In the past, if you wanted to know how a machine was performing on a shop floor, you would have had to put pencil to paper and note the output, the error rate, the quality, all manually. You would have to look at the quality of the inputs and the available supply of the raw materials. You would have had to look at the output over time to see if there was a consistency to quality, or errors, that needed to be investigated.
In other words, you were looking at potentially weeks of manual effort to figure out why and how a manufacturing process was going awry. That, in and of itself, isn’t a good use of anyone’s time and is a pretty inefficient way to become more profitable.
Then came Industrial IoT and the ability to monitor machines directly, providing quantities of data from the machine, as well as the operator, that can be analyzed in order to improve both production and quality.
Analytics are essentially the collection and manipulation of large quantities of data to reveal insights. Data about the performance of machines and people, going through the process of receiving an order through delivery of that order, is collected and reformatted as easy to understand metrics, to reveal where there are issues with performance or output quality.
This goes beyond the actual collection of the data to include the formulation of insights that can be used at every level of the organization. The advancement of tools and software in this area means that the process of collecting data is no longer manual and the analysis is centrally available, in real time, for everyone from the shop floor manager to the CEO to review and act on.
There are many advantages to leveraging real time business intelligence:
"Increased revenue (33.1%), increased customer satisfaction (22.1%) and increased product quality (11%) are the top three benefits of Industrial Analytics." (Source)
Production and quality are interrelated. A gain in one that results in a loss in the other is not moving the manufacturing process, or its associated costs, forward. In the past, data on performance was often looked at separately from quality. Whether a machine—or its operator—were lagging during the overnight shift set up on a big order was seen as distinct from the quality of the output.
The theory being that the customer would be okay with a longer production time on their order as long as the quality was there. But that’s no longer the case. Customers the world over are expecting more and rightfully so. To achieve a better level of satisfaction, the data on performance AND quality must be combined to achieve industry or organizational level markers. These metrics are the difference between keeping a client happy and simply keeping them.
"69% of decision makers believe Industrial Analytics will be crucial for business success in 2020, with 15% considering it crucial today." (Source)
The bottom line in manufacturing analytics is to go beyond the simple collection and display of data (descriptive) to leveraging it at a more granular level to be able to predict issues on the shop floor, and save money while improving output, in the process. Taking historical data, for example, and estimating what isn’t known, such as future demand, the statistical possibility of machine failure and so on will help everyone from the operator on the shop floor to the higher level decision makers to see where production is at or likely to be, at any given moment. Of course, as with any prediction or estimate, there is no guarantee of accuracy, but it’s far more likely to be valid when enough data is used to support the analytics, than a human prediction!
All in all, the manufacturing industry is seeing that analytics aren't just about ‘keeping track of things’ but about increasing revenue, reducing costs and maintaining a level of customer service that will keep it relevant in the years to come. Next week, we'll deep dive into the different types of manufacturing analytics and how you can start building your own analytics roadmap for your company. Stay tuned!
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