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Artificial Intelligence for Production Management and Control Towards Mass Personalization of Global Networks

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CIRP Novel Topics in Production Engineering: Volume 1

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

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Abstract

Companies operating in global production networks should handle the complex, uncertain, and volatile environment, making them more vulnerable to disruptions. The Mass Personalization (MPe) paradigm is already a reality and has increased the involvement of end-users in the product lifecycle. It requires responsive and flexible manufacturing operations to produce cost-effective individualized products in dynamic batch sizes at scale taking into consideration the unique preferences of each customer. Therefore, modern manufacturing and production systems and networks must be capable of responding quickly to (i) the alteration of demand and conditions in the supply chain, and (ii) the volatile customer demands. By extension, in the context of MPe, manufacturing and production systems must be capable of self-optimizing manufacturing operations in order to achieve flexible, autonomous, and error-tolerant production. On the other hand, Intelligent Manufacturing (IM) is a key concept that has evolved during the last five years and is, currently, gaining momentum thanks to the potential offered by the Industry 4.0 vision. Thus, the ability of a company to setup an effective data gathering and processing strategy, orchestrating data flows, and then draw meaningful and actionable insights from them, is critical to MPe success. As such, the technological drivers of MPe are the Big Data Sets and Artificial Intelligence (AI) among other pillar technologies of Industry 4.0. The scope of this essay is to identify and highlight the state-of-the-art on how the integration of AI and Big Data technologies and techniques will contribute towards the efficient personalization of each customer’s experience under the framework of Industry 4.0 and beyond.

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Abbreviations

6LoWPAN:

IPv6 Low-Power Wireless Personal Area Networks

ABC:

Artificial Bee Colony

ACO:

Ant Colony Optimization

AI:

Artificial Intelligence

AMQP:

Advanced Message Queuing Protocol

AR:

Augmented Reality

BA:

Bat Algorithm

BBO:

Biogeography Based Optimization

BFO:

Bacterial Foraging Optimization

B2B:

Business-To-Business

BLE:

Bluetooth Low Energy

BMS:

Biological Manufacturing Systems

CAx:

Computer Aided Technologies

CFO:

Central Force Optimization

CoAP:

Constrained Application Protocol

CLSA:

Clonal Selection Algorithm

CRO:

Chemical Reaction Optimization

CSA:

Cuckoo Search Algorithm

DE:

Differential Evolution

DDS:

Data Distribution Service

DL:

Deep Learning

DPO:

Dolphin Pod Optimization

EMO:

Electromagnetism Optimization

EBITDA:

Earnings Before Interest, Taxes, Depreciation and Amortization

FA:

Firefly Algorithm

FPA:

Flower Pollination Algorithm

GA:

Genetic Algorithm

GDP:

Gross Domestic Product

GPN:

Global Production Network

GSA:

Gravitational Search Algorithm

HMI:

Human–Machine Interface

HS:

Harmony Search

IoT:

Internet of Things

IIoT:

Industrial Internet of Things

IM:

Intelligent Manufacturing

ISA:

Intelligent Search Algorithm

IT:

Information Technologies

KHA:

Krill Herd Algorithm

LOA:

Lion Optimization Algorithm

LoRaWAN:

Long Range Wide Area Network

MSA:

Monkey Search Algorithm

M2M:

Machine-To-Machine

MCS:

Monte-Carlo-Simulation

ML:

Machine Learning

MPe:

Mass Personalization

NIOA:

Nature Inspired Optimization Algorithm

NP:

Non-deterministic Polynomial time

OIO:

Optics Inspired Optimization

OS:

Operating System

PFA:

Paddy Field Algorithm

PSS:

Product-Service System

PSO:

Particle Swarm Optimization

RFD:

River Formation Dynamics

SA:

Simulated Annealing

SFLA:

Shuffled Frog Leaping Algorithm

SOA:

Spiral Optimization Algorithm

SSO:

Social Spider Optimization

SCN:

Supply Chain Network

SDG:

Sustainable Development Goal

SME:

Small and Medium sized Enterprises

TS:

Tabu Search

WSN:

Wireless Sensor Networks

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Authors and Affiliations

  1. Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, Rio Patras, 26504, Greece

    Dimitris Mourtzis, Nikos Panopoulos & Panos Stavropoulos

  2. Laboratory for Advanced Manufacturing Simulation and Robotics, School of Mechanical and Materials Engineering, University College Dublin, Dublin, Ireland

    Nikolaos Papakostas

Authors
  1. Dimitris Mourtzis
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  2. Nikos Panopoulos
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  3. Panos Stavropoulos
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  4. Nikolaos Papakostas
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Correspondence to Dimitris Mourtzis .

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  1. Mechanical Engineering, Politecnico di Milano, Milano, Milano, Italy

    Tullio Tolio

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Mourtzis, D., Panopoulos, N., Stavropoulos, P., Papakostas, N. (2024). Artificial Intelligence for Production Management and Control Towards Mass Personalization of Global Networks. In: Tolio, T. (eds) CIRP Novel Topics in Production Engineering: Volume 1. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-54034-9_8

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  • DOI: https://doi.org/10.1007/978-3-031-54034-9_8

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