility. Keywords: IoT; PLM; Product Lifecycle Management; Traceability. Cite This Article: N.Norazlin, A.Y. Bani Hashim, M.H.F.M.Fauadi, & Teruaki Ito. (2017). TIME RESPONSE STUDY FOR COMMUNICATION IN PRODUCT LIFECYCLE MANAGEMENT. International Journal of Engineering Science Technologies, 1(1), 1-12. doi: 10.29121/IJOEST.v1.i1.2017.01 1.
INTRODUCTION The
evolvement of information technology open wide door for other fields like
business, manufacturing, management and etc. to be moved for high efficiency
and performance. Two difference definitions of PLM that bring to same goal of
manufacturing process. The perfect combination between people, process and data
is an integration concept of PLM (Gmelin and Seuring, 2014) was a first definition as shown in Figure 1.
According to Kevin (2013) PLM is a process that possess the ability to leverage
investment in product development process by delivering more innovative and
impactful products where it is extend from idea generation until product
retirement. The initial idea of PLM is to emphasize the customer relationship
management where delivering the customer service well is a main factor that
most business to compete each other. 1.1. Manufacturing in The
Future Online customization and purchasing
is a new disruptive purchasing model that affected the manufacturing system and
chain. This model required an evolution management while the operational levels
become a huge challenge (Mourtzis, 2016). Terms of
Big Data cannot be denied in Industry 4.0 where the only effective solution to
manage and control the complexity and disturbances is by adapting the
manufacturing networks (Mourtzis, Doukas,
& Psarommatis, 2015). Behind the manufacturing
networks, IoT, data exchange, product life cycle management (PLM), business
web, social web, computer hardware and software become the pillars. It is view
by Mourtzis, Doukas, & Psarommatis, (2015) in Figure 2 that incorporates the
recent trends in internet technologies that able to give better support to the
Industry 4.0. Figure 1: The PLM concept. The perfect
combination between people, process and data is an integration concept of PLM (Gmelin and Seuring, 2014). The
initial idea of PLM is to emphasize the customer relationship management where
delivering the customer service well is a main factor that most business to
compete each other. Any manufacturing field that
engaged with network involved its organization in manufacturing and assembly to
form raw material into finished product (Choi & Hong, 2002). The complexity
in the system is contributed by the variety that exists in an industry.
Complexity is a re-emerged activities that done repeatedly and inspired the
methodology of big-data management in computer network to take on complex
system. Furthermore it’s also energized many research fields with sufficiently
fast ability to tackle any problem in many industries (Barabási,
2011). In PLM perfective, a PLM network engaged with entire entities in
manufacturing in order to make sure the product produce meet the demand and
target. Furthermore the PLM network is aim to ease the manufacturing management
by providing the data on-board as well as can be accessed anywhere at any time. The applications of IoT have
compelling the enterprise operations to keep up and meet the market demand. The
force of global market makes many industries to rethink their productivity,
quality strategies techniques and approach of overall operations management.
Industry 4.0 as future manufacturing is targeting to compel the principles and
strategies of just in time (JIT), total quality management (TQM), computer
integrated manufacturing (CIM), agile manufacturing, lean production, quick
respond manufacturing (QRM) as well as supply chain management (SCM)
(Gunasekaran, & Ngai, 2012). Figure 2: Manufacturing view in the future (Mourtzis,
Doukas, & Psarommatis,
2015). As an emerging manufacturing,
the automation is not only automate the physical
processes but data also include. The automated of physical processes and
information processing able to achieve a long term sustainable production. The
automation processes become a goal in deterministic manufacturing and one of
the criteria for Industry 4.0. The challenge and
manufacturing issues in Industry 4.0 is summarized in Figure 3 below where it’s
divided into three main factors which are man, machine and management. To
handle the complexity in manufacturing network, the future focused leadership
and mind set is required. Furthermore the more intelligent equipment or
machines occupied, the higher skill worker required to operate that.
Traceability become a main focused in this study where the usage of IoT is
manipulating to track the product information and material used during
manufacturing process. The traceability makes the whole manufacturing processes
become visible and easy to manage. PREC-IN monitoring system
provides the most effective adjustment in process parameter and its lead to
reduce the final product performance. Furthermore, the corrective action is
achieved in just-in-time (Boorla, & Howard,
2016). Smart technologies for manufacturing bring a bundle of complexity in
order to manage and control the information either giver or share and improve
the communications in near-real-time. The expanding accessibility of 'huge information'
has raised the desire that we could make the world more unsurprising and
controllable. Indeed, the real time respond (RTT) in communication and
management able to overwhelm the instabilities get from delayed response or
worst information handling (Helbing, 2013). Industry 4.0 possess the smart technology equipment such as communication
devices and information tools in order to inform the customer/client about
product status by loading the data in near-real-time or just in time. Beside
that the operators accountability and line performance can be evaluated can be
informed in near-real-time (Siano, 2014). However, does the devices and tools able to respond
in near-real-time? In this study, RTT is study by using two different operating
system where the signal parsing through socket connection. Figure 3: Current manufacturing issues faced. The challenge and
manufacturing issues in Industry 4.0 is divided into three main factors which
are man, machine and management. To handle the complexity in manufacturing
network, the future focused leadership and mind set is required. 1.2.Issues in PLM PLM
emphasized the combination of people, process and data to be successfully
implemented. However the implementation of PLM also contributes to several
losses based on real case scenarios occurred around the world when it’s
neglected several factors. Figure 4 shows the six issues that been identified
from the current study and the factors affected in sustainable PLM. Green
focused should provide an important competitive advantage instead of minimizing
the environmental harm only. In to integrate environmental issues into new
product development (NPD), the environmental factors must be considered in all
stages of the manufacturing process (Polonsky & Ottman,
1998). The emerging of green technology involve two sides in manufacturing
perspective, customers demand and supply from manufacturers which pressuring
and responding to it. This point of view enforced the pre-production stage to
consider the environmental issues in the design process (Baumann et al., 2002).
