The Life Cycle of Manufacturing Networks in the Mass Customisation Era
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Course: | BUS606: Operations and Supply Chain Management |
Book: | The Life Cycle of Manufacturing Networks in the Mass Customisation Era |
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Date: | Saturday, September 7, 2024, 7:41 PM |
Description
Read this article. The research focuses on network design performance in our current era of product customization and personalization. With online order volume steadily increasing, what network design considerations do you feel are necessary to successfully fulfill custom orders?
Abstract
Manufacturers and service providers are called to design, plan, and operate globalised manufacturing networks, addressing to challenges of increasing complexity in all aspects of product and production life cycle. These factors, caused primarily by the increasing demand for product variety and shortened life cycles, generate a number of issues related to the life cycle of manufacturing systems and networks. Focusing on the aspects that affect manufacturing network performance, this work reviews the exiting literature around the design, planning, and control of manufacturing networks in the era of mass customisation and personalisation. The considered life cycle aspects include supplier selection, initial manufacturing network design, supply chain coordination, complexity, logistics management, inventory and capacity planning and management, lot sizing, enterprise resource planning, customer relationship management, and supply chain control. Based on this review and in correlation with the view of the manufacturing networks and facilities of the future, directions for the development of methods and tools to satisfy product–service customisation and personalisation are promoted.
Source: D. Mourtzis, https://link.springer.com/article/10.1007%2Fs12159-015-0129-0
This work is licensed under a Creative Commons Attribution 4.0 License.
Introduction
Mass production (MP) has been the established
manufacturing paradigm for nearly a century. MP initially answered to
the need of the continuously increasing population around the globe,
with a gradual improvement in its living standards, especially in the
developed world, for goods and commodities. However, since the 1980s and
with the beginning of the new millennium, a saturation of the market
towards mass produced products is observed. In 2006, Chryssolouris
states that: "It is increasingly evident that the era of MP is being
replaced by the era of market niches. The key to creating products that
can meet the demands of a diversified customer base, is a short
development cycle yielding low cost and high quality goods in sufficient
quantity to meet demand". Currently, the need for increased product
variety is intensifying, and customers in many market segments request
truly unique products, tailored to their individual taste. Companies are
striving to offer product variety while trying to produce more with
less (i.e. maximise their output while minimising the use of
materials and environmental footprint), while the landscape that they
must operate in, inflicted by the economic recession, has become more
complex and dynamic than ever.
In the mass customisation (MC)
paradigm, the establishment of which is still an ongoing process,
instead of treating customers merely as product buyers, a producer must
consider them as integrated entities in the product design and
development cycle. In this customer-driven environment that is shifting
towards online purchases and market globalisation, the underlying
manufacturing systems and chains are heavily affected. Owing to its
multidisciplinary nature, the manufacturing domain in general lacks of
unified solution approaches. The management of the co-evolution of
product, process and production on a strategic and operational level is a
huge challenge. Market globalisation broadens the target audience of a
product, while at the same time it constitutes supply strategies and
logistics' more difficult to manage. Adding to that, the Internet, one
of the primary enablers of globalisation, allowed online customisation
and purchasing, leading to new disruptive purchasing models. In their
turn, these models affected long-established businesses that could not
form an online presence fast and succumbed to the competition. Moreover,
the economic recession highlighted the need for quick adaptation to
demand; companies that could not adapt to the new requirements suffered
economic losses and their viability was challenged. Simultaneously, the
decreasing product costs and the increase in purchasing power in
developing countries generated new markets and destabilised demand.
Finally, the emergence of new materials, new forms of production, and
key enabling technologies constitute new diversified product features
and processes feasible, as well as they allow the interconnection
between ICT systems, humans, and engineering/manufacturing phases.
It
becomes apparent that manufacturers and service providers are presented
with numerous external and internal drivers and challenges that
have a visible impact on the smooth operation of the entire value-adding
network down to each individual manufacturing facility. A root
cause for these problems is that while the MC paradigm proposes a set of
practices and solutions for tackling these issues, its practical
implementation is still considered as work in progress in terms of
effectiveness of coordination and collaboration between stakeholders,
design and planning of networks and facilities, and execution and
control efficiency. An enabling solution for realising a
cost-effective implementation of MC is to properly configure easily
adaptable manufacturing networks, which are capable to handle the
complexity and disturbances that modern production requirements inflict. Support systems for the design, planning, and control with inherent
robustness are necessary in order for companies to withstand the
antagonism through sustainable practices. Technology-based business
approaches comprise a major enabler for the realisation of robust
manufacturing systems and networks that offer high value-added,
user-oriented products and services. These qualities are critical for
companies in order to master variety and maintain their viability.
Significant work has been conducted on this field, yet a focused review
of the literature regarding the influence of MC practices on different
aspects of the manufacturing network life cycle is missing. In
particular, the lack of dedicated reviews on the challenging issues of
design, planning, and operation of manufacturing networks in the
framework of MC forms the motivation for conducting this work.
Towards
bridging this gap in academic approaches, this work reviews the
existing literature related to the basic aspects of a manufacturing
network from its design, planning, and control life cycle perspectives
within the general MC landscape, targeting to the understanding of the
current situation and identification of future developments. For the
scope of the paper, areas of supplier selection, initial manufacturing
network design, supply chain coordination, complexity, logistics
management, inventory and capacity planning and management, lot sizing,
enterprise resource planning (ERP), customer relationship management
(CRM), and supply chain control are reviewed. The purpose is to
establish an overview of the current status of academic research and
pinpoint the challenges that have yet to be addressed by academic work.
Departing from that, major drivers and enabling technologies are
identified, as well as concepts that can lead to a more sustainable
implementation of MC are proposed.
The review is based on
structured search in academic journals and books, which were retrieved
primarily from Scopus and Google Scholar databases, using as keywords
the main fields of interest of the study, namely: evolution of
manufacturing paradigms, issues in MC and personalisation environments,
the role of simulation for manufacturing, methods and technologies
related to product and production complexity, and inventory management
and capacity planning, among others. Academic peer-reviewed publications
related to the above fields were selected, ranging over a period of 30
years, from 1984 to 2015, with only a few notable exceptions. Sciences
that were considered in the search were: engineering, management,
business, and mathematics. The review was carried out in three stages:
(1) search in scientific databases with relevant keywords, (2)
identification of the relevant papers after reading their abstract, and
(3) full-text reading and grouping into research topics. Indicatively,
the frequency of results from a search with the keywords "mass
customisation" or "product personalisation" in the abstract, title, and
keywords of the article as obtained by the Scopus database is depicted
in Fig. 1.
