It’s true that manufacturers have access to more data today than ever. Not only that, but the amount of data available increases as new data technologies is developed. However, many manufacturers don’t utilize the data because they have don’t have strategies in place that will allow them to best use the information. Data is only valuable if it is properly analyzed and turned into actionable insights, which is the purpose of predictive analytics.
Predictive analytics in a manufacturing environment allows manufacturers to make predictions about the future based on historical data. To make the most of predictive analytics, manufacturers must follow these steps:
Identify Problems
The first step is to identify the existing problems. This means that comapnies should know where improvements can be made in order to make the manufacturing process more efficient. What are the noticeable problems or “pain points” in the process? Is there an issue with a specific machine? Is there noticeable waste during production?
Collect Data
Once a problem or problems have been identified, manufacturers can zero in on the data that is relevant to the issue. Analyze the problem thoroughly in order to determine possible root causes of the issue and collect all data associated with the possible cause. If there is an issue with a specific machine, collect all possible data from the software that machine is using.
Analyze the Data
Once all of the data is collected, a process must be in place for analyzing that data. The data must also be mined and analyzed properly to uncover potential patterns. Once patterns are identified, manufacturers can determine what the cause of the problem is. Analysis can be as simple or complex as needed.
Turn Insights into Action
Once manufacturers are able to make future predictions based on historical data and analysis, they can use this information to improve the process going forward. Data and insights alone aren’t going to fix anything. Manufacturers must be willing to be open minded when considering the data and analysis and be willing to make changes.
Manufacturers that invest in predictive analytics must also be willing to make additional investments to improve the process once insights are uncovered (such as investing in new equipment, materials, or additional staff and training). Instead of focusing on upfront costs, the most important thing to consider is the long term ROI of making these necessary adjustments.
Source: http://blog.savigent.com/2016/01/optimizing-manufacturing-operations.html