Polonsky & Ottman, (1998) believes that the
successful of green NPD involve a wide set of stakeholders while Lee & Kim,
(2011) agreed that the suppliers plays a major role for NPD where it’s begin
from the design concept stage to the prototype development stage. Collaboration
and communication are two main factors for green NPD. Collaboration is defined
as coordination and alignment with project teams since the green NPD having a
broad demand and various inputs and multifunctional product development, to
meet market and environmental regulatory requirements become a main reason why
the team needs to be coordinated. Effective communication between stakeholders
is needed in order to provide information to produce green NPD. The information
become extremely valuable in preproduction stage where its involve design and
testing in order to ensure the NPD is meet the environmental regulations. Figure 4: Issues in PLM. In PLM, there are
three important factors that make it complete and works efficiently. People,
process and data required good collaboration and by intervening the technology
into PLM make it become more successful to merge the business globally. Complexity
in NPD required a stable system to manage the development process. To manage
the entire PLM is not an easy activity in order to meet the target such as
customer demand, early market entry, new invention product and etc. The
transforming of virtual production (designing, testing and simulation) into physical
production is difficult to control during phase of life. The managerial
complexity of PLM becomes cross-enterprise issues and even more challenging.
Late market entry and exceed the targeted cost are the serious consequences
faced if the company loses control in PLM (Stark, 2015). It is proven by real
cases reported when weak PLM was implemented. i) Case 1:
Airbus Company: A380 was reported on
missing target in new production and leads to delayed market entry due to their
weak product life cycle management (PLM) (TechDrummer,
2008) ii) Case 2:
Toyota Company: Until year 2009,
Toyota made a massive vehicles call due to car complexity of 11 major models
and over 9 million vehicles. The recalls cost at least $2 billion in cost of
repair and lost deals. The recall result in lost 5% of its market share in
United State of America and further drops foreseen (Gu, 2010). The
fluctuation demand occurred when the awareness campaign on green product and
keep the environment safe become effective. This point enforced company to
create new product that comply with environment regulation. The interest in
sustainable development growth rapidly when company start to consider
mitigating the material used and waste product and any future weakness as well
as inefficiencies can be avoided (Bevilacqua et al., 2007). Organizing and
managing the sustainable development and NPD become more complex and it’s
dependent on organized process and technology as a critical success factors (Gmeling & Seruing, 2014;
Johnson et al., 2010). PLM required technology as an integrative approach in
order to manage the data and process for NPD towards sustainable and
efficiently possible in new product process but not in development/design phase
only (Gmeling & Seuring,
2014). Current
practise of PLM is reviewed in Table 1 where the most application is neglected
the technology invention in order to manage the data and process. The
successful collaboration process can be achieved when PLM able to interact with
coordination, information exchange, negotiation and solving conflicts (Wiesner
et al., 2015). In PLM, there are three important factors that make it complete
and works efficiently. People, process and data required good collaboration and
by intervening the technology into PLM make it become more successful to merge
the business globally. To reduce the communication barrier cause by
geographically factor and the used of web-based management seem the only way to
make it successfully manage. Gaps
analysis as shown in Figure 5 has been concluded from the reviews on current
PLM implementation and agent web-based application. Too focused on NPD is
noticed as the first gap where current implementation or research put a lot of
focused in pre-production stage in order to make sure the product development
comply with the environment rules and regulations. Second gap reveal the poor
information interaction and lack of data exploitation for the entire PLM and Gmeling & Seruing, (2014)
noticed that the sustainable NPD is only feasible to be done in pre-production
only and hard to be implemented in the entire of PLM. It is because the company
lack of communication by ignoring the information exchange between supplier,
customer and retailer become the third gap in current PLM practise. This point
of view proved the idea proposed by Polonsky & Ottman,
(1998) where the sustainable NPD should be involved with wide stakeholders. In
order to achieve the sustainable PLM, the information exchange and trading is
required in entire PLM. By emphasizing the MAS in PLM, it’s able to make the
idea of sustainable PLM happen with its ability to solve the complexity and
expedite the process and secure communication network in management and
production process. Table 1:
Studies reviewed on PLM. In PLM, there are three important factors that
make it complete and works efficiently. People, process and data required good
collaboration and by intervening the technology into PLM make it become more
successful to merge the business globally.
