For many manufacturers, collecting data has become an important practice in the manufacturing process. However, fewer have found ways to make that data actionable and convert it to business intelligence. Harnessing data to empower predictive analytics can increase efficiency and maximize returns on investments.
What is predictive analytics?
Predictive analytics is the process of analyzing historical and current data and applying advanced statistical methods and analytical tools to make reliable predictions about the future. In manufacturing, this involves several steps:
Step 1: Identify a Pain Point
Whether a recurring fault, or wasted resources, first the problem must be identified.
Step 2: Collect the Right Data
Look at all of the factors that could be leading to the problem and identify what kind of data is needed to help solve the issue. Examples include temperature, vibration, acoustic forces, deflections and similar technical inputs. Collect the data using sensors and software that can connect to your equipment and systems.
Step 3: Turn Data into Intelligence
Once the data is prepared, a variety of data mining, machine learning and statistical techniques can be used to uncover numerical patterns and relationships between variables. Looking at these results, manufacturers can not only identify what is causing a problem, but also can gain a better understanding of the conditions under which quality and yields are highest.
Step 4: Take Action
This information is then used to create a model that can make reliable predictions. Rule sets and algorithms based on the model can be used to automatically detect errors earlier in the manufacturing process, cutting down on errors, scrap and other wasted resources.
Whether you’re looking to solve a single pain point, or increase efficiencies at a plant or enterprise level, leveraging data is a great place to start. While predictive analytics is not a standalone decision-making tool, it can supplement and support human experience and intuition and improve the quality and accuracy of decisions.