Most manufacturers realize if they had the right data they could improve their manufacturing operations. But, how? Every manufacturing company wants to make a high-quality product, as quickly, and cost-effectively as they can. Having the right data on production processes can help improve quality, update, and increase throughput.
But what are the right systems to use to collect and analyze all of the data your machines and sensors producing?
In this blog, we are going to compare manufacturing analytics vs. Tableau and discuss which one is the right tool for the job.
Many companies look at manufacturing analytics applications and believe that Tableau or other business intelligence software can accomplish the same goals.
They are wrong.
The two are very different solutions designed to do very different things. It is true that there is some overlap between the visualization capabilities of manufacturing analytics and Tableau, but that is where the similarities end.
Below we will discuss what Tableau is and what it is not, as well as do the same with a manufacturing analytics solution like Mingo.
What is Manufacturing Analytics?
As its name suggests, manufacturing analytics is an application that helps customers understand their data and beyond. A true manufacturing analytics application must do the following:
- Acquire data
- Clean & contextualize data
- Calculate manufacturing KPIs
- Produce role-based visualizations & dashboards
A manufacturing analytics system, like Mingo, must have the ability to collect data directly from the equipment on the factory floor. It should be able to connect directly to the PLCs, sensors, or machines. With connection and input from people on the line, it should extract meaningful information.
Clean & Contextualize Data
The data should be cleaned as it is collected as all data coming from the machines may not be relevant and should not be processed.
Some data should be filtered inside the plant, and some, once the data is sent to the cloud. This is done to reduce the noise and make sure the data collected is ready for display and calculations without further processing.
As the data is ingested, context is added. Additionally, the reasons why a part was scraped or why a line was down can be added to help with root cause analysis. Without context the data is meaningless.
Calculate Manufacturing KPIs
KPIs and metrics are key to any continuous improvement project and you must have the right metrics. The system will calculate things like OEE, cycle times, first-pass yield, and downtime in near real-time.
These are standard formulas that come out of the box in a manufacturing analytics solution and don’t have to be created from scratch.
Being able to visualize the data in meaningful ways is extremely important. A user wants the right data at the right time, and they don’t want to look for what they need.
A manufacturing analytics system shows users the right data, at the right time, nothing more and nothing less.
Most systems have default dashboards by job role out of the box so everyone from maintenance to plant managers to supervisors and schedulers have the data they need to do their jobs.
What is Tableau?
Quoting from the Tableau web site “Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.”
Tableau is very good, if not the best in the market, at creating beautiful visuals and connecting to many different types of databases. Many companies have built manufacturing dashboards using Tableau. There are hundreds of different types of plots and charts a user can build, allowing them to try many different options to display data exactly the way they want it.
Tableau can be used to display role-based dashboards, similar to what is displayed in a manufacturing analytics solution, but it can also be used as a data discovery tool. Data discovery allows users to dig through the data and build their own visualizations. This can be very useful for more technically advanced users.
Tableau can be deployed in the cloud or on-premises and customers typically pay-per-user or per-server license fees as well as designer licenses.
When deploying Tableau, you need a technical user that knows how to connect to your data, set up the server, and configure the initial data sets. When using the cloud system with on-premises databases, data must be synchronized with the server. Which means you can have a lag of up to an hour for the data to refresh.
When the data sets are built you can create formulas to calculate things like OEE, cycle time, etc. and display those on the dashboards or visualizations.
What Tableau is Not
As you can see, Tableau is missing some critical pieces of a manufacturing analytics application.
- Tableau does not acquire data; it relies on existing databases. Companies must deploy data acquisition solutions in each factory and synchronize that data into Tableau.
- Tableau does not clean or contextualize the data. The existing data must be processed to add production standards, shifts, part numbers, etc. This can be very time consuming and expensive to implement.
- Any KPIs a customer wants have to be built from scratch in Tableau, nothing comes out of the box.
- Finally, each company must define from scratch how they want to view their data.
These missing pieces are expensive to implement and require a diverse set of skills, an understanding of the PLCs and controls from which you need to gather data, the ability to process and contextualize the data, and finally an understanding of key manufacturing KPIs.
The Verdict: Manufacturing Analytics vs. Tableau
If you are looking for a general-purpose business intelligence tool that connects to any database, Tableau is the right tool for the job.
However, if you want a quick to implement, easy to use solution that can help you reduce downtime, improve quality, and performance, manufacturing analytics is the right tool for the job.
Manufacturing provides the visibility and accountability needed to improve the factory floor, without the headache most other solutions give.