Machine Learning in Manufacturing Industry
Machine Learning is a part of Artificial Intelligence and is a process of training a computer that focuses on how we use data and algorithms to think like human beings. However, machine learning is not an easy process. With the use of statistical methods, algorithms are trained to make predictions or classifications, unveil key insights within data mining projects. These insights are important to make better decisions without being specifically programmed to do so. Machine Learning Model is built when you train your machine learning algorithms with data.
For example, a predictive algorithm will build a predictive model, when you provide data, and therefore, you will receive a model which is based on predictive model data. Before deployment machine learning enables models to train on data sets. These trained models can be used in real-time to learn from data. The improvements in accuracy level are a result of the training process and automation that are part of machine learning. Machine learning algorithms' three main learning systems are an error function, decision process and the model optimization process.
Different techniques can improve the accuracy of the Predictive model.
1- Supervised Learning
2- Unsupervised Learning
3- Reinforcement Learning
4- Deep Learning
The Manufacturing Industry field is deeply impacted by Machine Learning and Artificial Intelligence in Industry 4.0 which encourages the usage of smart sensors, devices, and machines to enable Smart Manufacturing. Machine Learning Techniques enable the generation of actionable insights by processing the collected data to increase manufacturing efficiency. It provides predictive insights to empower complex manufacturing patterns and, offers a pathway for an intelligent decision support system in different tasks such as predictive maintenance, quality improvement, intelligent and continuous inspection, process optimization, task scheduling, and supply chain management.
Why is Machine Learning important in the Manufacturing sector?
The goal of Machine Learning is to make Artificial Intelligence solutions faster and smarter so that they can deliver even better results for whatever task they’ve been set to achieve. Because Artificial Intelligence technology is capable of having such a huge impact on society and modern business practices, revolutionizing everyday tasks from planning to logistics to operations and production is imperative to achieve consistency. A technology-driven approach which uses the Industrial IoT and internet-connected devices to produce goods and monitor processes. Its main objective is to automate the processes to maximize efficiency, increase sustainability, supply chain management, and identify the systems problem even before they occur by generating, optimizing, and implementing enormous volumes of data.
Manufacturers are investing in technologies that incorporate machine learning to give greater control and visibility. Additionally, it is supported by advanced robotics, augmented and virtual reality, and edge computing. It has the potential to offer timely decision support in a wide range of manufacturing and production applications, such as predictive maintenance, task scheduling, process optimization, supply chain, quality improvement, etc. There is no historical data to predict when a new product is launched. Machine Learning can use algorithms and analytics to track and determine the product’s success, incorporating data from social media channels, sales, web traffic, and other sources.
This helps in determining the organizations when and where replacement parts need to be ordered and stocked or restocked, most often eliminating excess inventories and overhead costs. Thus, parts are stocked appropriately, and customers are guaranteed a great service experience.
Machine learning is the next stage in the supply chain business, bringing in new data infrastructure can be both time-intensive and costly. Today manufacturers mainly rely on pricing practices of the past, like analyzing excel spreadsheets and cost-plus models, to price service parts. This leads to products being sold at different prices in different locations which creates poor customer experiences.
Manufacturers should be mindful when planning to price service parts taking into consideration all factors that can be used to enhance sales, including weather, part location, demand, and seasonality. With machine learning capabilities, today manufacturers can incorporate all of these factors, and more, to automatically adjust prices based on market requirements.
Artificial Intelligence and Robotics in Manufacturing
Artificial Intelligence and Robotics will prove beneficial in leading advancements in the manufacturing sector and fulfilling increased consumer demands. AI-driven robotics are affecting advancement in the Manufacturing industry worldwide. This way your organization can minimize and assign mechanical tasks to robots. It improves efficiency and, simplifies the whole manufacturing process and other systems. Earlier, more than one person was assigned to manage assigned tasks, with the implementation of AI-based robots, now the robot is sufficient to carry and makes production decisions with resilient outputs. In recent times people prefer customized products over costly industrial products. With the help of Artificial Intelligence labor cast can be minimized, it is the next step after robotics for improving productivity and minimizing the cost of production. Artificial Intelligence is highly essential for the improvement and survival of industries. Robots play a very important role in the assembly lines of production, packing, and shipping of products with the least manual help.
