Daydreaming factories

Aydin Nassehi; Marcello Colledani; Botond Kádár; Eric Lutters

doi:10.1016/j.cirp.2022.05.002

Daydreaming factories

Aydin Nassehi^*, Marcello Colledani, Botond Kádár, Eric Lutters

^*Corresponding author for this work

Research output: Contribution to journal › Article (Academic Journal) › peer-review

33 Citations (Scopus)

Abstract

Optimisation of factories, a cornerstone of production engineering for the past half century, relies on formulating the challenges with limited degrees of freedom. In this paper, technological advances are reviewed to propose a “daydreaming” framework for factories that use their cognitive capacity for looking into the future or “foresighting”. Assessing and learning from the possible eventualities enable breakthroughs with many degrees of freedom and make daydreaming factories antifragile. In these factories with augmented and reciprocal learning and foresighting processes, revolutionary reactions to external and internal stimuli are unnecessary and industrial co-evolution of people, processes and products will replace industrial revolutions.

Original language	English
Pages (from-to)	671-692
Number of pages	22
Journal	CIRP Annals
Volume	71
Issue number	2
Early online date	3 Jun 2022
DOIs	https://doi.org/10.1016/j.cirp.2022.05.002
Publication status	Published - 20 Aug 2022

Bibliographical note

Funding Information:
The authors would like to thank Mr Chan Muangpoon for his photographs of the augmented reality system for Factory Layout Planning and Ms Ola Al-Khuraybi for her contribution to the work on KPI characterisation. The authors would also like to express their gratitude to the CIRP colleagues who supported them with information, references, constructive criticism, and advice; especially, Dr Fazel Ansari, Dr Pinar Bilge, Professor Emanuele Carpanzano, Professor Franz Dietrich, Dr David Gyulai, Professor Hoda ElMaraghy, Professor Waguih ElMaraghy, Dr Benjamin Hafner, Professor Gisela Lanza, Professor Dimitris Mourtzis, Professor Stephen Newman, Dr Martin Peterek, Dr Sina Peukert, Professor Robert Schmitt, Professor Wilfred Sihn, Dr Alessandro Simeone and Dr Nicole Stricker. Special thanks are extended to Nitin Kaushik of Apollo Tyres, Hungary who provided details of their industrial case for inclusion in the paper. Botond Kádár acknowledges the support received from the European Commission through the H2020 project EPIC under grant No. 739592. This work was supported by the Engineering and Physical Sciences Research Council grants EP/R013179/1 and EP/R032696/1 .

Funding Information:
The authors would like to thank Mr Chan Muangpoon for his photographs of the augmented reality system for Factory Layout Planning and Ms Ola Al-Khuraybi for her contribution to the work on KPI characterisation. The authors would also like to express their gratitude to the CIRP colleagues who supported them with information, references, constructive criticism, and advice; especially, Dr Fazel Ansari, Dr Pinar Bilge, Professor Emanuele Carpanzano, Professor Franz Dietrich, Dr David Gyulai, Professor Hoda ElMaraghy, Professor Waguih ElMaraghy, Dr Benjamin Hafner, Professor Gisela Lanza, Professor Dimitris Mourtzis, Professor Stephen Newman, Dr Martin Peterek, Dr Sina Peukert, Professor Robert Schmitt, Professor Wilfred Sihn, Dr Alessandro Simeone and Dr Nicole Stricker. Special thanks are extended to Nitin Kaushik of Apollo Tyres, Hungary who provided details of their industrial case for inclusion in the paper. Botond Kádár acknowledges the support received from the European Commission through the H2020 project EPIC under grant No. 739592. This work was supported by the Engineering and Physical Sciences Research Council grants EP/R013179/1 and EP/R032696/1.

Publisher Copyright:
© 2022 The Author(s)

Keywords

Domain randomization
Production
Synthesis

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title = "Daydreaming factories",

abstract = "Optimisation of factories, a cornerstone of production engineering for the past half century, relies on formulating the challenges with limited degrees of freedom. In this paper, technological advances are reviewed to propose a “daydreaming” framework for factories that use their cognitive capacity for looking into the future or “foresighting”. Assessing and learning from the possible eventualities enable breakthroughs with many degrees of freedom and make daydreaming factories antifragile. In these factories with augmented and reciprocal learning and foresighting processes, revolutionary reactions to external and internal stimuli are unnecessary and industrial co-evolution of people, processes and products will replace industrial revolutions.",

