Industrial analytics platforms deliver business insights in near-real-time, often helping business leaders predict the likelihood of certain events occurring. Two related areas — business intelligence (BI) and the Industrial Internet of Things (IIoT) — can make industrial analytics even more powerful.
BI tools typically provide access to historical or current data about a company. These so-called descriptive analytics tell users what has or is happening. They can then use that information to make more confident decisions in challenging circumstances. Then, the IIoT encompasses connected machinery in industrial settings.
Here’s a look at how companies could use business intelligence and IIoT solutions to make industrial analytics more useful and valuable now and in the future.
Enabling Faster, More Accurate Conclusions
The ability to collect vast amounts of data was not an immediate help to many companies. When organizations lacked the resources to analyze it, representatives from those businesses often made decisions without looking at all available information.
One study revealed that 65% of businesses did not analyze or categorize all their collected consumer data. In such cases, representatives may overlook crucial details or make incorrect assumptions. However, BI and IIoT solutions could reduce those incidents.
Perhaps your company’s business intelligence software confirms a department’s declining profits for the past three years. You could then start getting to the heart of the issues by installing IIoT sensors on the machines used in that section of the business.
Do the metrics show unusual amounts of downtime, idle minutes, or excessively long changeover times? If so, you would have a clearer idea of which industrial analytics metrics to track and how to verify if progress occurred.
Helping Leaders Make Smarter Investments
The people in financial leadership roles at companies have the tough task of determining what purchases should have the highest return on investment. Using the IIoT and BI to help could reduce any financial expenditures that don’t have the desired effects.
It could also encourage people to look at unexplored avenues before making financial decisions.
Tackling Known Issues Regarding Company Equipment Needs
Consider a case where most members associated with a manufacturer’s research and development team deal with inadequate equipment. More specifically, they return from assignments and complain that the portable technologies used for gathering information and taking notes could not withstand the environments encountered during their field research.
Then, military-grade electronics marketed to consumers could address the matter. Specifications vary by product, but many items can operate underwater or withstand shocks, making them ideal for demanding environments.
Taking a look at BI data could show leaders which types of existing equipment pose the most issues for employees and why. It’s then easier to justify additional purchases of products that ease those identified challenges.
Collecting Evidence About Productivity Shortcomings
If a leader responsible for making a company’s financial decisions also explores IIoT sensor data, they might discover that certain investments could help the business meet its productivity goals.
Perhaps the initial examination of the data showed that a certain machine stops running for approximately 10 minutes every hour with no immediately obvious explanation. A more in-depth investigation that includes employee interviews might indicate that workers must halt the machine to restock the supplies they use while running it.
If so, investing in autonomous mobile robots (AMRs) could allow workers to send them to retrieve more supplies without shutting down a machine. However, without referring to IIoT data, a company leader may never realize that the lost productivity problem exists.
Minimizing Excessive Inventory
Successful leaders of industrial facilities know the importance of keeping enough inventory on hand to meet current and future demands. However, as you probably know, there’s often a fine line between “just enough” and “too much.”
Many businesses use industrial analytics suites to make their supply chains more resilient. Applying the IIoT and BI could bring even more favorable results during what’s often a daunting task.
Confirming the Effects of Equipment Outages
Maybe you struggle with having enough spare parts for your company’s critically important machines. IIoT data could give you reliable statistics such as how often certain parts require replacement and the average number of operating hours that can occur before a piece should get switched out.
Similarly, business intelligence information could help you assess the impact of machine outages over time. For example, maybe it shows that a component failure on a particular piece of equipment cost tens of thousands of dollars over the last two years due to downtime.
If so, it makes good financial sense to have extra parts on hand to prevent such scenarios.
Keeping Customers Content and Loyal
It may also become apparent that lacking enough of the right parts directly impacts how customers feel about your company.
That’s especially likely if you offer extended warranties that allow participants to have faulty products replaced. People would understandably get upset if they make valid warranty claims and hear it’ll take at least eight weeks to get the issues addressed due to a parts shortage.
In one instance, a global industrial machine manufacturer used the IIoT to build forecasting and availability models for its spare parts. Those outcomes caused a 90% increase in available parts while cutting inventory on hand by more than 30%. Customer satisfaction increased as a result.
BI tools could help in this scenario, too. They could indicate which products historically have the most warranty claims and confirm the locations of people who file them.
Guiding Decisions About How to Use Industrial Analytics
Implementing industrial analytics is typically a gradual process. That’s because most companies lack the resources to connect all their machines and company departments to make them simultaneously “smart.”
Research shows that many company leaders only have small segments of their factories connected to IIoT equipment. Costs pose concerns, and challenges crop up if a business mostly has legacy equipment that needs upgrades before it will work with newer tech.
It’s one thing to have a heavily connected factory. Does the connectivity extend to other areas like shipping and receiving, too? If not, you could miss out on valuable insights.
However, it’s understandable if your industrial analytics investments don’t happen immediately.
Digging into the data inside a BI tool could help you decide how and when to roll out your analytics plans. It might show that there was a 40% rise in customers complaining about out-of-stock items last year.
In that case, you might prioritize industrial analytics to support the supply chain. Alternatively, perhaps a piece of equipment overheated several months ago, causing a minor safety issue. Then, you may want to ensure the machine has an IIoT temperature sensor to prevent future mishaps.
Let BI and the IIoT Shape Your Industrial Analytics Choices
People often think of business intelligence, the Industrial Internet of Things, and industrial analytics as wholly separate entities. Distinctions exist among them, but they also overlap. The information here can help you use these offerings effectively.