Curitiba – BRAZIL
Nov. 23-25, 2022

Resilience and Sustainability ofCollaborative Enterprise Networks:
Recent Advances & Future Opportunities
Plenary Presentation

Shimon Y. Nof
PRISM Center, PGRN, & School of Industrial Engineering
Purdue University, W. Lafayette, Indiana, USA
nof@purdue.edu
1Our purpose:
This conference theme is:“Sustainable Operations and Digitalization in SMEs ina Post-Pandemic Era:
Challenges and Opportunities.”

Under this theme we explore here:• Recent advances and emerging opportunities
for production researchers worldwide -Resilience & Sustainability of production;• How can cyber-collaborative production
overcome disruptions and sustain our life?
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Outline
1. Be Resilient and sustainable →overcome disruptions

2. Cyber-Collaborative Production (CCP) models toovercome disruptions
3. C-AR, Cyber-Collaborative augmented
production in factories & supplies of the future
4. Summary and opportunities…… Is it a dream for SMEs?
No! Look at your cellphone
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What is Cyber? What is Cyber-Collaborative?
➢Cyber:→ digital software & hardware
1. Computing
2. Communication (incl. Internet, IoT)
3. Real Time Control
4. Digital brain models (cybernetics, AI, learning, IoS)
➢ Cyber-Collaborative Production & Logistics:
1. Cyber
2. Physical items & systems
3. Networked
4. cc-Work & cc-Management

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Brief overview from ICPR 26_Taiwan_2021
Challenges and Contributions toIntelligent and Transformative Production
Before, During, and Beyond Pandemic Times
URL: 26th ICPR Taiwan Aug. 2021_Plenary

5Disruptions inProduction, Supply Chains/Supply Networks,e-Work, and Cyber Physical Systems• Disturbances and obstacles that interrupt normal
operations, activities, and processes.

• Example: A pandemic is a series of disruptions
that propagate, and cause damages.
➢They continue now, post-pandemic
1. Disruptive failures → Damaging
2. Disruptive innovations → Opportunities
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Lessons learned: Production researchers’ roles
1. Understand disruptions’ characteristics
2. Predict, diagnose, prevent, recover from
disruptions
3. Prepare the workforce of the future➢ Massive data to collect, analyze, learn from➢ Share intelligence, knowledge, skills, tasks➢ Use data science & O.R. to develop cybercollaborative logic + augmentation to do that7

Resilience by Disruptions’ Prediction, Prevention & Response
Labs; Facilities;
Hospitals

Healthcare & Supply
Staff

Medical & Production
researchersLabs; Facilities;
Hospitals
8Labs; Facilities;
Hospitals[1, 2]ICPR 2022 Curitiba

Disruptive Innovations: Vaccines; Treatments; Telemedicine

Illustrated telemedicine and tele-critical care model for the future[3]
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Wearables, e-Textiles, and Soft Robotics for Personalized Medicinec) Monitor,
diagnose, predict,deliver/Prevent▪Smart bandages▪Smart stickers▪Wearable bioelectronicsa) Wearables collect data;b) Data analyzed locally/sent to analysis[4, 5]
10▪ Implantable soft
robotics tomonitor and todeliver drug,
therapy, nutrition
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Robotic Delivery in Production/ Nano structure

ICPR 2022 Curitiba[6]
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Cyber collaborative Response to Disruptions and their
Propagation (CRDP)
1. framework tounderstand;2. network modeling;
3. model and develop
cyber logic;
4. applied for supply
networks of power,
goods, foods, water,information.

[7]
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Example: Cyber vs. non-cyber protocols in the disruption

propagation repair case of complex production systems
1. Disruptions are not known to operators ahead of time.
2. Strategic resources can be deployed to prevent / handle
disruptions, with dynamic response capabilities.
3. Strategic resources can also cover multiple nearby
nodes.
4. For better disruption prevention results, the best
allocation combinations for prevention coverage weredetermined.
5. Network analyses tested the extent of damages inaffected systems, after a certain amount of time.
6. Experiments indicate: Cyber protocols provide
significantly better resilience.
[7, 8, 9]
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FCFS: First call first served
Non cyber
SPT: Shortest process time
MNDP: Minimizing neighbordisruption propagation
MATW: Minimizing additional task Cyber
workload
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Food supply chain security by agricultural robotic + cybersystems for early detection, diagnosis, and treatment
Agriculture CPS network[10, 11, 12]

Agricultural Robotic Systemwith HUB-CIs[13]
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Cyber collaborative Augmented Reality (C-AR) for CCP
1. Augment human-machine-team interfacing/sharing: knowledge, skills, and data (L)
2. Augment by simulating and distributing 2D / 3D / video results and instructions (R)[14]

A. Real-time, dynamic simulations and predictive decision support
B. Optimal allocation and distribution of C-AR to individual (H/M/T)
C. Authoring/creating adaptive C-AR16

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Authoring and creating effective AR presentations[15, 16, 17]
1. An expert demonstrate task performance
2. AR system captures/adapts a digital embodiment of expert(= Avatar) to create AR Tutor
3. Learner-worker uses smart glasses to observe, understand,execute the task

17Dimensions and features of the design space withexisting AR authoring systems
[18]

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AR Capture Systema.
b.
c.
d.

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Captures with stereo and fisheye cameras
Fisheye camera view
Backpack computer→ Creates upper body embodiment

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Tutoring elements of AR e.g., AdapTutAR
AR tutoring elements to convey knowledge to worker: (a) Avatar;(b) animation and arrow; (c) step instruction; (d) sub-task
description.

