Machine learning impacts the daily lives of many people,. A smartphone’s texting feature can anticipate a user’s most commonly used words or phrases and automatically suggest responses, for example. In the Ecommerce world, Amazon and other online retailers track buyer behaviors and make recommendations based on search history and preferences. Netflix will provide suggestions based on shows and movies you recently watched. Even Facebook recommends pages you should “like” based on those you already follow.
All of these platforms take data collected from your online behaviors and build upon it with each interaction using various algorithms. Each attempts to predict future behaviors based on those of the past. Many industries are embracing similar technology. Here are some ways discrete manufacturers can leverage the power of machine learning to improve efficiencies and production outcomes.
Gather Data for Decision Making
Before implementing machine learning technology, it’s important to summarize existing data into consistent formats that can be properly extracted and analyzed by predictive software. Even simple tools such as Excel spreadsheets and pivot tables can be formatted to provide valuable metrics. Gathering these insights prior to implementing machine learning enables your organization to put historical data to work to offer predictive analysis. Once you’ve systematized your existing data, you’re ready to move to the next step.
Implement Machine Learning Technology
While the data you’ve gathered can provide valuable insights, there are likely disconnects across departments, and sifting through all that data is daunting at best. Leveraging information across departments requires a robust technology solution. Using cloud-based business intelligence tools such as Microsoft’s Power BI can greatly amplify your insights and the value of your data by connecting disparate databases and creating integrated KPIs and dashboards. Machine data can be brought into the mix from various areas to look at uptime, downtime, operations, customer service, the quality of pieces coming off production lines, and more.
Leverage Machine Learning to Improve Business Results
In the same way Amazon can make recommendations based on purchase history, manufacturers can use machine learning tools to analyze customer data and suggest complementary products that enhance customer experiences. Algorithms are applied to determine what an outcome might be and make recommendations. For example, if a customer purchased a product with a warranty, predictive analytics can alert a company’s customer service department when that warranty is due to expire and recommend an extended warranty or other measures to mitigate risks in a timely manner.
The knowledge gained from the data can also be used to win more business by examining cyclical patterns. Analytics can help determine seasonal inventory needs based on weather patterns in certain regions of the country. For example, the hurricane season typically results in an uptick in flooring, mattress and other home goods sales in flood-prone areas. Making sure these products—including supply chain components —are amply supplied and available results in higher sales volumes. Predictive analytics help nurture valuable customer relationships and provide value beyond the sale, and can serve as a growth catalyst.
Improve Production with Machine Learning
The production line can also benefit from machine learning. For manufacturers running multiple machines, the data from each can be connected using enterprise resource planning (ERP) software to enable analysis that couldn’t be done in the past. Downtime can be examined to pinpoint where the issue stemmed from. Was it operator error or defective materials? Or was scheduled maintenance missed? The data extracted from an ERP can help determine the true cause and proactively address it to increase uptime.
From a field service perspective, remote monitoring of equipment can predict potential issues based on set tolerances and deploy automated resources and maintenance to prevent downtime. Predictive diagnostics is an imperative service aspect for critical function industries such as healthcare, energy or refueling stations that must run 24/7. Rather than wait for a problem to occur before dispatching service—which can cause lengthy, costly, and hazardous downtime—resources are deployed in a timely manner to ensure systems stay up and running.
Functionality is Key
In some traditional environments, management may experience a learning curve to understand and see how helpful the data can be. Implementing a pilot program can be a great way to show the value that predictive analytics can bring. As younger generations enter the workforce, many have a greater comfort level with machine learning tools and are more willing to utilize them. The insights gained from these tools can help organizations bridge the gap between more seasoned workers that may be leaving the workforce and those with less experience.
Making sure the data is accessible anywhere—including mobile devices—and easily interpreted is essential for gaining the most value from any system. Information should be displayed in an intuitive format that makes it plain and simple for users to view and digest. Because a workforce can consist of individuals with varying levels of technological proficiency, ease of use should be a major consideration when choosing a system.
Wipfli focuses on providing robust solutions that are intuitive yet effective. Their team of manufacturing experts is well equipped to identify the most practical, cost-effective solutions to help you grow. Reach out to them today to learn more.