By using advanced analytics for manufacturing, to understand the valuable information concealed within data they already have!
Most manufacturers have already made the easiest and most obvious changes to improve their operations, leveraging traditional methods to boost their own productivity and simultaneously create a more dependable supply chain.
But the pressure to do even more with less is relentless, especially in a volatile business climate such as the one the global community is currently experiencing. Therefore, manufacturers must continually look for new ways to improve the productivity and profitability of their operations.
We’ve used this space to say this before. Here it is again: there’s a valuable asset that many manufacturers have not yet optimized - their own data.
Some of you may want to argue that point, but note that we’re calling for the optimization of deriving sound business decisions from data. It’s a high bar. You may have called a few meetings, written a few emails, maybe even scribbled on a whiteboard and snapped a photo. That’s not good enough.
Thanks to the power of the cloud and advanced analytics, manufacturers can put data to work, gathering information from multiple data sources and taking advantage of machine learning models and visualization platforms to uncover new ways to streamline processes from sourcing to sales.
Advanced analytics for manufacturing and production not only help manufacturers solve stubborn problems, they may reveal some they never knew about such as weaknesses in the extended supply chain or unprofitable production lines. Specifically, practitioners can use intuitive displays to drill into safety metrics, product quality, on-time delivery, cost efficiency, and much more. Here are some common advanced analytics use cases for manufacturers.
Quality control is key to the customer experience and to your bottom line. Over time, ineffective quality control processes will affect customer satisfaction, buying behaviors, and ultimately lead to lost market share. And the costs don’t stop there.
Poor quality control leads to more customer support costs, warranty issues and repairs, and less efficient manufacturing. Good predictive analytics, however, can provide insight into potential quality issues and trends before they become truly critical issues. You can use advanced analytics to understand defects by item, work center, work order, shift and reason code. Separate trends from one-off outliers.
For manufacturers with major investments in infrastructure and equipment, the ability to manage that capital outlay is critical. By analyzing metrics and data related to the lifecycle maintenance of equipment, companies can predict both timelines for probable maintenance events and upcoming capital expenditure requirements, allowing them to streamline their maintenance costs and avoid critical downtime.
In short, knowing when a part is going to break reduces downtime and waste. By analyzing factors that drive the wear and tear of your devices and machinery, you gain transparency on the real lifetime of your products. According to McKinsey & Company’s 2017 report ‘Manufacturing: Analytics Unleashes Productivity and Profitability’, what is often termed predictive maintenance typically reduces machine downtime by 30% to 50% and increases machine life by 20% to 40%.
We’ve saved supply chain optimization for last. This use case is especially relevant as we watch global supply chains cope with the damage caused by the COVID-19 pandemic. Start by simply mapping your supply chain to at least the second level and preferably the third. Your overall goal is to identify weaknesses and build resilience. As a practical matter, you will learn to anticipate the right time to produce orders or plan shipments to maximize on-time delivery and resolve storage issues. Analyzing the duration of individual processes and the interdependencies among them provides information about the impact of disruptions and potential options for avoiding those disruptions.
Bear in mind that supply chain excellence requires strong supplier relationships and a constant exchange of information with your suppliers to ensure materials and parts are where they need to be when you need them. Halo fosters collaboration – even outside your company – on a common platform to increase pipeline efficiency.
Let’s wrap up with a reminder that data-driven insights require holistic data management. In other words, you need to bring it all together to tease out the deep meaning and enjoy the benefits. The term of art is data warehousing. Because Halo provides data integration across departments and systems, essentially everything your business collects — big data, small data, on premise, off premise, you name it — you have the ability to develop business insights that follow your entire business process, not just pieces of it.
Data warehousing builds a common repository that allows you to work with your information with a myriad of information access and visualization tools. No matter how your team chooses to work with your enterprise data, data warehousing ensures everyone has the same set of metrics, rules and assumptions to help them meet common goals.
If you haven’t already, make advanced analytics for manufacturing and production a priority at your company. Start, or finish what you started. What better way to use the downtime none of us expected to have in early 2020?