Although big data is often associated with tech companies, industrial businesses can benefit greatly from data acquisition and processing. Industry 4.0 standards require large amounts of data to optimize processes and improve operational margins. When data is properly acquired and fed into an industrial business intelligence system, your automotive manufacturing company can improve its operations and profitability. Why is data so important, what can it do, and how can you collect it properly? Let’s examine data’s role in auto manufacturing.
The Role of Data Acquisition in Enhancing Automotive Manufacturing
Data Drives Automation
Chief among the benefits of data acquisition in industry is that data facilitates automation. By removing the delay created by having individual workers verify information, you can speed up your industrial processes. For example, if you know how much raw material is needed to produce a part, the time it takes for a part to be produced, and the number of parts needed, you can calculate exactly how long a machine should run. Software can then automate the operation.
Rather than do these calculations manually, a data-driven system can do them for you. However, you need a digital inventory management system that can collect data regarding your raw materials. You also need machine data to know exactly how long a tool takes to produce a part and to keep track of the tool’s usage. Customer invoices can provide the data needed to measure output. When you collect this data properly, your system can process it in seconds.
Using Data to Manage Inventory Intelligently
Many companies within the automotive manufacturing industry rely on manual inventory management, which leads to unnecessary waste. While it may seem to make sense to keep raw materials on hand for work to avoid running out, this practice increases storage and resource management costs. Toyota’s Lean manufacturing approach actually sees inventory as a waste, and only stores excess components when their system predicts a shortage. Toyota smartly held onto computer chips and kept production running when they ran low globally.
Data can automate inventory management and procure materials just in time for manufacturing. By analyzing data related to resource consumption, delivery times from suppliers, and current or future production orders, a BI system can predict what materials will be needed and how much to procure. These predictions can be turned into timely purchase orders that minimize warehousing time and result in more efficient production, as evidenced in a Swedish study.
Improving Uptime With Predictive Maintenance
Data collected from machinery can help you to predict when machines are about to fail or require service. Measurements including operating temperature and vibration can serve as indicators of a machine falling out of spec. By recording this data at a high rate and feeding it into an industrial BI system, you can identify which machines are due for repairs. This predictive approach to maintenance is far more efficient than simply servicing your machines on the manufacturer’s recommended schedule.
Data is the foundation of any successful predictive maintenance program. Older machines may need to be retrofitted with sensors, while newer machines may have these features built in. Data collection systems can use wireless communications to make installation of such a system easier on your factory floor. Predictive maintenance improves uptime by allowing you to focus on the right equipment and letting you schedule maintenance more efficiently.
Reducing Environmental Impact With Data-Driven Solutions
Governments, enterprises, and consumers alike have demonstrated increasing concern about companies’ environmental impact. As regulations tighten, automotive manufacturing businesses will be expected to reduce their carbon footprint by minimizing energy consumption and waste. For instance, Saudi Arabia has set the goal of reaching net-zero carbon emissions by 2060. Fortunately, data can help to optimize energy use and reduce your environmental impact.
IoT energy metering devices can collect data regarding your electricity use at various points throughout your operations. This can help you find the biggest power draws so that you can prioritize efficiency upgrades. You can also predict the value of renewable upgrades, like putting solar panels on the plant’s roof. Furthermore, you can minimize material waste by recycling discarded material from operations. Bins that catch waste material can be weighed to provide more accurate data.
Boosting Quality With Testing Data
You can also improve the quality of your products by collecting more testing data. For many suppliers in the automotive sector, real-world testing is a challenge since the business may not have access to a prototype or a sufficient testing environment. Virtual test beds and testing machines can simulate real-world conditions and gather data to help you improve part design and fabrication so that your components are the best available.
With cloud-computing, it’s possible to accelerate virtual testing and perform hundreds or thousands of tests in a fraction of the time it would take to do them in the real world. While some real-world testing is always essential, the data captured from virtual testing can help you refine designs and production methods before proceeding to practical tests.
How to Increase Data Capture in Your Operations
Collecting all of the data you need to optimize your operations can be a challenge, especially if you have a variety of types of equipment. There’s no one-size-fits-all solution for industrial companies. Each company needs to identify its needs, determine the best way to collect data, and implement a system that can process that data and use it to automate and provide insights. Digital transformation experts can help you craft a personalized plan for better data acquisition and analysis.
Contact SAAB RDS to learn more about how we can help you transition to modern Industry 4.0 standards and use data to propel your company forward.