Fig. 1 Frequency of appearance of the
keywords "mass customisation" and "personalisation" in the abstract,
title, and keywords of the article
![](https://learn.saylor.org/pluginfile.php/5619134/mod_book/chapter/50004/01.jpeg)
The above
figure also visualises the increase of interest on these topics by the
scientific community, and the establishment of MC as a distinct field of
research. The trend resembles a typical hype cycle. In the beginning,
the abstract concept of MC is born from the realisation that product
variety is increasing. Then, key enabling technologies, such as the rise
of the Internet, web-based collaboration means, and flexible
manufacturing systems act as a trigger in the spread of MC, quickly
reaching a peak during late 1990s and early 2000s. Until then, most
studies are concerned with management and strategic issues of MC,
failing to address critical MC implementation issues. Afterwards,
researchers realised that a series of sub-problems ought to be tackled
first, leading to research indirectly associated with MC (e.g.
investigation of product family modelling techniques). Nevertheless, MC
is here to stay, therefore, research interest on complete MC solutions
starts appearing after 2005 and continues up to the current date.
The
rest of the paper is structured as follows. Section 2 presents the
evolution of manufacturing paradigms and discusses the recent shift
towards customer-centred manufacturing. Section 3 performs a literature
review on major topics related to the life cycle of manufacturing
networks, together with the latest advances in ICT for supporting the
design, planning, and control of manufacturing networks. Section 4
summarises the challenges that need to be addressed, aided by a generic
view of the manufacturing landscape of the near future. Finally, Sect. 5
concludes the paper.
Evolution of manufacturing and current challenges
Evolution of manufacturing paradigms
Over time, manufacturing paradigms, driven by the pressure of the environment in which they operate, change in character and evolve in patterns (Fig. 2). The various patterns witnessed up to now can be roughly correlated to movements between three stages: (1) craft shops that employ skilled artisans, (2) long-linked industrial systems using rigid automation, and (3) post-industrial enterprises characterised by flexible resources and information intensive intellectual work. Prevailing manufacturing paradigms are, in chronological order of appearance, the following: craft production, American production, mass production, lean production, mass customisation, and global manufacturing. Apart from American production, all other paradigms are still "operational" today in different industrial sectors.
Fig. 2 Evolution of manufacturing paradigms (adapted from [11])
![](https://learn.saylor.org/pluginfile.php/5619134/mod_book/chapter/50003/02.jpeg)
By
studying these notable transitions, which are attributed to the
pressure applied by social needs, political factors, and advances in
technology, it is noticeable that factory systems and technologies have
been evolving in two directions. Firstly, they increased the versatility
of the allowable products' variety that they produced. This resulted in
numerous production innovations, design technology advances, and
evolution in management techniques. Secondly, companies have extended
factories like tools and techniques. Factories emerged from firms that
introduced a series of product and process innovations that made
possible the efficient replication of a limited number of designs in
massive quantities. This tactic is widely known as economies of scale. Factory systems replaced craft modes of production as firms
learned how to rationalise and product designs as well as standardise
production itself. Although factory organisations provided higher
worker and capital productivity, their structure made it difficult to
introduce new products or processes quickly and economically, or to meet
the demands of customers with distinctive tastes; factory-oriented
design and production systems have never completely replaced
craftsmanship or job shops even if the new technologies continue to
appear. The result, in economic, manufacturing, and design concepts, has
been a shift from simple economies of scale, as in the conventional MP
of a limited number of products, to economies of scope and customer
integration. It is clear that MP factories or their analogues are
not appropriate for all types of products or competitive strategies.
Moreover, they have traditionally worked best for limited numbers of
variants suited to mass replication and mass consumption. The craft
approach offers a less efficient process, at least for commodity
products, but remains necessary for technologies that are still new or
emerging and continues to serve specific market niches, such as for
tailoring products for individual needs and luxury or traditional items.
A categorisation of the different production concepts based on the
indicators system reconfigurability, demand volatility, and product
complexity is depicted in Fig. 3.
Fig. 3 Characterisation of production paradigms based on demand structure, product complexity, and product flexibility
![](https://learn.saylor.org/pluginfile.php/5619134/mod_book/chapter/50003/03.jpeg)
Today,
issues introduced by the shift of business models towards online
purchasing and customisation must be tackled in cost-efficient and
sustainable ways in order for companies to maintain their
competitiveness and create value. To respond to consumer demand for
higher product variety, manufacturers started to offer increased
numbers of product "options" or variants of their standard product.
Therefore, practice nowadays focuses on strategies and methods for
managing product, process, and production systems development that are
capable of supporting product variety, adaptability, and leanness, built
upon the paradigms of MC and product personalisation. The currently
widespread MC is defined as a paradigm for "developing, producing,
marketing and delivering affordable goods, and services with enough
variety and customisation that nearly everyone finds exactly what they
want". This is achieved mostly through modularised product/service
design, flexible processes, and integration between supply chain members. MC targets economies of scope through market segmentation, by
designing variants according to a product family architecture and
allowing customers to choose between design combinations. At the
same time, however, MC must achieve economies of scale, in a degree
compared to that of MP, due to the fact that it addresses a mass market.
Another significant objective for companies operating in an MC
landscape is the achievement of economies of customer integration in
order to produce designs that the customers really want. On the
other hand, personalised production aims to please individual customer
needs through the direct integration of the customer in the design of
products. The major differences between the prominent paradigms of MP,
MC, and personalisation in terms of goals, customer involvement,
production system, and product structure are depicted in Fig. 4.
Fig. 4 Differences between production paradigms (adapted from [20])
![](https://learn.saylor.org/pluginfile.php/5619134/mod_book/chapter/50003/04.jpeg)
A
research conducted in the UK related to automotive products revealed
that 61 % of the customers wanted their vehicle to be delivered within
14 days, whereas consumers from North America responded that they
could wait no longer than 3 weeks for their car, even if it is custom
built. Such studies point out the importance of responsiveness and
pro-activeness for manufacturers in product and production design.
During
the last 15 years, the number of online purchases has increased and
recent surveys show that 89 % of the buyers prefer shopping online to
in-store shopping. Web-based and e-commerce systems have been
implemented and have proved to be very effective in capturing the pulse
of the market. These web-based toolkits aim at providing a set of
user-friendly design tools that allow trial-and-error experimentation
processes and deliver immediate simulated feedback on the outcome of
design ideas. Once a satisfactory design is found, the product
specifications can be transferred into the firm's production system and
the custom product is subsequently produced and delivered to the
customer. Still online 2D and 3D configurators do not solve
practical issues such as the assembly process of these unique variants.
