Competition is at an all-time high in the manufacturing industry, and big data is largely to blame for it. With the development of IoT and smart factories, manufacturing analytics have taken product development and making to new levels.
Implementing analytics into your manufacturing processes can help to improve operational, cost, and supply chain efficiency. Continue reading to get some tips that will help your manufacturing company have success with big data.
Use Analytics to Optimize your Supply Chain.
Every manufacturing company would love to be able to offer premium prices to their customers, and if it were that simple, everyone would do it. However, if you’re looking to cut the prices of your products without sacrificing product quality, you should start by looking for ways to improve your supply chain management.
One of the most common use cases of analytics in the manufacturing industry is raw materials procurement. As a leader in the field of data science solutions, TIBCO’s manufacturing analytics platforms help manufacturing companies enhance their inventory management systems. Furthermore, with analytics, you can find suppliers who have the raw materials you need at prices that will allow you to offer better prices to customers.
Predictive Maintenance Greatly Reduces Downtime.
Manufacturing processes cause a lot of wear and tear on machinery, and when manufacturing equipment breaks down, it can lead to unplanned downtime on the shop floor. Predictive analytics uses real-time and historical data to predict when machinery will need maintenance. With predictive maintenance, you can prevent your manufacturing equipment from malfunctioning and keep your production line running, which is great for your bottom line.
Get the Right Data.
Analytics insights come from asking the right questions and collecting the right data to improve operational and cost-efficiency. However, collecting massive amounts of data does your company no good if it isn’t the right data.
Big data is integral to the manufacturing industry, but it’s equally important to be wise about how you apply data analytics. The key is to let the data tell you to show you what parts of your operations can be improved. From there, your data analysts should be able to create algorithms that will deliver metadata, which you can use to pinpoint specific aspects of your operations and manufacturing processes.
Don’t Allow your Data Scientists to Manually Process Data.
Advanced analytics have been part of the manufacturing industry for quite some time, but legacy systems relied too much on human input. Even though your data stewards may be highly qualified, human-managed data cleansing processes are time-consuming and prone to error.
Indeed, the time data analysts spend cleansing data and preparing it for analytics would be much better spent on actual analysis of the data. A lack of integration of your manufacturing analytics platforms leaves too much to chance and human error, and that’s why you should leave the data processing to artificial intelligence.
Machine learning algorithms can pull real-time insights from various data sources, decide which data is important, and begin preparing the data for use by your analysts. This process optimization measure frees your data analysts to focus on the insights in the data rather than the collection and preparation of it.
The manufacturing industry is ever-evolving, and the manufacturing companies that are best able to harness the power of analytics are the ones that will thrive going forward. The keys to successfully utilizing business intelligence and analytics software are to cast a wide data net, prioritize actionable results, and use predictive analytics wisely to prevent downtime and supply chain problems. Whether it’s the procurement of raw materials from suppliers or developing new product lines for consumers, big data analytics has you covered front and back.