Today, the manufacturing sector is facing relentless pressure to increase productivity, reduce costs, improve quality, and more. The conventional manufacturing makes very little on real-time data. In contrast, data-driven manufacturing uses data collected from machines and production lines to analyze and optimize the manufacturing process, enabling informed decisions based on facts rather than experience or intuition, leading to targeted operational improvements.
What is data-driven manufacturing?
Modern manufacturing needs to be able to adapt quickly as new innovations emerge and current technologies fade out faster than ever before. Data-driven approaches help manufacturers collect and extract key data to enable more accurate and timely decisions. Data-driven manufacturing relies on three key components:
Data collection: It starts with collecting data from all areas of production, including sensors on machines, records from production lines, engineering changes notice (ECN), and external data such as changes in raw material specifications. Because of the large volume of data, or “big data,” a robust data management system is needed to collect and store it.
Real-time analytics: Manufacturers need real-time analytics to break down and understand this data, providing insights into how well production is performing. This way, manufacturers can make changes quickly and stop problems before they arise.
Artificial intelligence (AI): AI helps analyze data more deeply. By using machine learning (ML) to examine large data sets, AI can find patterns and predict what might happen next. Gen AI enables engineers to talk to big data with ChatBot technology, allowing engineers to gather insights to support their analysis.
Benefits of Adopting Data-driven Approach
As manufacturers use more data, they become more competitive. Real-time data allows manufacturers to keep a close eye on the performance of their machinery and equipment on the production line. Some of the benefits of using data-driven manufacturing include:
Predictive maintenance: Data analysis can show when a machine is likely to fail, allowing manufacturers to perform maintenance at the right time, reducing costly downtime instead of fixing the problem after it has already occurred, saving time and resources.
Increased workforce efficiency: Viewing data can reveal delays and problems in workflows, allowing factories to design specific training programs to equip employees with the skills they need, resulting in increased productivity and fewer errors.
Reduced waste: Data about how raw materials are used, how many the products are produced, and the number of defective products can reveal areas that can be improved, reducing waste and making better use of resources.
How to Start a Data-Driven Manufacturing System
Implementing a data-driven manufacturing system doesn’t mean changing everything at once, but rather starting slowly and steadily. Here are some simple steps to get started:
Data collection and analysis: This involves creating a robust system for collecting and analyzing data. Explore sources of data from the production lines that are important to the manufacturing process. This could be sensor data from machines, production logs, QC records, and even external factors such as ECNs or material changes. Then, find a way to securely collect and store the data. This could mean purchasing hardware or software to collect the data. Once you have the data, use an analytics tool to find patterns and trends, which can help you better understand your manufacturing process.
Identify bottlenecks and areas for improvement: Once the data is in, it’s important to identify areas that are impacting your operations. Focus on the bottlenecks that are slowing down production or impacting product quality. Manufacturers can create dashboards using tools that display real-time data that show key performance indicators (KPIs) of your processes. This helps you see where you need to improve, such as making machines run better, speeding up material flow, or improving communication between production units.
Integrate BI tools and technology: Integrating the right business intelligence tools can turn insights gained from analytics into actionable plans. Choose a BI that fits your manufacturing needs and budget. Look for platforms that enable data visualization, reporting, and advanced analytics, such as predictive modeling.
Continuous monitoring and optimization: Adopting data-driven manufacturing is not a one-time job, but a process that requires continuous improvement. Establishing an effective monitoring system will help you track key performance indicators (KPIs) and measure how well your changes are working.
Summary
When manufacturers are able to fully leverage their data, they can operate more efficiently, result in better business decisions. Even the US National Institute of Standards and Technology (NIST) recognizes this. So, it’s safe to say that data-driven manufacturing is a real game changer for manufacturers.
Article by: Asst. Prof. Suwan Juntiwasarakij, Ph.D., Senior Editor & MEGA Tech