The second factor is that getting insights from complex systems is hard. Really hard, like self-driving car hard. Your job as a plant supervisor is not an easy one, and completely automating that process with all your know-how is not something easily done.
Many analytics companies work around this complication by limiting the scope of the problem to such a narrow one that the benefits either marginal or only specific to the use case and not the overall system.
As an aside here, many companies treat these “analytics products” the same way they would a new machine. An independent component of the entire system.
So let’s look at a manufacturing analytics system to see what you can, and what you should be able to do.
Manufacturing analytics covers the use of data from a range of sources, including operations, machines and sensors, and other data systems such as ERPs to optimize output.
In many cases, it’s not for lack of data that companies have been slow to adopt these techniques, but rather a lack of data integration. Generally speaking, data is spread out across a wide range of sources and sensors.
There are three well known use cases for manufacturing analytics:
Predictive maintenance is a method of identifying machine downtimes and failures before they occur. It is the successor to preventative maintenance (and its predecessor, reactive maintenance). Whereas preventative maintenance requires scheduled downtimes to perform repairs, predictive maintenance uses data from a machine to predict when a potential failure will occur.
Operational efficiency refers to techniques focused on improving yield and/or throughput for a plant. Techniques here will focus on identifying optimal configurations and settings for machines to ensure that waste is minimized while maximizing total uptime. Many manufacturing analytics tools focus on making key performance indicators (KPIs) more easy to evaluate and review. For example, OEE is a common KPI that is commonly automated with manufacturing analytics and included in manufacturing dashboards.
System optimization refers to techniques that improve systemic aspects of a business, such as supply chain management, sales mix, production planning, etc. They often include components of demand forecasting and inventory management.
Many tools are available to help manufacturers perform some aspect of these use cases, but in our opinion they often miss the mark. Meh.
At its core, manufacturing analytics should surface insights to help companies identify opportunities for improvements - reducing costs and increasing throughputs.
The key concept here is surfacing insights. If the tools don’t do a good job of identifying insights in a timely manner, then people can’t make improvements.
This is worth repeating. Manufacturing analytics tools must:
Timeliness is perhaps one of the most important components. Arguably in the long run, getting average insights faster is more important than spectacular insights after the fact.
The data analytics maturity model allows us to grade companies based on their level of data proficiency. Generally speaking, it is better to walk before you can run, and with data the same holds true.
The four stages of data analytics maturity are as follows:
Companies looking to perform advanced manufacturing analytics use cases often make the mistake of jumping ahead to predictive and prescriptive, when they haven’t yet implemented the necessary infrastructure to perform the basics.
Manufacturing analytics vendors often cheat here - they either narrow the scope of their offering so much as to be of marginal benefit for the larger business, or they offer mediocre insights that are nothing more than just copy-pasting raw data from the plant.
Manufacturing analytics, like all data projects is a journey, not a product. Make sure you work with a partner that knows how to guide you on this journey.
Speak to Our Experts
Connect with a 3AG Systems expert today and start your journey towards efficient and effective data management.