Business intelligence software has never been a hotter tool for just about every industry. Recent acquisitions of major providers, like Tableau, have highlighted just how valuable businesses view technology that helps businesses organize, sort, and make sense of their vast amounts of data.
For manufacturers, the allure of business intelligence is the ability to look deeper into production, performance, and efficiency. Naturally, data is disconnected and often siloed on the factory floor. What’s more, it is not uncommon for data to be manually collected in spreadsheets and reorganized and reviewed retroactively.
This is the promise of business intelligence. The ability to pull data together in a way that is both actionable and accessible to those working with machines processes on a daily basis. But, is this how it is really being used?
What is Business Intelligence in Manufacturing?
Manufacturing intelligence and business intelligence can be used interchangeably, but business intelligence in manufacturing is analytics that provides insights into manufacturing processes. It may sound complicated but using data analytics can help manufacturing make informed decisions.
Business intelligence looks slightly different for every manufacturer, but most of the basic concepts are pretty uniform. The ability to assess machine and cell performance, understand uptime and downtime, monitor production performance, and see things like bottlenecks in real-time. These are examples of the types of core ways that manufacturers want to use business intelligence.
Here are some examples of the types of analytics that manufacturers commonly want to see with business intelligence software:
- Machine/cell performance (uptime, downtime, reasons for both)
- Scrap rates, production performance, quality (actuals)
- Throughput (scheduling, production vs. actuals)
- Real-time analysis of bottlenecks and shift performance
What Types of Business Intelligence Software do Manufacturers Use?
Manufacturers typically rely on one of three solutions for data analytics and business intelligence.
- Spreadsheets / manual data entry
- Custom-configured business intelligence solutions from large publishers
- Prebuilt manufacturing analytics that come with preconfigured dashboards
Spreadsheets (yawn!) are the traditional, old school way of gathering intel. The most inefficient and difficult way to scale data analytics is obviously with spreadsheets and manual data entry; however, this doesn’t mean all is lost. In most cases, there are easy ways to get off of paper.
The biggest issue with spreadsheets is the inability to see data in an organized fashion and in real-time. This means that often manufacturers will see issues with something, like unplanned machine downtime, and not know about it until it’s too late to do anything about it. What’s more this retroactive look also means missing the opportunity to evaluate why the downtime occurred.
Example of just how complex it can be to draw reports or dashboards in systems, like Tableau
The challenge with major business intelligence software (BI) systems is how difficult it can be to get them to work properly for manufacturers. Major software publishers (GE, Microsoft, Google, Oracle, etc.) have all built or acquired business intelligence technology that is extremely flexible so that it can fit multiple industries.
This means that the software itself is not meant to track machines, machine data, manufacturing processes, or anything else we mentioned above.
Manufacturers need to invest in 1 of 2 things (and in most cases both) to make popular BI solutions work:
- An expert to “implement” the software and teach technical employees to create reports
- IT experts on staff that can connect the software and draw reports and dashboards on demand
It is not intuitive to configure and setup business intelligence solutions, like Microsoft’s Power BI or Tableau, for a manufacturer even though manufacturers are often touted as perfect fits for these types of solutions.
While the business intelligence software mentioned above may sound like they can provide solutions, that is not necessarily the case. As we go further in-depth on this topic, you’ll discover the easiest and most affordable solution of the three is specialized manufacturing analytics.
What’s the Difference Between Business Intelligence Software and Manufacturing Analytics?
Manufacturing analytics systems have 3 features that make them a much better solution for the majority of manufacturers seeking some form of business intelligence software. The benefits of a manufacturing analytics system over business intelligence software are:
- They come pre-configured and can easily plug-in to existing machines and processes
- Feature dashboards that are already built to display and highlight commonly needed info
- Don’t require a technical implementation or IT resources
Not many of these systems existed for manufacturers several years ago but now have emerged as a top option.
Is Business Intelligence Software Recommended for Most Manufacturers?
It really depends on how we want to define business intelligence. The amount of data created and collected by manufacturers continues to grow. Being able to translate that data into insights that can improve business outcomes is critical to leveraging the power of the IIoT, manufacturing 2.0, and all the other forward-thinking buzzwords you can imagine.
The tools manufacturers use to do this will really depend on where they are starting from. In most cases, manufacturers are lean businesses that are often strapped for additional resources. Major technology investments with lengthy implementations don’t make much sense.
Most will benefit most from getting all the data with none of the hassles of technical integrations and configurations. This is why most manufacturers are likely looking for manufacturing analytics solutions vs. traditional business intelligence software.
You can learn a little more about how even the most unsophisticated manufacturers can get started with these kinds of solutions here. For everyone else, these solutions still offer the best value for the level of effort required to begin exploring the value of better data and better insights.
With data at your fingertips, your company will be able to make those data-driven decisions that can make a positive impact on your bottom line.