Figure 5 Gaps in current PLM practise. Current
practise of PLM neglected the technology invention in order to manage the data
and process. The successful collaboration process can be achieved when PLM able
to interact with coordination, information exchange, negotiation and solving
conflicts. 2. ROUND TRIP TIME Most of
the manufacturing target emphasized the time period for every activities
including communication. The purpose of the experiments in this section is to
verify the optimal cost through RTT. RTT is measure by using socket
communication between two or more computer involve client and server
environment. The PC or workstation is referring to client which provides with
friendly interface such as Windows. While a group of users is provide by server
to client for sharing the server program (Xue et. al, 2009). There
are two type of operating system used that running over the network where the
details of machines (computers) used is shown in Table 2. Windows and Linux as
an operating system provide the communication link between users and the
devices (Perchat et. al, 2013). The communication between
two programs running through socket that constitutes a client-server application.
The connection process started with client send a request to the server on
specific port. The server is on ready mode for listening and accepts the
request from the client. Once the connection is accepted, the client able to
use the socket to communicate with the server and begin with read/write from
their sockets. The process cycle is shown in Figure 6 below where the
activities is keep on happening until the server is disconnected. From the test
conducted, the time is estimated based on the formula (1) below where the is a time for
server to accept the connection while is an ended
time for the communication process. The client started to read and write or
vice versa during the communication process. From formula (2), the raw mean
time is measured in order to know the mode of distribution for the n samples where n is total number of tasks conducted.
(1)
(2) Table 2: Machine
Details. Windows and Linux as an operating system provide
the communication link between users and the devices (Perchat et. al, 2013). The communication between two programs running through socket that
constitutes a client-server application.
Figure 6: The general process cycle for one way
communication through sockets and server. The activities are kept on happening
until the server is disconnected. The connection process started with client
send a request to the server on specific port. The server is on ready mode for
listening and accepts the request from the client. Once the connection is
accepted, the client able to use the socket to communicate with the server and
begin with read/write from their sockets. 3.
RESULTS AND
DISCUSSIONS 3.1.Intra-Platform Intra-platform communication
occurred as internal signal respond in one machine as shown in Table 3 that obtained
from two operating system and four different machines. Huge difference of
respond obtained by V3 compare to V1, V2 and V4.
The different probably cause by the machine itself where V3 is habitualized with programming development compare to
others. Furthermore the other machine is rarely used. However, the speed of
network also contributes to the long period taken for every machine to respond.
3.2.Inter-Platform For the inter-platform signal
responds shown in Table 4, the RTT involved difference server for every
testing. Overall performance showed that the server V3 gives the fastest signal
respond in 14ms compared to others. However, the V4 shown the overall signal
respond in 588ms where the Linux is an operating system for that machine. Linux
give a lot of benefit for computer and network development but it less to be
used for manufacturing purpose. For server V1 and server V2 in shown the
instability occurred with overall signal respond is 644ms and 645ms
respectively. Table 3: Intra-platforms’ round trip time results. The testing is done for internal operating system communication. The V3 obtained the fastest result compare to others due to high frequency of used for programming.
Table 4: Inter-platforms’ round trip time results. The result obtained when the V1, V2, V3, and V4 become a server and communicate with others machine. The respond time is calculated and still showing the V3 is fastest than others.
4. CONCLUSIONS & RECOMMENDATIONS The
fastest respond is obtained by V3 compared to V1 and V2
that used same operating system which is Window. Linux as an operating system
for V4 also give better respond even the machine does not frequently
used for programming development. Stable communication showed by most of the
machine is contributed by stable networking signal and computer performance
itself. Linux as an operating system offered the stability in system security
compared to others operating system but because of the complexity of system to
be used as well as operated become an occupied barrier. Windows as an operating
system is typically used in industry while Linux been avoided to be used event
the operating system is much better compare to others. Habitualized
with programming activities become a contribution factor for V3
machine with Windows operating system. The time
response for the communication system possible to be gained in less than 20
seconds and afterwards it depends on how the man or human act on the
information given. The three common factors in industry are man, machine and
management is strongly related to PLM, traceability and time respond. If the
machine shows the effective value, but yet human behaviour does not be able to
respond well on the data, so that the management need to play a role in order
to make sure the data retrieved is delivered well. In Industry 4.0, the rapid
respond is needed for every issue by help from IoT. The fastest respond gained,
the more efficient of manufacturing processes is achieve by avoiding the delay
in traceability and expedite the contingency plan to be implemented in order to
meet the market demand. ACKNOWLEDGEMENTS The author would like to
acknowledge the High Education ministry of Malaysia for the study sponsored and
Universiti Teknikal
Malaysia Melaka (UTeM) for the facilities support. REFERENCES
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