[17]
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HUB-CIs architecture: Protocols & algorithms collect,learn, predict, prevent-> other workers-> other HUBs-> other sources

[19]
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Summary
Five realizations on disruptions and opportunities:
1. We can never assume there is no disruption coming justaround the corner. Or that it will take another 100years to occur.
2. Our civilization knows how to survive when terrible
events happen. (We hope.)
3. Disruptions can have negative impacts, and can havepositive effects.
4. Being prepared ahead of time for uncertain yet eventual
disasters is preferred.
5. Production researchers have been contributing.
Let’s continue being prepared.
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Acknowledgements
This research has been developed with partial support from theProduction, Robotics, and Integration Software for
Manufacturing & Management (PRISM) Center at Purdue
University; BARD project Grant# IS-4886-16R;
NSF project Grant# 1839971.

Collaboration with production researchers from PGRN, thePRISM Global Research Network, is also acknowledged.

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Digital & Cyber in Production Automation:Augments abilities of workers, robots, and organizationsCYBER (Cybernetics) = Computing + Communication + Realtime control + Brain models for collaboration protocols
Increasing levels of computers, communication, mobility,
and computational intelligence lead to higher levels ofautomation intelligence, and of resilience to internal andexternal disruptions.
Digital & Cyber Augmentation

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1.0

Computerized

2.0

Computer Integrated

3.0Internetworked + Mobile

4.0

Cloud-Based + Machine Learning

5.0

Cyber-Physical + Cybernetics
Digital & Cyber Convergence[1] CCT, 2007: Nof, S.Y. Collaborative control theory for e-work, e-production, and e-service
Annual Reviews in Control, 31(2), pp. 281-92.
[2] Zhong, H., Nof, S.Y., and Filip, F.G. 2014: Dynamic Lines of Collaboration in CPS
Disruption Response. Proc. 19th IFAC World Congress, Cape Town, South Africa.
[3] Chiang, K.L., and Huang, C.Y. 2022: Precision medicine and telemedicine. In Springer
Handbook of Automation 2nd ed.
[4] Munoz, et al. (2023) Engineering Applications of Social Welfare Functions, Springer
ACES Series[5] Martinez, R. (2022) Chapter in Springer Handbook of Automation 2nd Ed.
[6] Yetisen et al., (2018) Adv. Materials.
[7] Nguyen, W.P. V. (2020) Collaborative Response to Disruption Propagation (CRDP), PhD
Dissertation, Purdue University School of Industrial Engineering, USA[8] Nguyen, W.P. V., Nof, S.Y .(2019) Collaborative response to disruption propagation(CRDP) in cyber-physical systems and complex networks. Dec. Support Syst. 117,
1–13 (2019). https://doi.org/https://doi.org/10.1016/j.dss.2018.11.005[9] Nguyen, W.P.V., Dusadeerungsikul, O.P., and Nof, S.Y. Collaborative Control, Task
Administration, and Fault Tolerance for Supply Network, in Supply Network
Dynamics and Control (A. Dolgui, D. Ivanov, and B. Sokolov, Eds.), Springer
Series in Supply Chain Management, 2022.
[10] Guo, P., Dusadeerungsikul, P.O., Nof, S.Y. (2018) Agricultural cyber physical system
collaboration for greenhouse stress management. Comp. Electron. Agric. 150,439–454.
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[11] Dusadeerungsikul, P.O., Nof, S.Y. (2019) A collaborative control protocol for
agricultural robot routing with online adaptation. Comp. Ind. Eng. 135, 456–466.
[12] Dusadeerungsikul, P.O., Nof, S.Y. (2021) A cyber Collaborative Protocol for RealTime Communication and Control in Human-Robot-Sensor Work. Int. J. Comp.
Commun. & Control, 16,[13] Bechar, A., Nof, S.Y. (2023) Chapter in Systems Collaboration and Integration,(Huang, C.Y., Yoon, S.W, eds.) Springer ACES Series[14] Dangelmaier, W., Fischer, M., Gausemeier, J., Grafe, M., Matysczok, C., & Mueck,B. (2005). Virtual and augmented reality support for discrete manufacturingsystem simulation. Computers in Industry, 56(4), 371–383.
https://doi.org/10.1016/j.compind.2005.01.007[15] T. Wang et al.,(2020) CAPturAR: An augmented reality tool for authoring humaninvolved context-aware applications, Proc. of 33rd Ann. ACM Symp. on UserInterface Software and Technology, 328–341. doi: 10.1145/3379337.3415815.
[16] Y. Cao, et al. (2020) An exploratory study of augmented reality presence fortutoring machine tasks, Proc. of the 2020 CHI Conf. on Human Factors inComputing Systems, 1–13.
[17] G. Huang et al., (2021) AdapTutAR: An adaptive tutoring system for machine tasks
in augmented reality, Proc. 2021 CHI Conf. on Human Factors in ComputingSystems, 1–15.
[18] Chidambaram, S., et al., (2021) ProcessAR: An augmented reality-based tool tocreate in-situ procedural 2D/3D AR instructions: 10.1145/3461778.3462126.
[19 ] Moghaddam, M., Nof, S.Y. (2022) Information flow optimization in augmented
reality for production & manufacturing, Proc. of ICPR_AR, Curitiba 2022.
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