Digital Transformation in Industrial Manufacturing: Why Embrace the Latest Technology
The manufacturing industry is no stranger to change. Over the past century, manufacturing has undergone significant digital transformation, from production lines with manual workforces to today’s advanced manufacturing facilities that are automated and data-driven. Manufacturers must embrace digital transformation to compete in this global economy.
A manufacturing company’s competitiveness hinges on its ability to produce high quality products at a competitive price point while maintaining profitability.
Manufacturing companies can use AI for defect detection, predictive analytics, and process optimization which will enable them to improve production efficiency by reducing defects and downtime as well as increasing throughput rates.
Background on Manufacturing
The manufacturing sector is one of the most important industries in the world. In fact, it accounts for almost 50% of global GDP and employs more than 650 million people worldwide. The manufacturing sector is a key driver of economic growth and job creation.
Manufacturing covers sectors such as automotive, aerospace, textiles, metals, materials, electronics, and a whole lot more. Almost every physical thing that we touch has gone through a manufacturing process.
Products are ubiquitous in the modern world, and the market size is huge. With digital transformation, manufacturers can become more efficient at production, and thus increase profits.
AI for Defect Detection
Artificial Intelligence (AI) is a manufacturing game changer. AI devices, such as cameras, robots, and drones can be used to detect manufacturing defects that can impact quality and performance.
In the past, manufacturing defects were often found during post-production inspections, which increased manufacturing time and cost. With AI for defect detection, manufacturing defects can be automatically detected during the manufacturing process. This will improve product quality and reduce manufacturing time and cost.
To implement AI for Defect Detection, it is recommended that you set up a meeting with Simerse. We can provide you with state of the art AI algorithms and associated training data, tailored to your defect detection use case.
In manufacturing, predictive analytics is the practice of using data mining and machine learning techniques to identify patterns in past manufacturing performance data in order to predict future manufacturing outcomes.
Predictive analytics can be used to improve product quality and reduce manufacturing time and cost. In some cases, predictive analytics can even be used to prevent manufacturing defects from occurring.
To see predictive analytics in action, consider steel production. In a steel production facility, sensors monitor temperature, machinery speed, and quality. A smart predictive analytics system will be able to correlate quality with any of these sensor variables. Ultimately, the analytics system is able to predict changes in quality (based on AI defect detection) based on how sensor variables change.
Such a predictive system can provide manufacturing teams with valuable information on how to best control manufacturing variables to produce high quality products.
The manufacturing process is complex and involves many variables. To optimize the manufacturing process, it is necessary to streamline manufacturing processes, decrease manufacturing time and cost, increase throughput rates, improve product quality, and reduce manufacturing defects.
Process optimization can be achieved by using statistics. A process optimization system attempts to find an optimal solution by minimizing or maximizing a function. Data is inputted to this function by various sensors, both visual and non-visual.
Multivariate optimization is a technique that identifies the relationship between different input variables and the manufacturing output. Once the relationships are identified, it becomes possible to change manufacturing inputs to optimize the manufacturing process.
Data analytics is the process of examining large data sets to uncover hidden patterns and trends. Manufacturing companies are generating more data than ever before. By analyzing this data, manufacturing companies can improve product quality and reduce manufacturing time and cost.
To effectively use data analytics, it is necessary to have a data management system in place. A data management system will help you collect and store manufacturing data in a single location.
Often public cloud providers like AWS and Azure are used by manufacturers to store data. Other manufacturers opt for on-premises solutions. Whichever route you take, it is important to ensure reliable, understandable access to your data.
Once you have a data management system in place, you can use various data analytics tools to analyze the manufacturing data. Data analytics tools can be broken down into two categories: descriptive and predictive. Descriptive analytics provides manufacturing teams with information on the past, while predictive analytics can help manufacturing teams predict the future.
Analytics Underpin Digital Transformation
Ultimately, a combination of AI defect detection, predictive analytics, and process automation can do wonders for manufacturing efficiency. By embracing digital transformation, industrial manufacturing companies can improve product quality, decrease manufacturing time and cost, and increase throughput rates. All of this is made possible by the power of data analytics.
Digital transformation is essential for manufacturing companies looking to stay competitive in a digital world. Embrace the latest technology and manufacturing techniques to optimize manufacturing processes and increase manufacturing efficiency.
Even small and medium manufacturers can adopt data analytics for their operations. You do not necessarily need full-time data scientists and expensive statisticians to optimize your process. There are many pre-built software tools provided by companies like Simerse which can automatically supercharge your industrial operations.
The Bottom Line
AI manufacturing solutions can be used by manufacturers to improve manufacturing efficiency and increase throughput rates. Using AI manufacturing solutions will enable manufacturing companies to reduce defects, downtime, and increase productivity while remaining competitive in the global economy.
Manufacturers must embrace digital transformation through new manufacturing technologies like AI if they want to remain competitive in this changing world marketplace.