The Life Cycle of Manufacturing Networks in the Mass Customisation Era
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?
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.).