The rise of digital resources has brought about massive changes in the manufacturing industry.
Nearly every piece of machinery includes some digital element: artificial intelligence, sensors, machine learning, and other devices that make up the Industrial Internet of Things (IIOT). Each of these pieces work together to improve automation and optimize production processes. In the aftermath of the COVID-19 pandemic, manufacturers have seen how beneficial this digitized automation can be for their organization.
Not only do digital-age manufacturing processes help reduce the number of workers on the shop floor, but they also provide insightful datasets that leaders can use to help with decision-making. Gaining this kind of real-time visibility into the details of production allows for more efficiency, less spending, and greater productivity.
But before organizations can harness the power of manufacturing data, they need to understand what it is.
We’ll cover everything you need to know about manufacturing data in this blog so your organization can utilize the information your machines are already collecting.
What is manufacturing data?
Manufacturing data refers to all the information generated and collected from the manufacturing process. This encompasses both small and big data. Using advanced analytics, organizations combine smaller datasets like machine maintenance, quality control, information collected by sensors, and other individual pieces of information to gain insight into big data. Analyzing this information enables leaders throughout the supply chain to make more calculated and informed decisions for improvement.
Like the rest of the world, the manufacturing industry has become “smarter.” This doesn’t mean there are higher IQs in the workforce, though there could be; it means that the industry has adopted more technology that “thinks” for itself.
Smart manufacturing tools include machinery that utilizes technology like robotics, machine learning, and artificial intelligence. Each of these helps automate data collection, making it easier for organizations to gather insights and make more informed decisions.
3 Types of Manufacturing Data
Manufacturers collect data for various reasons and therefore need different ways to categorize the information. One way to do that is to think about your data in terms of your organization’s capability, reliability, and uniqueness.
Data that measures an organization’s capability helps you establish what you can do for your partners and clients and allows you to set realistic metrics within your own organization. Prospective customers want to see if your business can provide everything they need, and the best way to ensure them that you can is by providing them with hard numbers.
Recording high-quality datasets like number of units per day, scheduling details, project length, and amount of resources helps organizations show exactly how they can produce what their prospects want. Internally, these numbers also help managers set realistic goals and benchmarks, which will ensure proper project timelines, expectations, and management.
Collecting data on reliability enables the manufacturing organization to keep track of their safety and quality standards. Tracking and logging information on preventative maintenance, safety inspections, repairs, and equipment replacement can save organizations a ton of time and money in the long run. Managers can monitor significant dates such as day of purchase or repair and frequency of check-ups so that they reduce the chances of their machinery deteriorating. Smart manufacturing makes this process even easier as sensors and other technology within the machinery automate the updates for the organization.
Tracking quality standards can also help organizations measure their own reliability. Data scientists can use the information collected on products that fail to meet expectations to find patterns. These patterns can lead to insights on how to reduce errors and improve production processes so that products always meet quality standards.
In competitive markets across the United States such as New York, Chicago, or the Bay Area, organizations are always looking for ways to get ahead. The manufacturing sector is no different. Every organization wants to show how they can best meet the needs of their clients and partners. The ones who can empirically show the data that sets them apart from the competition will have the most success.
Showcasing the success of a unique production process or highlighting your productivity numbers can lead potential customers to your organization instead of the competition. Doing this requires high-quality data, though.
Numbers go a long way. Saying “We have a very productive and efficient team,” doesn’t have the same kind of power as saying “This year, we are producing 25% more units per day in the same amount of working hours when compared to last year.”
Manufacturing data gives you the numbers to show how your organization stands out.
3 Ways Manufacturing Data Improves Production Processes
Keeping an eye on production data can ensure that everything always runs smoothly. Whether organizations are focusing on new products or new iterations of older ones, production data helps leaders identify holes in their workflows or production processes. An analytics project with a focus on production data can also give qualitative and quantitative insight into the end products. Once organizations have armed themselves with these pieces of information, they can start to create more informed solutions and improve their production processes. Here are three primary avenues where big data can have big impacts.
Planning marks the beginning stages of any production process. Organizations are eager to jump into the actual manufacturing work, but success requires a thoughtful and calculated plan. Gathering manufacturing data makes it easier for organizations to do this planning because it equips them with the information they need to organize an effective and efficient process. Analyzing factors from previous projects like workflows, labor usage, and time spent on production will give insight into when planning for future projects.
Tracking manufacturing data has helped industry-leading manufacturers gain insight into their customers' wants and needs. Datasets like customer feedback, returns, and responses to testing can give organizations an edge when it comes to developing new products.
Organizations are only as good as their product’s quality, so having the ability to measure and maintain quality standards is essential for any manufacturer looking to excel in the industry. Automated, data-driven insight can help ensure that every product meets the manufacturer’s expectations of high-quality output every single time.
4 ways to use manufacturing data day-to-day
Predictive Maintenance: Smart manufacturing equipment includes sensors on machinery that provide real-time maintenance data on equipment. This information enables maintenance supervisors to address maintenance issues before the problems get too bad, reducing the cost of repairs or new machinery. The added and automated visibility also makes it less likely that organizations will miss important maintenance needs.
Inventory Management: Forecasting demand used to rely on human estimates and predictions, but data analytics can help organizations plan for demand more accurately. When equipped with algorithms and increased visibility of historical sales data, teams can take the guesswork out of forecasting and minimize mistakes, reducing the costs associated with over or under-producing. Moreover, the growing popularity of the subscription model is only possible with smart inventory management—requiring algorithms and automation to turn customers into repeat subscribers.
Price Optimization: A variety of costs go into manufacturing a product. The final price index needs to take into consideration factors like raw material, equipment usage, labor, and distribution costs before organizations settle on a final selling price. That final price also needs to appeal to the customer. Balancing all this can become a real challenge as leaders look for ways to offer the best price while still gaining a profit. Optimizing the price means finding that perfect balance that benefits the customer and the manufacturer, and optimization only happens with the right data. Modern tools can analyze competitor pricing, gather information from various data points, and ultimately calculate the best price. Keeping track of all this information will help any organization stay ahead of their competition.
Supply Chain Risk Management: Collecting data on inventory, supply needs, output, delivery logistics, and other supply chain systems and processes will provide organizations with detailed information they can use to improve supply chain management.
How Propel Enables Manufacturing Data Collection
Whether you're manufacturing aerospace equipment or pharmaceuticals, one thing remains true.
Data works best when it works for you.
Smart manufacturing equipment already records the data. Insights wait to be found and acted upon, but without the right technical tools to perform data analytics, those insights will just sit there like an undug gold mine.
In order to get the most out of it, organizations need to expand their tech stack. In the digital age, it’s just far too complex and inefficient to try to record, analyze, and calculate all this information in an Excel sheet.
Propel has positioned itself as the top product lifecycle management tool on the market. The cloud-based platform serves as the single source for data across the organization, making it easy for leaders to see all the information they need in one place on any digital device. The visualization enables high-quality products to go to market faster and more efficiently by
- Streamlining new product introduction
- Empowering company-wide collaboration
- Designing for quality
- Increasing productivity
- Decreasing cost.
Are you looking to get your products to market faster? Consider how a connected PLM, QMS, and PIM can bring you lightyears ahead of your peers. Get a demo now.