Below are some examples of Artificial Intelligence and Robotics in the manufacturing industries.
Quick Maintenance and Damage control: With the help of AI-based robots can detect and solve the problem. They are programmed in such a way that they can detect the faults and manage solutions to overcome the damage.
Automated Control: Advance Technology has made it easier to control the whole system with the help of just a touch of a button. Artificial Intelligence machines are programmed, in such a way that they can work automatically and make accurate decisions.
Demand-based production: Production is managed depending on the demand and capacity. Every stage is monitored by sensors, which provide data to AI-based software, and production is managed as per the result of the data provided.
Below are a few major companies that are improving the manufacturing field by investing in machine learning and AI-powered technologies like Intel, Bosch, General Electric, Microsoft, Siemens, etc.
With FutureAnalytica Predictive Maintenance cloud-agnostic platform you can monitor the machine fleets. The main aim is to record, monitor, and analyze all the processes from design to recovery. Thus, finding the faults and correcting them within no time.
Another example of the use of Artificial Intelligence in Energy Management is that it improves emissions from specific gas turbines with 500 sensors that monitor pressure, temperature, etc. The data is fed to the platform to give you accurate insights. The energy consumption of a production operation can significantly reduce operations costs. Reduced costs can allocate more funding for process improvement resources which can lead to higher yield and quality.
Our AI-based solutions in the Manufacturing sector link all functions like design, manufacturing, engineering, distribution, supply chain, and services into one scalable intelligent system to give smart outputs.
The large-size manufacturing companies are looking for solutions and are working towards using core automated MLOPs superior algorithms like AI-based Dynamics Modeling, Rich Explainable Deep Learning etc.
Benefits of Machine Learning and Artificial Intelligence for Manufacturing
The introduction of Artificial Intelligence and Machine Learning to the manufacturing industry represents a vital change with many benefits and opening doors to new business opportunities. Implementing Machine learning in the manufacturing industry will improve productivity without compromising the quality of the product. Artificial Intelligence and Machine learning helps businesses create smart and new business strategies.
Some of the AI-backed solutions are
Predictive Monitoring: It helps in monitoring equipment failures. Machine Learning-based Predictive maintenance solutions enable manufacturers to predict device failures accurately. It helps manufacturers reduce planned equipment maintenance and offers enhanced product reliability, quality, and durability. It can schedule device maintenance for particular time intervals. Hence, machine learning is engaged in performing repetitive tasks without human involvement.
Quality Control: The main advantage of Artificial Intelligence in manufacturing is quality assurance. Machine Learning models are used by businesses to discover deviations from normal design specifications and unveil faults or inconsistencies that the ordinary human eye may not notice. Integration of machine learning techniques into the quality assurance process increases product quality while saving money and time.
Demand Forecasting: It is one of the best benefits of machine learning in manufacturing. Artificial Intelligence and Machine Learning algorithms can incorporate into procurement and cost management fields. It can improve the accuracy of product demand prediction. Using historical data, Machine Learning models can provide meaningful insights and make quick decisions for gaining sales profits.
Inventory and Logistics Management: Manufacturing industries are not only focused on production functions, but they also give equal importance to their supply chains and logistics operations. In traditional methods, order value calculations, order data collection, logistics performing, and product-related tasks are manual. But, deploying Machine Learning in manufacturing can efficiently handle issues in logistics services and cut unnecessary costs. In addition to a successful blend of Artificial Intelligence, Machine Learning, and IoT with asset tracking sensors, the emerging technologies improve and automate supply-chain management operations. Beyond monitoring every step of manufacturing processes and production, it also optimizes inventory management.
