The Top Uses of Data Transformation, Machine Learning and Visualization for Manufacturing

Organizations have the potential to add tremendous business value to their operations when they attain access to the endless amount of data insights in their business.

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Written by: Fatma Kocer, VP of Engineering Data Science at Altair

Organizations have the potential to add tremendous business value to their operations when they attain access to the endless amount of data insights in their business. In a manufacturing setting, gigabytes of valuable data from the factory floor and manufacturing equipment can go unseen without the proper data analytics technology to filter, organize and visualize those insights.

(Image courtesy of Altair.)

(Image courtesy of Altair.)

Gaining access to that untapped data and the capabilities to analyze it offers the potential for companies to gain competitive advantages, understand the original source of machine and manufacturing errors and reduce costs and downtime through preventative maintenance using machine learning (ML). Teams and individual users can use high performance data solutions to optimize the entire supply chain and provide decision-makers with the insight they need at both the factory floor and senior management level.  

For organizations considering a data-driven manufacturing process strategy, consider these top uses of data processing, ML and visualization to implement into your current business.

Predict Equipment Failures with AI and ML

Understanding the complete life cycle of manufacturing equipment is a major benefit for manufacturers in terms of monitoring and maintaining assets. It is important for manufacturing teams to utilize a data-driven maintenance strategy because when access to that type of information isn’t available up-front, generic equipment maintenance calendars are put in place where equipment is serviced even if it isn’t yet needed. Relying on previous equipment trends and routine maintenance intervals is useful but falls far short of providing an accurate view of machine life and leads to unnecessary downtime.

According to McKinsey, the use of predictive maintenance can reduce machine downtime by 30 to 50 and increase machine life by 20 to 40. Identifying relevant variables that are causing asset failures is the key to proactively avoiding downtime that will impact the entire supply chain.

Real-time dashboards can visualize data streaming from sensors embedded in equipment and make it easy to spot unusual behaviors or patterns in machine performance. Artificial intelligence (AI) can use that same data to predict when failures are likely to occur, allowing the engineering team to schedule shutdowns at times that do not overly disrupt the manufacturing process. Utilizing advanced analytics tools like this properly can speed time-to-market, increase quality and improve a plant’s productivity.

(Image courtesy of Altair.)

(Image courtesy of Altair.)

Visualize Your Data to Detect Anomalies Quickly

Having access to historical and real-time data is important, but access alone won’t improve a manufacturing process. The value in data is how it is used, and manufacturers may find that they are lacking a strategy for how to utilize theirs efficiently. Finding the useful data within data streams is often difficult due to the sheer volume of it coming in from areas such as factory sensor networks and machine output. It is impossible to act on the constant data stream without the right analysis and visualization tools.

The ability to handle all this data properly enables engineers and managers to extract useful, actionable information from it by focusing on outliers, spotting trends and clusters. The proper visualization tools enable stream processing and visualization for real-time and time series data, so analysts can save time manually searching for data anomalies and make critical production decisions much faster. Teams can then look back into their data history to understand the relationships that created the issue and modify their existing analytical applications to prevent similar future errors from occurring in individual or groups of machines.

(Image courtesy of Altair.)

(Image courtesy of Altair.)

Transforming the Warranty Process to Improve Product Quality

The root cause of a product quality issue is often poor data in the supply chain. For example, a manufacturer of injection molded parts may find they are dealing with an unusually large scrap rate. At first glance, the manufacturer may attribute this to contaminants in the material or a faulty mold. With more thorough investigation, the true reason is a poor inspection process for the company’s purchasing and receiving operations. Rather than having to look at individual factors for the problem, companies can take advantage of data analytics tools to find the root-cause of issues and utilize ML algorithms to detect and prioritize problems before they become a bigger concern within their manufacturing processes.

ML can streamline root cause analysis (RCA) prioritization with algorithms that recognize patterns, clusters and trends in huge amounts of warranty claims, quality control, shipping, purchasing and other data sets automatically.

Along with RCA, manufacturers look to warranty claims they receive to discover valuable information about key concerns such as product quality, reliability and customer expectations about the products they are putting into the market. The data in these claims can reveal valuable insights into product faults, manufacturing defects, or where reliability may be lacking in certain areas of product design, so teams can save costs on manpower and resources needed to handle claims.

Informing Future Product Designs Through IoT and Manufacturing Analytics

Businesses are learning that their consumers provide the most critical insights into product satisfaction. They are discovering where manufacturing can improve products, beginning at the start of the design process.

Manufacturers can perform testing during the production process as much as they would like, but these results do not always represent how products are performing in the field. Internet of Things (IoT) and connectivity in today’s products gives manufacturers and design teams valuable, realistic data about their performance and behavior, allowing for direct communication and data sharing from products.

In the consumer goods industry, Mabe, a leader in home appliances uses Altair Knowledge Studio and Altair Panopticon  to understand how to improve future designs in their washing machines, build predictive models and deploy real-time data dashboards. Using historical and live data, the company can instantly see where ML algorithms predict failures in the products and send that data back to manufacturing teams to correct future designs. As connectivity between products continues to expand, companies are taking advantage of modern analytics tools to create strategies to prevent and predict failure within their own products and manufacturing processes.

Ready to Explore Manufacturing Data Analytics?

If you are interested in exploring data analytics for your manufacturing processes, you will want to prioritize technology that can use data to fuel innovation, drive new opportunities and accelerate your smart manufacturing transformation while working in harmony with existing simulation tools and high-performance computing infrastructure. Altair is a leader in manufacturing operations and data analytics that both provides the technology and expertise to guide you along the digital transformation journey for your business.

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