Although proposed approaches include e-assembly systems for
collaborative assembly representation and web-based collaboration
systems, the research in this area needs to be expanded in order to
provide tools for assembly representation and product variant
customisation. An additional constraint is that globalised design and
manufacturing often require the variants for local markets to be
generated by regional design teams, which use different assembly
software and source parts from different supply bases. The
incorporation of the customers' unique tastes in the product design
phase is a fairly new approach to the established ways of achieving
product variety and entails significant reorganisation, reconfiguration,
and adaptation efforts for the company's production system. Variety is
normally realised at different stages of a product life cycle. It can be
realised during design, assembly, at the stage of sales and
distribution, and through adjustments at the usage phase. Moreover,
variety can be realised during the fabrication process, e.g. through
rapid prototyping.
It should finally be noted that
naturally, even if the trends dictate a shift towards personalised
product requirements, it should always be considered that forms of
production such as MP cannot be abandoned for commodities and
general-purpose products, raw materials, and equipment. After all,
paradigms are shaped to serve specific market and economical situations.
Globalisation
Globalisation
in manufacturing activities, apart from its apparent advantages,
introduces a set of challenges. On the one hand, a globalised market
offers opportunities for expanding the sphere of influence of a company,
by widening its customer base and production capacity. Information and
communication technologies (ICT) and the Internet have played a
significant role to that. On the other hand, regional
particularities greatly complicate the transportation logistics and the
identification of optimum product volume procurement, among other.
Indicatively, the difficulty in forecasting product demand was
highlighted as early as in 1986 by the following observation from Intel
laboratories: when investigating the match between actual call off and
the actual forecast, they estimated that supply and demand were in
equilibrium for only 35 min in the period between 1976 and 1986. Enterprises started locating their main production facilities in
countries with favourable legislation and low cost of human labour;
thus, the management of the supply chain became extremely complex,
owing primarily to the fact that a great number of business partners
have to mutually cooperate in order to carry out a project, while being
driven by opportunistic behaviours. Thus, manufacturing networks need to
properly coordinate, collaborate, and communicate in order to survive.
On a manufacturing facility level, the impact of supply
chain uncertainties and market fluctuations is also considerable. The
design and engineering analysis of a complex manufacturing system is a
devious task, and the operation of the systems becomes even harder when
flexibility and reconfigurability parameters must be incorporated.
The process is iterative and can be separated into smaller tasks of
manageable complexity. Resource requirements, resource layout, material
flow, and capacity planning are some of these tasks, which even
after decomposition and relaxation remain challenging. In
particular, in the context of production for MC businesses, issues such
as task-sequence-dependent inter-task times between product families are
usually ignored, leading to inexact, and in many cases non-feasible,
planning and scheduling. Even rebalancing strategies for serial lines
with no other interdependencies is challenging, leaving ample room for
improvement in order for the inconsistencies between process planning
and line balancing to be minimised.
From a technological
perspective, the increased penetration of ICT in all aspects of product
and production life cycles enables a ubiquitous environment for the
acquisition, processing, and distribution of information, which is
especially beneficial for a globalised paradigm. With the introduction
of concepts like cyber physical systems (CPS) and Internet of things
(IoT) in manufacturing, new horizons are presented for improving
awareness, diagnosis, prognosis, and control. Also, the relatively new
paradigm of agent-based computation provides great potential for
realising desirable characteristics in production, such as autonomy,
responsiveness, distributiveness, and openness.
Manufacturing networks life cycle and mass customisation
In this section, the recent advances and the challenges presented during the life cycle of a manufacturing network are discussed. A typical modern manufacturing network is composed of cooperating original equipment manufacturer (OEM) plants, suppliers, distribution centres, and dealers that produce and deliver final products to the market. The topics discussed include supplier selection, supply chain coordination, initial network configuration, manufacturing network complexity, inventory management, capacity planning, warehousing, lot sizing, ICT support tools, and dynamic process planning, monitoring, and control. These topics are in line with the life cycle phases of a manufacturing network (Fig. 5).
Fig. 5 Manufacturing network life cycle
![](https://learn.saylor.org/pluginfile.php/5619134/mod_book/chapter/50005/05.jpeg)
Supplier selection
The building blocks of any manufacturing network are the cooperating companies. The significance of the selection of these stakeholders (supplier, vendors) has been indicated as early as in 1966 is known as the supplier selection problem. This decision-making problem is highly challenging since it goes beyond simple comparison of component prices from different suppliers. It is often decomposed into sub-problems of manageable complexity, such as formulation of criteria for the selection, qualification of partners, final selection, and feedback verification. In Fig. 6, the decomposition of the supplier selection problem into small more manageable problems is presented, together with indicative methods for solving these sub-problems.
Fig. 6 Supplier selection problem, its decomposition into small more manageable problems, and indicative methods for solving them
![](https://learn.saylor.org/pluginfile.php/5619134/mod_book/chapter/50005/06.jpeg)
The
supplier selection problem becomes even more complicated in the era of
MC since a certain level of adaptability and robustness is necessary
when operating within a volatile and rapidly changing environment. The
most commonly used criteria in supplier selection studies include
quality and performance. However, when having to deal with
unpredictability and fluctuating demand, which are common in MC,
additional factors need to be considered such as management
compatibility, transparency of operations, strategic direction,
reliability, and agility. While trying to adhere to
eco-friendliness directives, frameworks
incorporate environmental footprint criteria to green supply chain
design. Moreover, several other criteria may be relevant according to
the design and planning objectives of a niche supply chain, which could
be identified using data mining methods.
The Internet and
web-based platforms are used in recent years in order to counterbalance
uncertainty, monitor altering parameters (e.g. weather in supply
routes), and proactively adapt to changes. Moreover, several
proposed supplier selection models incorporate the relative importance
of the supplier selection factors depending on the types of targeted MC
implementation, e.g. for the component-sharing modularity type of MC,
the requirements for selecting suppliers would not be the same as the
component-sweeping modularity implementation type. Like in the case
of a stable low variety production, the analytic hierarchy process
(AHP) is commonly used as a means to solve the multi-criteria
decision-making problem of supplier selection. Incorporating uncertain
information about the real world, essentially extending the
Dempster–Shafer theory, the D-AHP method for
solving the supplier selection problem. The suggested D numbers
preference relation encapsulates the advantages of fuzziness and handles
possible incomplete and imprecise information, which is common in
human-driven systems such as supply chains. Similarly, a combined
analytic hierarchy process - quality function deployment (AHP–QFD)
framework that handles uncertain information,
selects suppliers, and allocates orders to them. A multi-criteria
decision-making method to support the identification of
business-to-business (B2B) collaboration schemes, especially for
supplier selection.