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note = "Funding Information: The authors would like to thank Mr Chan Muangpoon for his photographs of the augmented reality system for Factory Layout Planning and Ms Ola Al-Khuraybi for her contribution to the work on KPI characterisation. The authors would also like to express their gratitude to the CIRP colleagues who supported them with information, references, constructive criticism, and advice; especially, Dr Fazel Ansari, Dr Pinar Bilge, Professor Emanuele Carpanzano, Professor Franz Dietrich, Dr David Gyulai, Professor Hoda ElMaraghy, Professor Waguih ElMaraghy, Dr Benjamin Hafner, Professor Gisela Lanza, Professor Dimitris Mourtzis, Professor Stephen Newman, Dr Martin Peterek, Dr Sina Peukert, Professor Robert Schmitt, Professor Wilfred Sihn, Dr Alessandro Simeone and Dr Nicole Stricker. Special thanks are extended to Nitin Kaushik of Apollo Tyres, Hungary who provided details of their industrial case for inclusion in the paper. Botond K{\'a}d{\'a}r acknowledges the support received from the European Commission through the H2020 project EPIC under grant No. 739592. This work was supported by the Engineering and Physical Sciences Research Council grants EP/R013179/1 and EP/R032696/1 . Funding Information: The authors would like to thank Mr Chan Muangpoon for his photographs of the augmented reality system for Factory Layout Planning and Ms Ola Al-Khuraybi for her contribution to the work on KPI characterisation. The authors would also like to express their gratitude to the CIRP colleagues who supported them with information, references, constructive criticism, and advice; especially, Dr Fazel Ansari, Dr Pinar Bilge, Professor Emanuele Carpanzano, Professor Franz Dietrich, Dr David Gyulai, Professor Hoda ElMaraghy, Professor Waguih ElMaraghy, Dr Benjamin Hafner, Professor Gisela Lanza, Professor Dimitris Mourtzis, Professor Stephen Newman, Dr Martin Peterek, Dr Sina Peukert, Professor Robert Schmitt, Professor Wilfred Sihn, Dr Alessandro Simeone and Dr Nicole Stricker. Special thanks are extended to Nitin Kaushik of Apollo Tyres, Hungary who provided details of their industrial case for inclusion in the paper. Botond K{\'a}d{\'a}r acknowledges the support received from the European Commission through the H2020 project EPIC under grant No. 739592. This work was supported by the Engineering and Physical Sciences Research Council grants EP/R013179/1 and EP/R032696/1. Publisher Copyright: {\textcopyright} 2022 The Author(s)",

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doi = "10.1016/j.cirp.2022.05.002",

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T1 - Daydreaming factories

AU - Nassehi, Aydin

AU - Colledani, Marcello

AU - Kádár, Botond

AU - Lutters, Eric

N1 - Funding Information: The authors would like to thank Mr Chan Muangpoon for his photographs of the augmented reality system for Factory Layout Planning and Ms Ola Al-Khuraybi for her contribution to the work on KPI characterisation. The authors would also like to express their gratitude to the CIRP colleagues who supported them with information, references, constructive criticism, and advice; especially, Dr Fazel Ansari, Dr Pinar Bilge, Professor Emanuele Carpanzano, Professor Franz Dietrich, Dr David Gyulai, Professor Hoda ElMaraghy, Professor Waguih ElMaraghy, Dr Benjamin Hafner, Professor Gisela Lanza, Professor Dimitris Mourtzis, Professor Stephen Newman, Dr Martin Peterek, Dr Sina Peukert, Professor Robert Schmitt, Professor Wilfred Sihn, Dr Alessandro Simeone and Dr Nicole Stricker. Special thanks are extended to Nitin Kaushik of Apollo Tyres, Hungary who provided details of their industrial case for inclusion in the paper. Botond Kádár acknowledges the support received from the European Commission through the H2020 project EPIC under grant No. 739592. This work was supported by the Engineering and Physical Sciences Research Council grants EP/R013179/1 and EP/R032696/1 . Funding Information: The authors would like to thank Mr Chan Muangpoon for his photographs of the augmented reality system for Factory Layout Planning and Ms Ola Al-Khuraybi for her contribution to the work on KPI characterisation. The authors would also like to express their gratitude to the CIRP colleagues who supported them with information, references, constructive criticism, and advice; especially, Dr Fazel Ansari, Dr Pinar Bilge, Professor Emanuele Carpanzano, Professor Franz Dietrich, Dr David Gyulai, Professor Hoda ElMaraghy, Professor Waguih ElMaraghy, Dr Benjamin Hafner, Professor Gisela Lanza, Professor Dimitris Mourtzis, Professor Stephen Newman, Dr Martin Peterek, Dr Sina Peukert, Professor Robert Schmitt, Professor Wilfred Sihn, Dr Alessandro Simeone and Dr Nicole Stricker. Special thanks are extended to Nitin Kaushik of Apollo Tyres, Hungary who provided details of their industrial case for inclusion in the paper. Botond Kádár acknowledges the support received from the European Commission through the H2020 project EPIC under grant No. 739592. This work was supported by the Engineering and Physical Sciences Research Council grants EP/R013179/1 and EP/R032696/1. Publisher Copyright: © 2022 The Author(s)

PY - 2022/8/20

Y1 - 2022/8/20

N2 - Optimisation of factories, a cornerstone of production engineering for the past half century, relies on formulating the challenges with limited degrees of freedom. In this paper, technological advances are reviewed to propose a “daydreaming” framework for factories that use their cognitive capacity for looking into the future or “foresighting”. Assessing and learning from the possible eventualities enable breakthroughs with many degrees of freedom and make daydreaming factories antifragile. In these factories with augmented and reciprocal learning and foresighting processes, revolutionary reactions to external and internal stimuli are unnecessary and industrial co-evolution of people, processes and products will replace industrial revolutions.

AB - Optimisation of factories, a cornerstone of production engineering for the past half century, relies on formulating the challenges with limited degrees of freedom. In this paper, technological advances are reviewed to propose a “daydreaming” framework for factories that use their cognitive capacity for looking into the future or “foresighting”. Assessing and learning from the possible eventualities enable breakthroughs with many degrees of freedom and make daydreaming factories antifragile. In these factories with augmented and reciprocal learning and foresighting processes, revolutionary reactions to external and internal stimuli are unnecessary and industrial co-evolution of people, processes and products will replace industrial revolutions.

KW - Domain randomization

KW - Production

KW - Synthesis

UR - https://www.scopus.com/pages/publications/85131591866

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DO - 10.1016/j.cirp.2022.05.002

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AN - SCOPUS:85131591866

SN - 0007-8506

VL - 71

SP - 671

EP - 692

JO - CIRP Annals

JF - CIRP Annals

IS - 2

ER -

Daydreaming factories

Abstract

Bibliographical note

Keywords

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