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.