Solution for Supply Chain Management: One more strength of Machine Learning-based algorithms is in resource management. The best example is the power-consumption optimization algorithm due to which companies like Google reduced 40% approx. on its electricity bills in its data center cooling system.
Significance of Robots in Manufacturing: It’s a fact that robots play an important role in the manufacturing industry 4.0. The benefit of industrial robots in performing repetitive manufacturing tasks is increasing rapidly. The robotic-powered manufacturing process offers opportunities to the manufacturers in achieving agile production and reduces human errors.
Automated Guided Vehicles (AGVs): Manufacturing industries are using AGVs in production and assembly locations. This Artificial Intelligence and Machine Learning-powered autonomous vehicles can easily carry large components. The best thing about AGVs is they can adjust their route by detecting objects or sensing humans.
1- It reduces poor-quality products and increases output
2- Through predictive maintenance reduces cost overheads
3- It offers a synchronized production workflow
4- It ensures robot-human collaboration in the workplace
5- Improve manufacturing processes
6- Gives meaningful insights from real-time faults to manufacturers for designing consumer-focused products
With FutureAnalytica's AI-driven Production and Supply Chain Optimization, Engineers can find the optimized process for different products. Questions like 'What conveyor speed or temperature should I input for the highest yield?’ or ‘What machine should I use for this high pitch emerging technology circuit board?'
Use Cases in the Manufacturing Industry
The utilization of Artificial Intelligence in the manufacturing industry is incredible. Industrial AI robot collaboration enables manufacturers to deliver faster productions. It is changing the way manufacturers design products; it offers insights for the best design. Many big brands are using AI for manufacturing operations. For instance, BMW uses AI for product quality, Nissan uses AI for manufacturing to design ultra-modern cars, and General Motors uses AI for intelligent maintenance.
Process Optimization – AI-powered software can help businesses optimize processes to achieve sustainable production levels. Manufacturers can prefer AI-powered process mining tools to identify and eliminate obstruction in the organization’s day to day functionalities using a process mining tool.
Manufacturers can compare the performance of different regions down to individual process steps, including cost, duration, and the person performing the step. These insights help organizations streamline processes and identify bottlenecks so that manufacturers can take action.
AI-Powered Digital Twin - Is also known as a digital replica in the manufacturing sector designed to accurately reflect a physical object. This digital representation uses input from real-world component status, functionality, and/or interaction with other devices, this can be used for performance issues and the areas of improvement, thus applying back to its original physical object.
Types of Digital Twins:
1) Component/Parts Twins
2) Asset Twins
3) System or Unit Twins and,
4) Process Twins.
It is beneficial for better research and design, greater efficiency, and helps manufacturers to decide on the lifecycle of their product, whether it can be recycled or harvested. Besides this, it also helps in manufacturing project operations, improving system design, testing new products, monitoring & preventative maintenance, and analyzing the customer experience.
Speech Recognition – This is also known as automatic speech recognition (ASR), speech-to-text, or computer speech recognition, and uses natural language processing (NLP) to process human speech into a written format. Nowadays, mobile devices incorporate speech recognition into their systems to conduct a voice search, eg: Siri.
Customer Service - Online chatbots are replacing human agents in the customer journey. They answer frequently asked questions (FAQs) around topics like providing personalized advice or shipping, suggesting sizes for users or cross-selling products, and changing the way we think about customer engagement across all media platforms. Eg: on Facebook Messenger, tasks are usually executed by virtual assistants and voice assistants.
Computer Vision - Artificial Intelligence technology enables computers and systems to gain meaningful information from digital images, videos, and other visual inputs, and based on those inputs, it can take action. This ability to provide recommendations differentiates it from image recognition tasks. It is powered by convolutional neural networks. Computer vision has applications in photo tagging in social media, self-driving cars within the automotive industry, and radiology imaging in healthcare.
