One of the biggest technology trends across the manufacturing, processing and distribution industries over the past few years has been analytics. And while technology and software development companies offering this kind of feature or software approach analytics in a variety of ways, they are all essentially about aggregating data from production devices and looking across the data for trends and/or anomalies. Using this, they can spot areas for improvement or identify potential problems before they adversely affect production operations.
However with so many available analytics technologies out there using so many different approaches, many manufactures are confused about how to effectively assess the use of analytics for their business.
One needs to narrow down on the key aspects of the analytic software that really matters to the manufacturer and caters to their needs.
The first step is to have a roadmap for what you want to accomplish. What information would be useful to help make decisions and improve your business. Then you can begin thinking about how to deliver that information and how to prove the quality of it.
More often than not, the data journey often starts with using small standard reports that highlights what has happened, prior events and transactions. As the entity starts to get more sophisticated, they might move up the curve to ad hoc reports and drill downs that help explain what has happened and why it has happened. After this is when we start to see the move to the next level which is advanced analytics.
In advanced analytics, we start to use the power of computing to perform the statistical analysis , forecasting and even predictive analytics. So this means that the reports and dashboard aren’t just showing what has happened and leaving it to the user to interpret the information, the reporting tools are starting to flag and alert on areas of risk or areas of opportunities, pointing us to where to focus our attention.
Good analytics depends on having captured quality data in the first place. For that reason it’s important that there is an overlap or coordination between the system that captures data and the analytics system. It is equally important to ensure that business processes are being managed in a way that supports capturing the data that is required to the answer questions that we want to solve with analytics. There needs to be a fluid relationship between the capturing data team from. Be it the CRM System, Sales Automation system with the analytics team.
Analytics can be used to solve various problems in the manufacturing industry e.g.
- Predicting when quality issues are bound to arise. By looking at historical patterns and data sets, find what are the combination of circumstances that will likely lead to a quality problem. That way, find a way to eliminate the root causes.
- Demand forecasting resulting in better planning, improved scheduling and material planning.
Things get useful when you can identify a problem before it happens.
An elaborate example, it is easy to know all the signs of an unhappy employee or factors that might contribute to employee churn. Getting skipped over from a promotion, bad review, turnover from manager, multiple hard shifts; and while mangers might know about all these factors, the hard part in a busy production environment where there is a large team that needs overseeing, would be finding that needle in a haystack. Which in this case is which particular employee is exhibiting signs of churning.
The benefit of putting computing power to work is to find those needles in the haystack and give us advanced warning.
Given how broadly applicable analytics technologies are, you might ask yourself who is the target user? I would say anybody who can make a decision on behalf of the company based on having access to information, is a potential user of an analytics software or tool.
