Industry 4.0 for SMEs: Exploring Operationalization Barriers and Smart Manufacturing with UKSSL and APO Optimization

The research aimed to find out why SMEs have a hard time adopting smart manufacturing, what makes smart manufacturing operational, and if only large companies can afford to take advantage of technological opportunities. It used a knowledge-based semi-supervised framework named Unsupervised Knowledge-based Multi-Layer Perceptron (UKMLP), which has two parts: a contrast learning algorithm that takes the unlabeled dataset and uses it to extract feature representations, and a UKMLP that uses that representation to classify the input data using the limited labelled dataset. Next, an artificial protozoa optimizer (APO) makes the necessary adjustments. This research is based on the hypothesis that large companies may be able to exploit Small and Medium-sized Enterprises (SMEs) to their detriment in cyber-physical production systems, thus cutting them out of the market. Secondary data analysis, which involved evaluating and analyzing data that had already been collected, was crucial in accomplishing the research purpose. Since big companies are usually the center of attention in these discussions, the necessity to delve into this subject stems from the reality that SMEs have a higher research need. The results confirmed the importance of Industry 4.00 in industrial production, particularly with regard to the smart process planning offered by algorithms for virtual simulation and deep learning. The report also covered the various connection choices available to SMEs in order to improve business productivity through the use of autonomous robotic technology and machine intelligence. This research suggests that a substantial value-added opportunity may lie in the way Industry 4.0 interacts with the economic organization of companies.

@article{Shaik2025,
  title     = {Industry 4.0 for SMEs: Exploring Operationalization Barriers and Smart Manufacturing with UKSSL and APO Optimization},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {4},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Meeravali Shaik and Piyush Kumar Pareek},
  doi       = {10.14569/IJACSA.2025.0160441},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160441}
}