AIMFG Insights Machine Learning Guide

AI owes  its ability to improve processes and operations to the Machine Learning (ML) that underpins techniques for parsing patterns in large datasets. From the supply chain to the shop floor and from production cycles to product life, manufacturers that implement ML models can unlock AI’s business benefits.

Thanks to their ability to continuously self-correct based on the accuracy of their predictions, ML models improve efficiency, quality, and innovation. Making predictions based on the patterns detected by ML algorithms, AI adjusts parameters in pursuit of greater accuracy in real-world outcomes

Implementing ML, manufacturers can reduce downtime and maintenance costs, eliminate rework, and extend the lives of products and the machines that make them. They can improve product quality, optimize processes, and enhance supply chain visibility. Most importantly, they can make decisions based on data-driven insights.

Growing adoption among manufacturers is driven by advancements in technology, increasing competition, and the need for operational efficiency. The upward trend promises to continue, particularly among small and medium-sized manufacturers seeking competitive edge.

This report examines the ML basics, including the types of ML and their applications in manufacturing. It offers guidance in the selection and onboarding of tools and platforms that create the continuous improvement these technologies enable.

Types of ML

AI is revolutionizing manufacturing with the Predictive AI that builds models and analyzes historical data to anticipate future outcomes. And with the Generative AI that synthesizes data and runs scenarios to make those outcomes more predictable.

ML techniques are classed primarily as Supervised Learning and Unsupervised Learning. All employ Predictive AI and Generative AI to improve processes and operations.

Commonly used in classification and regression tasks, Supervised Learning uses algorithms that are trained on so-called labelled datasets in which each data point is associated with a corresponding label or outcome. The algorithm learns to map input data to the correct output based on labeled examples.  Supervised Learning is applied  for quality control, predictive maintenance, and demand forecasting, among other tasks.

Unsupervised Learning involves training algorithms to find patterns or structures in sets of unlabeled data. This approach is useful for clustering similar products or identifying anomalies. Manufacturers use Unsupervised Learning to optimize processes.

A hybrid approach that combines labeled and unlabelled data for training, Semi-Supervised Learning is useful when labeled data may be limited or costly to obtain. This is useful for defect detection, where there are labeled examples of defects amid large flows of unlabeled data from processes and products.

Transfer Learning uses knowledge gleaned from one task to improve performance in related tasks. In manufacturing, Transfer Learning accelerates the training of ML models for production lines and similar processes. By transferring techniques from one area to another, manufacturers can accelerate training and improve performance.

Combining multiple models to improve predictive performance, Ensemble Learning improves accuracy in predictive maintenance, quality control, and demand forecasting. By aggregating and analyzing predictions from so-called weak learners trained on different subsets of data, Ensemble Learning improves outcomes.

Anomaly Detection is a pattern-recognition technique critical for anticipating equipment failures, process deviations, and quality issues in real-time. Using clustering, isolation forests, and autoencoders, Anomaly Detection helps manufacturers ensure timely intervention to prevent downtime and rework.

Manufacturers use Dimensionality Reduction techniques to fine data, preserving as relevant information as they eliminate features that are not germane for Data Analytics. Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) are common techniques used for visualization, feature selection, and noise reduction in data from sensors on the shop floor.

Time Series Analysis (TSA) analyzes and forecasts sequential data points collected over time. In manufacturing, TSA is essential for predicting equipment failures, optimizing production schedules, and managing inventory levels. Machine learning techniques such as Autoregressive Integrated Moving Average (ARIMA), Recurrent Neural Networks (RNNs), and Long Short-term Memory (LSTM) networks are commonly used for TSA forecasting in manufacturing applications.

Implementing ML in Manufacturing

Effective ML integration is predicated on assessing needs, mapping data sources and evaluating the quality of data those sources generate. Once completed, manufacturers can set goals and select the ML models needed to achieve them.

When assessing needs and setting goals, manufacturers should identify specific areas or processes where AI add value and address challenges. Ideally, projects will be small in scale, to make for easier achievement, and extensible, so that ML models can be extended to neighboring processes in a given line or cycle.

Gathering relevant data from various sources and ensuring its quality, accuracy, and consistency is essential. This may necessitate the cleaning and preprocessing of data to make it suitable for training ML models.

The nature of the process and availability of data, whether labeled, unlabeled or mixed, will dictate selection of ML algorithms and techniques. So, too, the capacity for interoperability and integration into existing systems and processes.

Validation using real-world data assesses the accuracy, reliability, and scalability of ML models. Continuous monitoring of system performance provides the feedback that is central to refinement.

Third-Party Products and Platforms

Before implementing ML models, manufacturers should ensure they have the necessary infrastructure, resources, and expertise in place. This may involve investing in data management platforms, upgrading IT infrastructure, and workforce training or upskilling.

Open-source ML platforms such as TensorFlow and Scikit-learn are flexibile and customizable but require outlay for deployment and maintenance. Open-source platforms typically are free to use, with expenses arising from the need for skilled personnel and infrastructure improvements.

Vendors provide pre-built models, tools, and support services, that streamline implementation. Their platforms offer various pricing models, including subscription-based and enterprise agreements, with costs dependent on usage, features, and support.

When selecting vendor products, manufacturers should consider factors such as ease of integration, scalability, customization options, and vendor reputation. To measure the performance of vendor ML models and platforms, manufacturers should track key metrics such as accuracy, precision, recall, and F1 score.

Additionally, assessing RoI by comparing the costs saved and revenue generated with by AI provides valuable insights. Regular monitoring of KPIs with established feedback loops ensures the continuous improvement and optimization of ML models.

Given their potential to transform manufacturing by enabling predictive analytics, process optimization, and data-driven decision-making, AI technologies underpinned by ML models are becoming required kit in modern manufacturing. By carefully selecting between open-source and vendor platforms, establishing clear objectives, and measuring performance, manufacturers can realize business value in this rapidly evolving IT landscape.