Traditionally, a new concept of smart factory that

Traditionally, manufacturing has reduced
costs and achieved economies of scale through mass production. The development
of multiple technologies such as computer-aided-design(CAD) has led to more
advanced manufacturing processes, which have improved personalized demand and
product quality. In the meantime, the emergence of Additive Manufacturing (AM)
presented a new concept of the integration concept of manufacturing 1. Based
on the development of various technologies and the AM concept, manufacturers
began to focus on providing consumers with better quality products. Therefore,
many manufacturing companies have focused on mass customization to achieve the
economy of scope 2.  From the 21st
century, there is a new paradigm throughout the manufacturing industry, such as
serviceoriented architecture (SOA) 3 and IoT 4. Especially, with the
development of communication technology, the rise of IoT technology has
introduced a new concept of integration by facilitating information 4. This
new concept means interconnected supply chains that consider multiple factories
in a network at a plant through the new technologies 5. There are also many
research for resource allocation for virtual manufacturing 6 and agile
manufacturing 7.  In a series of
manufacturing developments, consumers are demanding more and more customized
demand, and to meet these needs, manufacturing enterprises are building
collaborative processes through various technologies.

2.1. Smart Supply Chain Management

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2. Literature review

As we enter the fourth industrial
revolution, manufacturing is in a new phase. In fact, some factories are trying
to reduce costs by combining various advanced technologies. In particular,
Internet of Things (IoT) and Cloud technologies are the most popular in the
manufacturing industry. These two technologies are regarded as cutting-edge
technologies that enable free resource sharing between factories, and are being
integrated through various experiments. If these advanced technologies enable
smooth sharing of resources between factories, a new concept of smart factory
that is different from the existing one can be considered. We call these
factories the Connected Smart Factory Network (CSFN). The CSFN is an
environment in which a supplier, factory or customer in the network are
connected through the cloud. With the CSFN, participants who are members of the
network can freely share information and each member can make better decisions.
Consumers can be more specific about their needs through the network. They want
to customize consumption in the network. Factories share the resources and
availability of each other to build an optimal supply chain. It also
establishes an inter-factory network to respond flexibly to the consumer’s
customized needs. This CSFN should be easy to connect and disassemble.
Suppliers can make cost-effective decisions by making optimal decisions based
on these interfactory networks and customized consumer needs. Thus this enables
better planning and collaboration of the supply chain.  We refer to the new supply chain with a
concept of the CSFN as the smart supply chain. The smart supply chain must be
able to respond quickly to the small demands of personalized products and have
flexible connections between factories. 
On the other hand, many factories have individual and diverse
uncertainties, such as weather, power outages, and workers’ ability.
Customization in the smart supply chain also implies uncertainty. Factory
production capacity is always irregular, and irregular supply chains, especially
with customized consumption, can increase the uncertainty of processing times.
But existing optimization model is very vulnerable to this uncertainty because
it obtains a highly variable optimal solution by parameters.  Therefore, the concept of smart supply chain
to be discussed in the future should be considered together with uncertainty,
and a robust model against uncertainty must be considered together.

1. Introduction

Abstract. In the era of the fourth
industrial revolution, new technologies such as IoT, Cloud and 3D printers are
integrated into manufacturing system. In particular, connected smart factories
are expected to efficiently produce a variety of personalized products with
small lot size. Therefore, it is necessary to manage new supply chain based on
connected smart factories differently from the existing supply chain for mass
production. In the new environment, processing time may be not stable and
different depending on the factory environment because it produces a small
amount of product. In this paper, we propose a distributionally robust
optimization model to construct and operate a smart supply chain by sharing
resources of smart factories within a given lead time at a minimum cost in the
face of processing time uncertainty. It overcomes the conservativeness issue of
the traditional robust optimization model with box uncertainty set. Simulation
experiments demonstrate the outperformance of the proposed model compared to a
deterministic model and robust counterpart with box uncertainty set in terms of
robustness against uncertainty.

