Skip to content
#artificial intelligence Review Open access

Digital Transformation in the Electrical Manufacturing Industry: A Literature Review of Key Considerations and Strategic Priorities

Oct 2026 · American Journal of Innovation in Science and Engineering · 0 citations · 28 references
Digital Transformation in Industry

Abstract

This paper reviews the digital transformation landscape confronting the electrical manufacturing industry, examining the technological, operational, and strategic considerations that determine successful adoption. Drawing on industry reports, case studies, and recent literature, the study synthesizes emerging technology trends, including advanced connectivity, the Internet of Things, artificial intelligence, and big-data analytics, and their implications for electrical businesses transitioning toward Industry 4.0 practices. A risk framework is developed to categorize the principal threats and opportunities facing the sector, addressing challenges such as legacy-system interoperability, networking infrastructure gaps, high capital requirements, supply chain disruptions, and evolving data-governance regulations. Successful transformation practices from established organizations, including P&G, GE Digital, Schneider Electric, and Enel X, are examined to extract lessons applicable to the electrical manufacturing context. The findings indicate that a resilient technology foundation, anchored in enterprise resource planning and a coherent digital ecosystem, underpins sustained transformation, while regulatory, workforce, and infrastructure constraints remain significant barriers. The paper concludes by outlining the digital ambitions and priorities electrical businesses should pursue, including investment in data analytics capabilities, cybersecurity, and low-code/no-code enterprise tools, to remain competitive amid accelerating global sustainability and digitalization targets.

Read PDF

Similar papers

#artificial intelligence Conference Open access Jun 2018

Gibson Env: Real-World Perception for Embodied Agents

This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.

F. Xia, Amir Zamir, Zhi-Yang He et al. · 1,069 citations · ⚡112
#artificial intelligence Open access May 2023

Evaluating the Performance of Large Language Models on GAOKAO Benchmark

GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...

Xiaotian Zhang, Chun-yan Li, Yi Zong et al. · 216 citations · ⚡17

KTO: Model Alignment as Prospect Theoretic Optimization

This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.

Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff et al. · 195 citations · ⚡28
#artificial intelligence Open access Jul 2024

Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval

This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.

Kyra Wilson, Aylin Caliskan · 131 citations · ⚡8
#artificial intelligence Review Nov 2024

How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits

This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...

Masoud Mohseni, Artur Scherer, K. Johnson et al. · 121 citations · ⚡9

Related blog posts

Hugging Face Blog Oct 20, 2026

MPS in PyTorch 2.15

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.