AUTOMATED CONVERGENCE: A UNIFIED FRAMEWORK LINKING MANUFACTURING ROBOTICS AND WAREHOUSE INTELLIGENCE FOR RESILIENT, HIGH-THROUGHPUT, AND SOCIALLY INCLUSIVE SUPPLY CHAINS
DOI:
https://doi.org/10.65009/qwr5dj80Keywords:
Industry 5.0, collaborative robots, warehouse robotics, digital twin, AI-driven resilience, predictive orchestration, conversational agents, smallholder inclusion, supply-chain integration,,Abstract
Despite massive investments in robotic automation, most supply chains remain
fragmented between manufacturing and warehousing, causing excess inventory, delayed reactions,
and fragility during disruptions. This paper presents Automated Convergence, a real-time
integrated architecture that connects factory cobots and warehouse robots via a shared digital twin,
joint AI decision-making, and decentralized coordination. Discrete-event simulation of a volatile
four-echelon electronics network demonstrates 31 % higher throughput, 41 % shorter lead times,
27 % lower costs, sustained >95 % service levels under ±40 % demand shocks, 28 % lower carbon
intensity, and 60 % higher viable smallholder participation compared with traditional siloed
automation.
References
G. Makhija, “Learning-Enabled Resilience Engineering in Sustainable Supply Networks,”
Asian and Pacific Economic Review, vol. 18, no. 1, pp. 1159–1170, 2025.
https://doi.org/10.65985/APER.2025248444
G. Makhija, “Adaptive Storage Scaling for Disruption-Ready Multi-Echelon Networks: A
Comprehensive Simulation-Based Framework,” European Economic Letters, vol. 15, no. 4, pp.
–1586, 2025. https://doi.org/10.52783/eel.v15i4.3927
G. Makhija, “Learning-Driven Predictive Orchestration for Cost-Efficient Global Supply
Networks,” Journal of Informatics Education and Research, vol. 5, no. 4, pp. 1551–1560, 2025.
https://doi.org/10.52783/jier.v5i4.3926
G. Makhija, “Conversational Agents for Decentralized B2B Orchestration in Manufacturing
SCM,” Phoenix: International Multidisciplinary Research Journal, vol. 3, no. 4, 2025.
https://doi.org/10.65009/fvx61n40
G. Makhija, “Embedding Inclusion at Scale: Implementing Supplier Integration Pathways for
Smallholders in Emerging-Market Food Networks,” Phoenix: International Multidisciplinary
Research Journal, vol. 3, no. 3, 2025. https://doi.org/10.65009/zgzh9768
Y. Cohen, M. Faccio, F. G. Galizia, C. Mora, and P. Pilati, “Artificial intelligence in supply
chain management: A systematic literature review,” Int. J. Prod. Res., vol. 61, no. 22, pp. 7479
, 2023, https://doi.org/10.1080/00207543.2022.2140532.
A. Raatz et al., “Collaborative robots in assembly: A review,” CIRP Ann., vol. 71, no. 2, pp.
–660, 2022, https://doi.org/10.1016/j.cirp.2022.05.005.
D. Loske and M. Klumpp, “Intelligent robotics in warehouses: A review,” Int. J. Prod. Res.,
vol. 61, no. 11, pp. 3672–3695, 2023, https://doi.org/10.1080/00207543.2022.2089922.
M. Bortolini, M. Faccio, F. G. Galizia, and C. Mora, “Industry 4.0 and Industry 5.0:
Differences, synergies and future research directions,” Int. J. Prod. Res., vol. 62, no. 3, pp. 1001
, 2024, https://doi.org/10.1080/00207543.2023.2268647.
R. Bogue, “Robotic exoskeletons: A review of recent progress,” Ind. Robot, vol. 42, no. 1, pp.
–10, 2015, https://doi.org/10.1108/IR-10-2014-0401.
T. Wauters et al., “Warehouse automation: A simulation-based optimization approach,” Eur.
J. Oper. Res., vol. 312, no. 2, pp. 518–536, 2024, https://doi.org/10.1016/j.ejor.2023.07.024.
ISO 10218-1:2011 and ISO/TS 15066:2016, Robots and robotic devices — Collaborative
robots, 2016.
Dev.
S. Bogatyrev et al., “Deep learning for vision-based robotic grasping: A review,” IEEE Trans.
Cogn.
Syst.,
vol.
,
https://doi.org/10.1109/TCDS.2021.3120331.
Syst.
no.
,
pp.
–1471,
,
Y. Zhao et al., “Deep reinforcement learning for warehouse order picking optimization,” IEEE
Trans.
Man Cybern. Syst., vol. 53, no. 5, pp. 2838–2850, 2023,
https://doi.org/10.1109/TSMC.2022.3224136.
Q. Leng et al., “Digital twin-driven production scheduling and warehouse management,”
IEEE
Trans.
Ind.
Informat.,
vol.
https://doi.org/10.1109/TII.2022.3201853.
,
no.
,
pp.
–5023,
,
H. Zhang et al., “Multi-agent reinforcement learning for collaborative warehouse robotics,”
Transp.
Res.
E Logist. Transp. Rev., vol. 170, p. 103024, 2023,
https://doi.org/10.1016/j.tre.2023.103024.
F. Tao, M. Zhang, Y. Liu, and A. Y. C. Nee, “Digital twin driven smart manufacturing,” J.
Manuf. Syst., vol. 64, pp. 112–131, 2022, https://doi.org/10.1016/j.jmsy.2022.06.004.
Y. Liu et al., “Federated reinforcement learning for smart manufacturing,” IEEE Trans. Ind.
Informat., vol. 19, no. 6, pp. 7321–7332, 2023, https://doi.org/10.1109/TII.2022.3219876.
