Table 3 Use cases in IIoT field and the used ML algorithms with description of pros and cons

Predictive maintenance [48]

Industrial field

LSTM, CNN

Pros: Anticipates equipment failures, reducing downtime and maintenance costs

Cons: Requires substantial historical data, the possibility of false alarms or missed predictions

Quality control [49]

Manufacturing

SVM, neural networks, decision trees

Pros: Enhances product quality, reduces defects, ensures real-time monitoring

Cons: Initial training complexity, need for consistent data quality, the potential need for retraining

Supply chain management [50]

Logistics, manufacturing

Reinforcement learning

Pros: Optimizes inventory, reduces costs, enhances delivery efficiency

Cons: Sensitive to data quality, requires fine-tuning for specific industries

Process automation [23]

Manufacturing

Deep learning, natural language

Pros: Reduces errors, enhances overall efficiency

Cons: integration challenges,

Predictive analytics [22]

Various

time-series analyses, anomaly detection

Pros: Improves decision-making

Cons: Challenges in data pre-processing