Supply chain coordination
The
literature on organisational knowledge creation points out that
"coordination" plays an important role in combining knowledge from
stakeholders, while it also mediates the relationship between
product modularity and MC. A report on coordination mechanisms for
supply chains.
Concerning coordination in
supply chains, in general, two topologies are studied, namely the
centralised and the decentralised one (Fig. 7). In the first, the
coordination decisions are taken by a central body, often the leading
supply chain OEM, whereas in the second, each member independently makes
its own operational decisions. The decentralised topology has been
proven to improve the performance in the context of MC. A
supply chain that is commissioned to provide a variety of customised
products requires a total systems approach to managing the entire flow
of information, materials, and services in fulfilling customer demand. Further incentives have to be provided to the members, so as to
entice their cooperation through the distribution of the benefits of the
coordination for instance.
Fig. 7 Centralised and
decentralised supply chain topologies. In a centralised topology,
material and information move only downstream. In the decentralised one,
material/information can be transferred both upstream and downstream to
better serve customisation, personalisation, and/or regionalisation
![](https://learn.saylor.org/pluginfile.php/5619134/mod_book/chapter/50005/07.jpeg)
The
need for adaptation to the new MC requirements has led to the
definition of a novel framework for autonomous logistics processes. The
concept of autonomous control "describes processes of decentralised
decision-making in heterarchical structures, and it presumes interacting
elements in non-deterministic systems, and possess the capability and
possibility to render decisions independently". However, regardless
the topology, the alignment of the objectives of the different
collaborating organisations in order to successfully carry out projects,
optimise system performance, and achieve mutual profits is
indispensable. While an action plan suffices for the coordination
of a centralised supply chain, it is inadequate with a decentralised one since entities tend to exhibit opportunistic behaviour.
Nevertheless, in terms of overall network performance, decentralised
topologies have shown great benefits for serving the mass customisation
paradigm.
Initial manufacturing network configuration
The initial manufacturing network configuration must consider the long-term needs of cooperation and often determines its success. In a constantly changing environment, the configuration of the manufacturing network must be, therefore, flexible and adaptable to external forces. The problem has been extensively addressed in the literature using approaches classified in two main categories, namely approximation (artificial intelligence, evolutionary computation, genetic algorithms, tabu search, ant colony optimisation, simulated annealing, heuristics, etc.) and optimisation techniques (enumerative methods, Lagrangian relaxation, linear/nonlinear integer programming, decomposition methods, etc.) and their hybrids (Fig. 8). Focusing on agile supply chains, a hybrid analytic network process mixed-integer programming model with uttermost aim the fast reaction to customer demands. Fuzzy mathematical programming techniques have been employed to address the planning problems for multi-period, multi-product supply chains. A coloured Petri Nets approach for providing modelling support to the supply chain configuration issue. A dynamic optimisation mathematical model for multi-objective decision-making for manufacturing networks that operated in a MC environment.
Fig. 8 Issues to be considered during the initial manufacturing network configuration and indicative methods used
![](https://learn.saylor.org/pluginfile.php/5619134/mod_book/chapter/50005/08.jpeg)
Still,
the accuracy of planning ahead in longer horizons is restricted. The
incorporation of unpredictable parameters in the configuration through a
projection of the possible setting of the network in the future may
lead to unsafe results.
Inventory management/capacity planning/lot sizing
Inventories
are used by most companies as a buffer between supply chain stages to
handle uncertainty and volatile demand. Prior to the 1990s, where the
main supply chain phases, namely procurement, production, and
distribution, were regarded in isolation, companies maintained buffers
of large inventories due to the lack of regulatory mechanisms and
feedback. The basis for manufacturing and inventory planning was
relatively safe forecasts. However, in the era of customisation the
basis is actual orders and the pursuit is minimisation of inventories.
These requirements constitute inventory management and capacity planning
functions very important for a profitable MC implementation.
In
complex distributed systems such as modern manufacturing companies with a
global presence, the question of optimal dimensioning and positioning
of inventory emerges as a challenging research question. Various
strategies for inventory planning have been reported based on how the
underlying demand and return processes are modelled over time, thus
making a distinction between constant, continuous time-varying, and
discrete time-varying demand and return models. Integrated capacity
planning methods encompassing stochastic dynamic optimisation models
over volatile planning horizons exhibit high performance in the context
of MC and personalisation. The DEWIP (decentralised WIP) control
mechanism, focusing on establishing control loops
between work centres for adjusting the WIP levels dynamically. Its
performance was assessed against other well-accepted systems such as
LOOR, Conwip, and Polca. Methods used for solving the capacitated lot
sizing problem are indicatively shown in Fig. 9.
Fig. 9 Methods (indicative) used for solving the capacitated lot sizing problem
![](https://learn.saylor.org/pluginfile.php/5619134/mod_book/chapter/50005/09.jpeg)
In
particular, in just-in-time (JIT) environments, MC impacts the amount
of inventory that needs to be carried by firms that supply many part
variants to a JIT assembly line. In addition, the supply of parts is
performed either on constant order cycles or more commonly under
non-constant cycles. The goal chasing heuristic, pioneered within
the Toyota production system, seeks to minimise the variance between the
actual number of units of a part required by the assembly line and the
average demand rate on a product-unit-by-product-unit basis, while
applying penalties for observed shortages or overages. Of course,
information sharing and partner coordination systems are a prerequisite
for JIT procurements. For instance, DELL, which achieved a highly
coordinated supply chain to respond to MC, communicated its inventory
levels and replenishment needs on an hourly basis with its key suppliers
and required from the latter to locate their facilities within a 15-min
distance from DELL facilities. Another consideration during
inventory management is the type of postponement applied in a company.
Studies have shown that postponement structures allow firms to meet the
increased customisation demands with lower inventory levels in the case
of time postponement (make-to-order), or with shorter lead times in the
case of form postponement. Also, an assemble-to-order process, a
variation of form postponement, does not hold inventory of the finished
product, while in form postponement, finished goods inventory for each
distinct product at the product's respective point of customisation is
kept. An indicative example is given in the case of Hewlett
Packard, where using form postponement, the company achieved the
postponement of the final assembly of their DeskJet printers to their
local distribution centres.
