# Artificial Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challenges

arXiv: https://arxiv.org/abs/2108.03383 · HTML: https://mlsanity.com/paper/2108.03383/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 179 references that are in the map.
- First posted on arXiv 7 Aug 2021.

## Builds on (most cited first, 6 of 6)

- [Taskonomy: Disentangling Task Transfer Learning](https://mlsanity.com/paper/1804.08328/) (2018)
- [Efficient Processing of Deep Neural Networks: A Tutorial and Survey](https://mlsanity.com/paper/1703.09039/) (2017)
- [Enabling Cognitive Smart Cities Using Big Data and Machine Learning: Approaches and Challenges](https://mlsanity.com/paper/1810.04107/) (2018)
- [Big Data Analytics for Manufacturing Internet of Things: Opportunities, Challenges and Enabling Technologies](https://mlsanity.com/paper/1909.00413/) (2019)
- [SRE: Semantic Rules Engine For the Industrial Internet-Of-Things Gateways](https://mlsanity.com/paper/1710.09627/) (2017)
- [Artificial Intelligence and Machine Learning to Predict and Improve Efficiency in Manufacturing Industry](https://mlsanity.com/paper/1901.02256/) (2019)

## Built on by (most cited first, 5 of 5)

- [Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applications](https://mlsanity.com/paper/2203.10794/) (2022)
- [Enhancing Model Interpretability with Local Attribution over Global Exploration](https://mlsanity.com/paper/2408.07736/) (2024)
- [On-Road Object Importance Estimation: A New Dataset and A Model with Multi-Fold Top-Down Guidance](https://mlsanity.com/paper/2411.17152/) (2024)
- [SMARTAPS: Tool-augmented LLMs for Operations Management](https://mlsanity.com/paper/2507.17927/) (2025)
- [Prognostics of Multisensor Systems with Unknown and Unlabeled Failure Modes via Bayesian Nonparametric Process Mixtures](https://mlsanity.com/paper/2602.19263/) (2026)

## Abstract (arXiv)

The traditional production paradigm of large batch production does not offer flexibility towards satisfying the requirements of individual customers. A new generation of smart factories is expected to support new multi-variety and small-batch customized production modes. For that, Artificial Intelligence (AI) is enabling higher value-added manufacturing by accelerating the integration of manufacturing and information communication technologies, including computing, communication, and control. The characteristics of a customized smart factory are to include self-perception, operations optimization, dynamic reconfiguration, and intelligent decision-making. The AI technologies will allow manufacturing systems to perceive the environment, adapt to external needs, and extract the processed knowledge, including business models, such as intelligent production, networked collaboration, and extended service models. This paper focuses on the implementation of AI in customized manufacturing (CM). The architecture of an AI-driven customized smart factory is presented. Details of intelligent manufacturing devices, intelligent information interaction, and the construction of a flexible manufacturing line are showcased. The state-of-the-art AI technologies of potential use in CM, i.e., machine learning, multi-agent systems, Internet of Things, big data, and cloud-edge computing are surveyed. The AI-enabled technologies in a customized smart factory are validated with a case study of customized packaging. The experimental results have demonstrated that the AI-assisted CM offers the possibility of higher production flexibility and efficiency. Challenges and solutions related to AI in CM are also discussed.