Singapore is a country where manufacturing has become the backbone of the economy, yet currently, this particular industry is undergoing a seismic shift. At its centre stage is machine learning, a subset of artificial intelligence that empowers machines to learn and improve from experience without explicit programming. With the arrival of machine learning capabilities, the whole manufacturing realm has been revolutionised. With ML powers, the potential of manufacturing will be unlimited!
In this blog article, we delve deep into the specific machine learning models driving this transformation, exploring their applications, benefits, and the potential they hold to elevate manufacturing excellence to unprecedented heights.
We will discover
Exploring Machine Learning
- ML is a division of AI that uses data and algorithms to teach AI systems how to learn, express, and copy human learning processes to enhance accuracy over time.
- If we look into the mechanism of ML, we see three key components of machine learning algorithms. First is a decision process where algorithms make predictions or classifications based on input data, whether labelled or unlabelled. Second, an error function evaluates the model’s predictions against known examples to measure accuracy. Third, a model optimisation process adjusts weights within the algorithm to better fit the training data, continuously refining accuracy through iterative evaluation and optimisation until a desired level is achieved.
- Most people are confused between Machine Learning, Deep Learning, and Neural Networks. But these are three different things, and it is important to distinguish them correctly. For example, neural networks are a subset of machine learning, while deep learning operates within neural networks, reflecting different levels of complexity and specificity within AI applications.
The 4 Main Machine Learning Algorithms for Industrial Applications
Supervised Learning Model
Supervised learning models operate with labelled data. Yes, this means each data point is already tagged with a known outcome.
However, this labelling process can be time-consuming, requiring manual effort or automated methods like capturing product locations in a store. This is where the two primary types of supervised learning algorithms, such as classification and regression, come into play.
Classification algorithms assign categories to entities based on data, answering questions like yes or no or categorising objects into multiple groups. Popular algorithms for this include decision trees, logistic regression, random forest, and support vector machines.
On the other hand, regression algorithms are there to analyse relationships between variables, which are useful for predicting trends such as product sales affected by factors like price, temperature, or shelf location.
If we look into some common applications of this in the manufacturing landscape, you can use them for product classification, sales prediction, supplier categorisation, and predictive maintenance for estimating failure rates in machinery or systems. These models are foundational in making predictions and decisions in various fields, enhancing efficiency and accuracy in data-driven tasks.
Unsupervised Learning Model
Unsupervised learning models in machine learning automate the process of identifying patterns within data sets without pre-existing labels. These models are crucial for exploratory data analysis, aiding in defining how to approach complex data science challenges effectively.
It has two primary types of unsupervised learning algorithms, including clustering and dimensionality reduction. Let us explain them to you.
Clustering algorithms have the ability to categorise similar data points into groups based on various criteria, enabling manufacturers to uncover underlying patterns within each group. Likewise, dimensional reduction algorithms streamline multiple variables into more manageable forms for specific problem-solving, enhancing efficiency in data analysis.
These algorithms encompass methods like feature selection, which prioritises relevant data attributes for specific inquiries, and feature projection, which uncovers intricate relationships among variables by creating new intermediary variables suited to the problem at hand.
The manufacturers can utilise these two algorithms, clustering and dimensionality reduction, for organising inventory based on production sales data, linking sales data with store shelf locations, segmenting customer profiles, and extracting meaningful features from images. These techniques are valuable in extracting insights from data without predefined outcomes, facilitating data-driven decision-making and problem-solving across diverse industries.
Semi-Supervised Learning Model
This combines aspects of supervised and unsupervised techniques by using unsupervised algorithms to create labels for data that can then be utilised by supervised learning methods.
These models automate label generation through various approaches. Clustering techniques can easily identify data resembling human-generated labels; self-supervised learning trains algorithms to perform tasks that correctly assign labels; and multi-instance techniques generate labels for groups of examples with specific characteristics.
Through these methods, semi-supervised learning optimises efficiency in handling large datasets, whereas manually labelling every data point would be impractical. This approach enhances the accuracy and scope of supervised learning applications by expanding the pool of labelled data available for training models. The latter also improves overall predictive capabilities across diverse fields, such as image recognition, natural language processing, and anomaly detection in data analysis for manufacturing companies.
Reinforcement Learning Model
This model basically focuses on improving algorithms through interactive processes. These models operate by defining a set of actions, parameters, and goals that are refined through trial and error.
During each step, the algorithm makes decisions or predictions, and its outcomes are compared to the desired results. Based on these comparisons, the algorithm receives rewards for desirable outcomes or penalties for undesirable ones, adjusting its strategy over time to maximise rewards.
Plus, neural networks are commonly employed in reinforcement learning algorithms to process complex data inputs and optimise decision-making processes. For instance, in manufacturing applications, algorithms learn from real-world or simulated data to improve responses to different production conditions, such as supply chain disruptions or machine failures.
This iterative learning approach allows reinforcement learning models to adapt and enhance their performance continually, making them valuable in optimising decision-making processes across various dynamic environments and applications.
Are ML Models and ML Algorithms the Same?
The distinction between machine learning models and ML algorithms lies in their roles within the broader field of artificial intelligence.
You already have an idea about ML models. When it comes to ML algorithms, they are the underlying computational methods and techniques used to train models on data, enabling them to learn patterns and make predictions. These algorithms include a range of approaches such as decision trees, neural networks, and clustering methods, each serving specific purposes like classification, regression, or clustering.
On the other hand, ML models are the tangible outcomes of applying these algorithms to data. They are the trained systems or structures that embody the learned patterns and can be used to make predictions or decisions. Therefore, while ML algorithms are the tools that facilitate learning and pattern recognition, ML models are the practical applications of these algorithms, representing the learned knowledge and predictive capabilities that are deployed in various real-world scenarios, from machine health diagnostics to financial forecasting.
Cerexio's ML Capabilities Embedded Advanced Manufacturing Solutions
Cerexio presents a variety of software suits where ML capabilities are embedded to optimise production processes, reduce downtime, and enhance product quality. Through the power of ML and other models and algorithms, your manufacturing company can extract the best of data-driven insights and predictive analytics for maximum ROI. Not to mention that these solutions streamline operations, cut costs, and boost efficiency, ultimately increasing profitability for your company.
Bolstering Manufacturing Excellence to Sky Limits via ML-Powered Solutions
As we have explored above, machine learning models, when strategically implemented, can be the catalyst for a manufacturing renaissance. However, it is also crucial to remember that successful ML deployment requires a robust data infrastructure, skilled talent, and a culture of continuous learning, adaptation, and expertise in the field.