Logistics management
Logistics can play a crucial role in optimising the position of the customer order decoupling point and balance between demand satisfaction flexibility and productivity. In a customer-centric environment, the supply chain logistics must be organised and operated in a responsive and at the same time cost-effective manner. Customisation of the bundle of product/services is often pushed downstream the supply chain logistics, and postponement strategies are utilised as an enabler for customisation. Maintaining the product in a neutral and non-committed form for as long as possible, however, implicates the logistics process. Traditional logistics management systems and strategies need to be revisited in the context of customisation, since distribution activities play a key role in achieving high product variety, while remaining competitive. Most OEMs form strategic alliances with third-party logistic (TPLs) companies. The introduction of TPLs in the supply chain serves two purposes. First, it acts as a means of reducing the complexity of management for an OEM through shifting the responsibilities of transportation, and in many times customisation, to the TPLs. Second, it extends the customisation capabilities as TPLs can actively implement postponement strategies. Postponement strategies with logistics as an enabler are located at the bottom of Fig. 10 and can serve all types of customisation, from plain shipment to order up to extremes of engineer-to-order or personalisation.
Fig. 10 Postponement strategies for different supply chain structures and logistics
![](https://learn.saylor.org/pluginfile.php/5619134/mod_book/chapter/50005/10.jpeg)
Moreover,
the management of logistics is a process inherently based on
communication and collaboration. Developments of either
function-specific or all-in-one ICT solutions targeted on logistics. Tools for warehouse and transportation management,
ERP, supply chain management (SCM), and information sharing are reported
under the umbrella of e-logistics. The concept of virtual logistics is
also proposed for separating the physical and digital aspects of
logistics operations, having Internet as an enabling means to
handle ownership and control of resources.
Supply chain control
The
information transferred from one supply chain tier to the next in the
form of orders is often distorted, a phenomenon known as the bullwhip
effect. In particular, when customer demand is volatile such as the case
is in MP, the bullwhip effect misguides upstream members of the supply
chain in their inventory and production decisions. Nevertheless,
the performance of the supply chain is highly sensitive to the control
laws used for its operation. The application of the wrong control policy
may have as a result the amplification of variance instead on its
minimisation. Dynamic modelling approaches have been proposed to manage
supply chains, accounting for the flow of information and material, to
capture the system dynamics. Multi-agent approaches for modelling
supply chain dynamics. Software components known as
agents represent supply chain entities (supplier, dealers, etc.), their
constituent control elements (e.g. inventory policy), and their
interaction protocols (e.g. message types). The agent framework utilises
a library of supply chain modelling components that have derived after
analysis of several diversified supply chains. For instance, a novel
oscillator analogy in presented for modelling the manufacturing
systems dynamics. The proposed analogy considers a single degree of
freedom mass vibrator and a production system, where the oscillation
model has as input forces, while the manufacturing system has demand as
excitation. The purpose is to use this simple oscillator analogy to
predict demand fluctuations and take actions towards alignment.
Another
necessity in supply chain control is the traceability of goods.
Traceability methods, essential for perishable products and high-value
shipments, exploit the radio frequency identification (RFID) technology
during the last years. A traceability system that traces lots
and activities is proposed by Bechini et al. The study examines
the problem from a communication perspective, stressing the need to use
neutral file formats and protocols such as XML (extended markup
language) and SOAP (simple object access protocol) in such applications.
The emerging technology of IoT can provide ubiquitous traceability
solutions. Combining data collection methods based on wireless sensor
network (WSN) with the IoT principles, the method can
support the traceability of goods in the food industry. In a similar
concept, the role of an IoT infrastructure for order fulfilment in a
collaborative warehousing environment. The IoT
infrastructure is based on RFID, ambient intelligence, and multi-agent
system, and it integrates a bottom-up approach with decision support
mechanisms such as self-organisation and negotiation protocols between
agents based on a cooperation concept.
Supply chains formed for
servicing customisation are more complex as structures and less
predictable in their dynamic behaviour than stable traditional supply
chains. Recent complexity studies deal with the emerging aspects of
increasing complexity of manufacturing activities and the dynamic nature
of supply chains. The importance of managing the complexity in
supply chains is evident, as recent studies depict that lower
manufacturing network complexity is associated with reduced costs and
overall network performance. A complete and comprehensive
review of complexity in engineering design and manufacturing.
Simulation and ICT support systems for manufacturing networks life cycle
Robust
and flexible ICT mechanisms are rendered necessary for improving
performance in each of the previous life cycle aspects of supply chains
and for bridging inter- and intra-enterprise collaboration environments.
Digital enterprise technologies in general represent an established,
new synthesis of technologies and systems for product and process
development and life cycle management on a global basis that brings
many benefits to companies. For instance, the benefits offered by the
adoption of virtual engineering through the life cycle of production are
shown in Fig. 11. To manage the huge portfolio of products and
variety, as well as tracking the expanding customer base, ERP and CRM
suites are necessary tools. Additionally, cloud technology is already
revolutionising core manufacturing aspects and provides ample benefits
for supply chain and manufacturing network life cycle. Cloud technology
and the IoT are major ICT trends that will reshape the way enterprises
function in the years to come.
Fig. 11 Increased efficiency through virtual engineering approaches
![](https://learn.saylor.org/pluginfile.php/5619134/mod_book/chapter/50005/11.jpeg)
Simulation for manufacturing network design
Literature
on ICT-based systems for improving manufacturing networks is abundant
and highlights the need for increased penetration of ICT systems in
design, planning, and operation phases. A simulation-based method to
model and optimise supply chain operations by taking into consideration
their end-of-life operations is used to evaluate the capability of OEMs
to achieve quantitative performance targets defined by environmental
impacts and life cycle costs. A discrete event simulation model of
a capacitated supply chain is developed and a procedure to dynamically
adjust the replenishment parameters based on re-optimisation during
different parts of the seasonal demand cycle is explained. A model
is implemented in the form of Internet-enabled software framework,
offering a set of characteristics, including virtual organisation,
scheduling, and monitoring, in order to support cooperation and flexible
planning and monitoring across extended manufacturing enterprise.
Furthermore, the evaluation of the performance of automotive
manufacturing networks under highly diversified product demand is
succeeded through discrete event simulation models with the use
of multiple conflicting user-defined criteria such as lead time, final
product cost, flexibility, annual production volume, and environmental
impact due to product transportation. Finally, the application of the
mesoscopic simulation approach to a real-world supply chain example is
illustrated utilising the MesoSim simulation software.
Existing
simulation-based approaches do not tackle the numerous issues of
manufacturing network design in a holistic integrated manner. The
results of individual modules used for tackling network design
sub-problems often contradict each other because they refer to not
directly related manufacturing information and context (e.g. long-term
strategic scheduling vs. short-term operational scheduling), while
harmonising the context among these modules is challenging. This
shortcoming hinders the applicability of tools to real manufacturing
systems as it reduces the trustworthiness of results to the eyes of the
planner among other reasons.