Recommendation Engines – Using historic data, artificial intelligence algorithms can help to discover data trends that can be used to develop more effective cross-selling strategies. This can be used as a relevant add-on recommendation to customers during the checkout process for online retailers.
Automated stock trading – It is designed to optimize stock portfolios, without human intervention AI-driven high-frequency trading platforms make thousands or even millions of trades per day.
Cybersecurity - Though it’s a good thing that manufacturing companies are adopting IoT tech inside their organizations., this also makes them susceptible to cyber threats like phishing and hacking. The solution to this is AI enables cybersecurity systems, that can automatically detect and cease any cyber-attack with utmost precision and alert the security teams to take any further actions.
Energy Consumption Forecasting – Using Machine Learning and Artificial Intelligence algorithms, it is possible to forecast energy consumption too. By gathering and analyzing data from different parameters like lighting, temperature, and movement level within a building facility, to create a predictive model that can forecast energy usage in the future. Achieving this level of efficiency will not only save energy costs but will also reduce greenhouse gas emissions.
IT Operations - For a smooth running of an organization, it is very important that all its systems from hardware to software should run smoothly. But manually managing and monitoring all the systems within an organization it’s difficult due to their complexity and lack of time. AI can automate big data management by first gathering enough data through sensor devices, and then analyzing it to create a predictive model capable of detecting or predicting faults in IT operations.
How Future Analytica can help the manufacturing sector in your journey?
FutureAnalytica is the only holistic automated machine-learning, no-code AI platform providing end-to-end seamless data-science functionality with data-lake. AI app-store & world-class data-science support, thus reducing time and effort in your AI journey.
Artificial Intelligence (AI) is most frequently applied in manufacturing to improve overall equipment efficiency (OEE) and first-pass yield in production. Over time, manufacturers can use AI to increase uptime and improve quality and consistency, which allows for better forecasting. The solutions we offer in manufacturing IIoT are
1) Predictive Maintenance
2) Production Optimization
3) Supply chain optimization
4) IT operations
5) Energy Management
6) Attrition Management
7) Predictive Yield
8) Warehouse management and,
With FutureAnalytica enterprises involved in Manufacturing has the opportunity to integrate machine learning and artificial intelligence into their operations and obtain a competitive advantage by gaining predictive insights into production. The basic technologies of machine learning are ideally suited to the complex difficulties that manufacturers face regularly. Superior machine learning algorithms have the potential to improve prediction accuracy at every stage of manufacturing, from keeping supply chains running effectively to producing customized, built-to-order items on time. Manufacturers are gradually using Artificial Intelligence robots in the manufacturing process to provide a safer workplace and increase efficiency. They are also using AI to discover product flaws as well as quality and design concerns. It can also produce hundreds of product designs in a matter of seconds using a combination of AI, ML, and industrial revolution technologies. These design options aid producers in developing end-products with a distinct structure.
AI solutions assist in managing inventory and balancing supply and demand for Manufacturers. AI inventory management solutions as well as AI demand forecasting apps and tools, assist in managing inventory levels and retaining lucrative customers.
Uncover issues that drive both dissatisfactions and churn with AI-powered pre-emptive client engagement. Firms can identify clients at high risk of attrition by learning from examples of clients that have closed or moved accounts in the past through the Attrition Management solution offered by our organization.
We hope this article was insightful and helped you to understand the impact of AI and auto MLOps in the Manufacturing sector. How will you modernize the plant for more resiliency without risking stability in large scale operational disruption? Will you use proprietary algorithm techniques? Achieve scale by consistently deploying advanced technologies on an AI-based cloud-agnostic platform. Thank you for showing interest in our blog, and if you have any questions related to Attrition Management, Predictive Maintenance, Supply Chain Management, Machine Learning, or AI-based platforms, please send us an email at email@example.com.