Traditionally, manufacturing has reduced
costs and achieved economies of scale through mass production. The development
of multiple technologies such as computer-aided-design(CAD) has led to more
advanced manufacturing processes, which have improved personalized demand and
product quality. In the meantime, the emergence of Additive Manufacturing (AM)
presented a new concept of the integration concept of manufacturing 1. Based
on the development of various technologies and the AM concept, manufacturers
began to focus on providing consumers with better quality products. Therefore,
many manufacturing companies have focused on mass customization to achieve the
economy of scope 2.  From the 21st
century, there is a new paradigm throughout the manufacturing industry, such as
serviceoriented architecture (SOA) 3 and IoT 4. Especially, with the
development of communication technology, the rise of IoT technology has
introduced a new concept of integration by facilitating information 4. This
new concept means interconnected supply chains that consider multiple factories
in a network at a plant through the new technologies 5. There are also many
research for resource allocation for virtual manufacturing 6 and agile
manufacturing 7.  In a series of
manufacturing developments, consumers are demanding more and more customized
demand, and to meet these needs, manufacturing enterprises are building
collaborative processes through various technologies.

2.1. Smart Supply Chain Management

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For You For Only $13.90/page!


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2. Literature review

As we enter the fourth industrial
revolution, manufacturing is in a new phase. In fact, some factories are trying
to reduce costs by combining various advanced technologies. In particular,
Internet of Things (IoT) and Cloud technologies are the most popular in the
manufacturing industry. These two technologies are regarded as cutting-edge
technologies that enable free resource sharing between factories, and are being
integrated through various experiments. If these advanced technologies enable
smooth sharing of resources between factories, a new concept of smart factory
that is different from the existing one can be considered. We call these
factories the Connected Smart Factory Network (CSFN). The CSFN is an
environment in which a supplier, factory or customer in the network are
connected through the cloud. With the CSFN, participants who are members of the
network can freely share information and each member can make better decisions.
Consumers can be more specific about their needs through the network. They want
to customize consumption in the network. Factories share the resources and
availability of each other to build an optimal supply chain. It also
establishes an inter-factory network to respond flexibly to the consumer’s
customized needs. This CSFN should be easy to connect and disassemble.
Suppliers can make cost-effective decisions by making optimal decisions based
on these interfactory networks and customized consumer needs. Thus this enables
better planning and collaboration of the supply chain.  We refer to the new supply chain with a
concept of the CSFN as the smart supply chain. The smart supply chain must be
able to respond quickly to the small demands of personalized products and have
flexible connections between factories. 
On the other hand, many factories have individual and diverse
uncertainties, such as weather, power outages, and workers’ ability.
Customization in the smart supply chain also implies uncertainty. Factory
production capacity is always irregular, and irregular supply chains, especially
with customized consumption, can increase the uncertainty of processing times.
But existing optimization model is very vulnerable to this uncertainty because
it obtains a highly variable optimal solution by parameters.  Therefore, the concept of smart supply chain
to be discussed in the future should be considered together with uncertainty,
and a robust model against uncertainty must be considered together.

1. Introduction

Abstract. In the era of the fourth
industrial revolution, new technologies such as IoT, Cloud and 3D printers are
integrated into manufacturing system. In particular, connected smart factories
are expected to efficiently produce a variety of personalized products with
small lot size. Therefore, it is necessary to manage new supply chain based on
connected smart factories differently from the existing supply chain for mass
production. In the new environment, processing time may be not stable and
different depending on the factory environment because it produces a small
amount of product. In this paper, we propose a distributionally robust
optimization model to construct and operate a smart supply chain by sharing
resources of smart factories within a given lead time at a minimum cost in the
face of processing time uncertainty. It overcomes the conservativeness issue of
the traditional robust optimization model with box uncertainty set. Simulation
experiments demonstrate the outperformance of the proposed model compared to a
deterministic model and robust counterpart with box uncertainty set in terms of
robustness against uncertainty.

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