Enterprise resource planning
An
ERP system is a suite of integrated software applications used to manage
transactions through company-wide business processes, by using a common
database, standard procedures, and data sharing between and within
functional areas. Such ICT systems entail major investments and
involve extensive efforts and organisational changes in companies that
decide to employ them. ERP systems are becoming more and more prevalent
throughout the international business world. Nowadays, in most
production distribution companies, ERP systems are used to support
production and distribution activities and they are designed to
integrate and partially automate financial, resource management,
commercial, after-sale, manufacturing, and other business functions into
one system around a database.
A trend, especially in the
mid-market, is to provide specific ERP modules as services. Such need
generates the challenge for ERP system providers to offer mobile-capable
ERP solutions. Another issue is the reporting and data analysis, which
grows with the information needs of users. Research in big data
analytics and business intelligence (BI) should become more tightly
integrated with research and applications of ERP.
Customer relationship management
In
Internet-based retailing, which is the preferred business model
followed in MC, customer information management is a necessity. In
particular, exploiting consumer data, such as purchase history,
purchasing habits, and regional purchasing patterns, are the cornerstone
of success for any company active in MC. In business-to-business and
business-to-customer, CRM suites are thus indispensable. According to
Strauss and Frost, CRM involves, as a first step, research to gain
insight so as to identify potential and current customers. In a second
step, customer information is used to differentiate the customer base
according to specific criteria. Finally, the third step involves
customised offerings for those customers that are identified as
"superior" from the previous phase, enabling thus, the targeted offering
of customised products. During the first step of identification of
customers, market research and consumer behaviour models are used. In a
second phase, for establishing differentiation techniques, data mining
and KPIs assessment are used. Finally, for fine-tuning customisation
options, information such as price, variants, promotions, and regions
are examined.
As Internet becomes ubiquitous in business,
CRM has been acknowledged as an enabler for better customisation since
it offers management of the new market model less disruptively.
Internet-enabled CRM tools also bring the customer closer to the
enterprise and allow highly responsive customer-centred systems without
significant increase in costs. e-CRM implementations have been
assessed in the study. Noticeably, most major CRM suite vendors
have started providing cloud-based services, a business model that suits
SMEs that cannot afford huge ICT investments. Based on the balanced
scorecard method, the study assessed e-CRM performance using 42
criteria in a number of companies. The results show that a successful
CRM implementation is associated with tangible outcomes, such as
improvements in financial indicators, customer value, brand image, and
innovation. Finally, the latest generation of CRM tools, referred to as
social CRM, exploit social networking technology to harness information
about customer insights and engagement.
Cloud computing and manufacturing
A
comprehensive definition of cloud computing is provided by the National
Institute of Standards and Technology: "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".
Several applications have been reported in recent years where a cloud
infrastructure is used to host and expose services related to
manufacturing, such as: machine availability monitoring,
collaborative and adaptive process planning, online tool-path
programming based on real-time machine monitoring, manufacturing
collaboration and data integration based on the STEP standard, and
collaborative design.
The benefits of cloud for improving
manufacturing network performance are numerous (Table 1). Cloud can
offer increased mobility and ubiquitous information to an enterprise
since the solutions it offers are independent of device and location.
Moreover, computational resources are virtualised, scalable, and
available at the time of demand. Therefore, the intensive costs for
deploying high-performance computing resources are avoided. In addition
to that, purchasing the application using the model software as a
service is advantageous for SMEs who cannot afford the huge investments
that commercial software suites entail. However, there are some
considerations also (Table 1). A main challenge for the adoption of
cloud in manufacturing is the lack of awareness on security issues. This
major issue can be addressed using security concepts and inherently
safe architectures, such as privately deployed clouds. The security
concept must include availability of ICT systems, network security,
software application security, data security, and finally operational
security. Considerable funding is spent by the global security software
market, in order to alleviate security issues. Recent reports show that
the expenditure on cloud security is expected to rise 13-fold by 2018. Moreover, there is the possibility of backlash from
entrenched ideas, manufacturing processes and models caused by the
hesitation for the adoption of innovative technology. Finally, the lack
of standardisation and regulation around cloud hinders its acceptance by
the industry.
Table 1 Benefits and drawbacks of cloud technology for manufacturing
Benefits | Drawbacks |
---|---|
Increased mobility that allows decentralised and distributed SCM | Lack of standardisation and protocols create hesitation in adoption of Cloud solutions |
Ubiquitous access to information context empowering decision-making | Security and lack of awareness on security issues, especially in SMEs, that are part of supply chains/clusters |
Device and location independent offering context-sensitive visualisation of crucial data relevant to the mfg. network | Privacy issues generate legal concerns, identity management, access control, and regulatory compliance |
Hidden complexity permits the diffusion of ICT solutions even to traditional, averted by disruptive solutions, sectors | Dependence on the cloud provider (provider stops providing services, absence of contracts/regulation) |
Virtualised and scalable on-demand computational resources (problems of varied computational complexity) | Loss of control over data (assuring smaller companies that their data are not visible by anyone in the supply chain, but the owner is challenging) |
Low cost for SMEs that cannot afford huge ICT investments and lack the know-how to maintain them |
Challenges for future manufacturing
MC provides a set of enabling concepts and methods for providing the customer with products they desire and for organising production resources and networks to realise these products. However, on a practical strategic, tactical, and operational level, the tools for the realisation of MC are under development and refinement and a number of issues related to the design of manufacturing networks and their management are still not tackled in a holistic integrated manner. Several particular challenges need to be addressed as described below. Possible solutions are also proposed in the context of supporting a more efficient implementation of MC and personalisation.
Challenges for the manufacturing network life cycle
Regarding
supplier selection, existing frameworks that handle both selection of
suppliers, order allocation, and capacity planning are rare in the
literature. Therefore, inconsistencies between the design phase and the
actual implementation of the supply chain are a common issue. The
problem most commonly treated jointly with supplier selection is the
order allocation problem, among
other. Moreover, several studies point out the difficulties of
coordination between large networks of stakeholders. Potential solutions
in novel approaches to tackling the issues generated in supply chain
coordination for the procurement of customised products are proposed, where organisation flatness is proposed as a mediator
for enhancing MC capability. Flatness in cross-plant and
cross-functional organisation alleviate the need to decisions to pass
through multiple layers of executives, simplifying coordination and
information sharing. Among the several challenges for configuring
robust manufacturing networks to satisfy MC are the need for frameworks
that handle the entire order fulfilment life cycle (from product design
to delivery), methods to allow easy modelling and experimentation of
what-if scenarios and deeper examination of the impact of product
variety on the performance of manufacturing networks. On the field of
SCM, identifying the benefits of collaboration is still a big challenge
for many. The definition of variables, such as the optimum number of
partners, investment in collaboration, and duration of partnership, are
some of the barriers of healthy collaborative arrangements that should
be surpassed. Available solutions for lot sizing are following
traditional approaches and are not able to address the increasing
complexity of problems arising in the modern manufacturing network
landscape. The economic order quantity (EOQ), established for more than
100 years, still forms the basis of recent lot sizing practices. In
setups of complex and changeable products, the problem of lot sizing
becomes extremely complex. Nevertheless, the optimality of inventory and
capacity planning is often neglected due to increased complexity of the
supply chain problems which comes with higher priority. For instance,
in multi-agent manufacturing systems, each agent resolves inventory
issues in its domain partition level, without clear global optimisation
overview. Furthermore, the broader role of logistics capabilities
in achieving supply chain agility has not been addressed from a holistic
conceptual perspective. Therefore, an open research question is
the relationship between logistics capabilities and supply chain
agility. Regarding ERP suites, apart from their apparent benefits, the
reported successful implementations of ERP systems are limited when
considering implementation costs and disruptions caused in production. One reason for the low success rates in ERP implementations is
attributed to the organisation changes needed for the industry that
disrupt normal flow of business. Another reason is that production
planning, a core function handled by currently deployed closed-loop
MRPII (manufacturing resource planning) and ERP suites, is performed
through the fundamental MRP (material requirements planning). This leads to the generation of low-detail shop-floor schedules,
assuming infinite production capacity and constant time components, thus
leading to inflated lead times. Challenges on the technological
level of ERP systems include delivery of software as a service, mobile
technology, tightly integrated business intelligence, and big data
analytics. Challenges in the field of product data management
(PDM) are related to the efficiency of these systems with regard to
studying factors that affect the accessibility of product data, for
instance, the nature of data in different timeframes of a development,
the relationship between the maturity of the data, and the probability
of them being modified. The deployment and tight integration of
product life cycle management (PLM) tools must also be considered since
they bring an abundance of benefits against current manufacturing
challenges. Yet these benefits are still not appreciated by many
industrial sectors, mainly due to the following reasons: (1) they are
complex as a concept and understanding their practical application is
difficult, (2) they lack a holistic approach regarding the product life
cycle and its underlying production life cycle and processes, and (3)
the gap between research and industrial implementation is discouraging. Concerning CRM, although data rich markets can exploit the
feedback of consumers through social networks to identify user polarity
towards a product–service, improve its design, and refine a product
service system (PSS) offering, only few initiatives have tapped that
potential.
Further challenges that are related indirectly to the
previous aspects are discussed hereafter. Concerning individual
disparate software modules, it is often observed that they contradict
each other because they refer to not directly related manufacturing
information and context. The harmonisation, both on an input/output
level and to the actual contents of information, is often a mistreated
issue that hinders the applicability of tools to real-life manufacturing
systems. Limitations of current computer-aided design (CAD) tools
include: the complexity of menu items or commands, restricted active and
interactive assistance during design, and inadequate human–computer
interface design (focused on functionality). To fulfil the needs
of modern manufacturing processes, computer-aided process planning
should be responsive and adaptive to the alterations in the production
capacity and functionality. Nowadays, conventional computer-aided
process planning (CAPP) systems are incapable of adjusting to dynamic
operations, and a process plan, created in advance, is found improper or
unusable to specific resources. Highlighted challenges for life
cycle assessment (LCA) are modularisation and standardisation of
environmental profiles for machine tools, as well as modelling of
"hidden flows" and their incorporation in value stream mapping tools. Regarding knowledge management and modelling, reusable
agent-oriented knowledge management frameworks, including the
description of agent roles, interaction forms, and knowledge
description, are missing. Moreover, ontologies used for knowledge
representation have practical limitations. In case an ontology is
abstract, its applicability and problem-solving potential may be
diminished. On the other hand, in the case of very specific ontologies,
reasoning and knowledge inference capacities are constrained.
Furthermore, in the turbulent manufacturing environment, a key issue of
modern manufacturing execution systems is that they cannot plan ahead of
time. This phenomenon is named decision myopia and causes undoubtedly
significant malfunctions in manufacturing. In the field of layout
design and material simulation, some commercial software can represent
decoupling data from 3D model and export them in XML or HTML format.
While this is an export of properties, it cannot fully solve the
interoperability and extensibility issues since the interoperability
depends on how the different software and users define contents of data
models. Concerning material flow simulation, it can be very
time-consuming to build and verify large models with standard
commercial-off-the-shelf software. Efficient simulation model generation
will allow the user to simplify and accelerate the process of producing
correct and credible simulation models. Finally, while the steady
decline in computational cost renders the use of simulation very
cost-efficient in terms of hardware requirements, commercial simulation
software has not kept up with hardware improvements.
Solutions for addressing the challenges in the future manufacturing landscape
A
view of the manufacturing system of the near future that incorporates
the latest trends in research and ICT developments and can better
support MC is shown in Fig. 12. It is envisioned that, fuelled by
disruptive technologies such as the IoT and cloud technology, entities
within supply chains will exchange information seamlessly, collaborate
more efficiently, and share crucial data in real time. Data acquisition,
processing, and interpretation will be supported by wireless sensor
networks. The information will be available on demand and on different
degrees of granularity empowered by big data analytics. Drilling down to
specific machine performance and zooming out to supply chain overview
will be practically feasible and meaningful. The distinction between the
physical and the digital domains will become less clear. Besides,
physical resources are already considered as services under the cloud
manufacturing paradigm. A tighter coupling and synchronisation between
the life cycles of product, production, resources, and supply chains
will be necessary, while the distinction between cyber and physical
domains will become hazier. A discussion on potential directions for
adhering to this view of manufacturing is provided hereafter.
Fig. 12 View of manufacturing in the near future
![](https://learn.saylor.org/pluginfile.php/5619134/mod_book/chapter/50006/12.jpeg)
New
technologies and emerging needs render traditional SCM and
manufacturing network design models obsolete. To support manufacturing
network design, planning, and control, a framework that integrates,
harmonises, processes, and synchronises the different steps and
product-related information is needed. The framework will be capable of
supporting the decision-making procedure on all organisation levels in
an integrated way, ranging from the overall management of the
manufacturing network, down to the shop-floor scheduling fuelled by big
data analytics, intuitive visualisation means, smart user interfaces,
and IoT. An alignment and coordination between supply chain logistics
and master production schedules with low-level shop-floor schedules is
necessary for short-term horizons. The framework needs not be restricted
on a particular manufacturing domain; since it is conceived by
addressing universal industrial needs, its applicability to contemporary
systems is domain-independent. The constituents of the framework are
described hereafter.
The system will be supported by automated
model-based decision-making methods that will identify optimum (or
near-optimum) solutions to the sub-problems identified above, such as
for the problem of the configuration of manufacturing networks capable
of serving personalised product–services. The method must consider the
capabilities of the manufacturing network elements (suppliers of
different tiers, machining plants, assembly plants, etc.) and will
indicate solutions to the warehouse sizing problem, to the manufacturing
plant allocation, and to the transportation logistics. The decision
support framework requires interfacing with discrete event simulation
models of manufacturing networks and assessment of multiple conflicting
and user-defined performance indicators.
The joint handling of
order allocation, supplier selection, and capacity planning is necessary
to alleviate inconsistencies between the supply chain design and
implementation phases under a flatness concept. The incorporation of the
entire order fulfilment life cycle is additionally envisioned, enhanced
with methods that allow easy modelling and experimentation on what-if
scenarios. The relationship between logistics capabilities and supply
chain agility can also be revealed through this holistic view of the
constituents of the supply chain.
Regarding SCM, collaboration
concepts based on cloud computing and cloud manufacturing are a game
changer. Through the sharing of both ICT as well as manufacturing
resources, SMEs can unleash their innovation potential and thus compete
more easily in the global market.
Further to that, the
measurement and management of the manufacturing network complexity
should be considered as a core strategic decision together with
classical objectives of cost, time, and quality. Handling a variety of
market excitations and demand fluctuations is the standard practice even
today in many sectors, while this trend is only bound to intensify. In
parallel, a risk assessment engine should correlate complexity results
and leverage them into tangible risk indicators. Complexity can then be
efficiently channelled through the designed network in the less risky
and unpredictable manner.
To address the increasing complexity of
problems arising in the modern manufacturing network landscape, the lot
sizing and material planning need to be tightly incorporated to the
production planning system. The consideration of capacitated production
constraints is needed in order to reflect realistic system attributes. A
shared and distributed cloud-based inventory record will contain
information related to MRP and ERP variables (e.g. projected on-hand
quantities, scheduled order releases and receipts, changes due to stock
receipts, stock withdrawals, wastes and scrap, corrections imposed by
cycle counting, as well as static data that describe each item
uniquely). This record should be pervasive and contain dataset groups
relevant to intra-departmental variables, as well as datasets visible
only to suppliers and relevant stakeholders, in order to increase the
transparency of operations.
The mistreated issues of deployment
and tight integration of PLM, ERP, and CRM tools must also be tackled
through interfacing of legacy software systems and databases for
seamless data exchange and collaboration. Software as a service PLM,
ERP, and CRM solutions available to be purchased per module will be the
ideal ownership model since it allows greater degree of customisation of
solutions, more focused ICT deployment efforts, and reduced acquisition
costs. CAD/CAM, PDM, and MPM (manufacturing process management) systems
and databases will be interfaced and interact with digital mock-ups of
the factory and product–services solutions as well for synchronising the
physical with the digital worlds. In addition, the knowledge capturing
and exploitation is pivotal in the proposed framework. Product, process,
and production information is acquired from production steps and is
modelled and formalised in order to be exploited by a knowledge reuse
mechanism that utilises semantic reasoning. This mechanism is comprised
of an ontological model that is queried by the knowledge inference
engine and allows the retrieval of knowledge and its utilisation in
design and planning phases. The developments should also mediate the
deeper examination of the impact of product variety on the performance
of manufacturing networks.
In parallel, there is an urgent need
of standardisation and harmonisation of data representation for
manufacturing information, for example: the product information (BoM,
engineering-BoM and manufacturing-BoM), the manufacturing
processes (bill of processes - BoP) including the manufacturing
facilities layout, the associated relations (bill of relations - BoR),
and related services (Bill of Services - BoS) should be pursued through a
shared data model. Moreover, the product complexity needs to be
assessed based on functional product specifications using, for instance,
design structure matrices (DSM), which incorporate components
(BoM), the required manufacturing and assembly processes (BoP) including
sequences/plans, relationships (BoR), and the accompanying services
(BoS). The complexity of the product in relation to the manufacturing
network and service activities (impact on delivery time and cost, and
effect on the overall reliability) will be quantified and will be
incorporated in the decision-making process.
Last but not least,
it should be noted that the components of the proposed framework must be
offered following a software as a service delivery method and not as a
rigid all-around platform. The framework should act as a cloud-based hub
of different solutions, offering web-based accessibility through a
central "cockpit" and visualisation of results through common browser
technology and handheld devices (tablets, smartphones, etc.).
Conclusions
The ability to customise a product/service is offered
to consumers for many years now, while truly unique products will be
requested in the near future by users around the globe using the
Internet as a means of integration in the design process. In addition,
the shortening of life cycles and time to market, increased outsourcing,
manufacturing at dispersed sites, and the diverse cooperation in
networks increase the complexity of production. Agility,
reconfigurability, and synchronisation from process up to supply chain
levels are necessary in order for companies to respond effectively to
the ever-changing market needs. Driven by the ever-increasing need
to reduce cost and delivery times, OEMs are called to efficiently
overcome these issues by designing and operating sustainable and
efficient manufacturing networks.
This work reviewed the existing
literature related to the basic aspects of a manufacturing network life
cycle within the MC landscape. The focus was to study existing
practices and highlight the gaps in the current approaches related to
these aspects of manufacturing network design, planning, and operation.
Afterwards, the identification of future directions of academic and
industrial research is proposed. Departing from that, major drivers and
enabling technologies are identified and concepts that can lead to a
more sustainable implementation of MC are proposed.
Summing up,
the theoretical foundations of MC have been laid for many years now
[150]. Still, there is an apparent gap between the theoretical and the
actual application of MC, and bridging this gap is a challenging task
that needs to be addressed. A safe conclusion reached is that the
complexity generated in manufacturing activities due to the exploding
product variety requires a systematic approach to be considered during
the design, planning, and operating of the entire manufacturing system. All in all, piecemeal digitalisation of manufacturing network is
not a viable option; revisiting of the entire supply and manufacturing
network life cycle is essential for sustainability. The pursuit for a
smoother, more efficient, more rewarding, and eco-friendly manufacturing
is ongoing.