Smart manufacturing opens the door to real-time data
In today’s digitally transformed manufacturing world, smart manufacturing and Industrial Internet of Things (IIoT)-enabled technologies have opened the door for manufacturers to improve business efficiency and enhance operational process capabilities. The IIoT enables machines to talk to each other and collect and share large amounts of data. Manufacturers need to capture this data, learn about it, and then drill down to get to root causes. Then they can use this real-time data to optimize processes and reduce costs.
Just the facts
Many manufacturers interpret data differently and end up with several versions of the facts, which makes it almost impossible to find common ground. Manufacturers need to find one set of data based on facts. To do so, think about data in terms of time series, structured, and unstructured data. That may sound a bit simplistic, but it’s a good place to start capturing and organizing facts. Then manufacturers can start to analyze and use the information.
While some might think data scientists are necessary for data analysis, it’s worth considering using intuitive analytics software instead. In the smart manufacturing world, that debate doesn’t really matter as there may be times when one or both are needed. What does matter is analyzing the data and going deep to find the root cause of what’s happening within the facility.
Why analyze data? To find root causes, for sure, but what else? There are four basic elements that help describe the various types of information manufacturers need:
First, look to get descriptive information from the data. What happened? This data provides the basic facts and description: what, when, where something happened, and who was there. This first level is usually easy once the user has one version of relevant facts.
Second, extract diagnostic information from the data. Why did it happen? This is where it gets more difficult. This is the root cause. And the root cause of the root cause. This requires asking why and then asking why again, multiple times. With this type of drill-down approach, the data starts to produce answers to what happened and why it happened.
Third, get predictive information out of the data. What will happen? If everything stays the same, and everything keeps going in its current direction, what will happen next? This data is more difficult to obtain, but it’s also where data starts to get valuable. Predictive information tells the user what’s going to happen, when it’s going to happen and why.
The fourth and final element, prescriptive information, asks what needs to happen, and what action is needed. This is the most difficult analytical aspect, but it’s also the most valuable. The data should provide information on what action to take to keep something bad from happening or to help ensure something good happens. The data also can help find ways to optimize and make processes better.
Beyond basic analytics, data is a strategic asset and can be valuable as a differentiator in the marketplace. Some don’t realize the value of the data until it’s used to make significant changes to production processes.
Manufacturers can create new revenue streams from the data they’re collecting. They can strategically use smart manufacturing and create new business models and new value for customers. Other benefits and improvements include:
- Increased product quality
- Reduced costs and rework
- Minimized waste and energy usage
- Increased consistency
- New production capabilities and improved speed
- Being able to proactively prepare for future customer or regulatory requirements.
To make these strategic decisions, look at three other aspects that make data valuable:
1. Fast and actionable
Data needs to be fast and actionable. It needs to be in real time to be valuable. Generally, the older the data, the less valuable the data is when it comes to changing what’s going on right now on the plant floor. Getting the right data to the right person at the right time, in real time, is a very powerful concept. It supports decision-making and allows people to apply problem-solving skills to make significant operational improvements.
People need to know what the data means and what it says so they can do something meaningful with it. The data needs to relate to the task and have the context around it regarding what needs to be done now. Even if the data is important, it still needs to be relevant to what’s happening and to the real-time problems people are trying to solve.
Data needs to be smart and lead to knowledge and ultimately wisdom. The data needs to be intelligent whether or not anything resembling artificial intelligence (AI) is applied. It needs to create meaning based on the context and on other relevant data that addresses the immediate problem. Fundamentally, the data needs to help make people smarter — not just in the moment to solve a problem — but also in the long run. In this way, the data, knowledge, and wisdom are cumulative over time.
In this era of Big Data, don’t miss data’s true value. Don’t just analyze it and decide to adjust something up or down by half a percent. Think about making major decisions with this data. Maybe making that half-percent adjustment was the right thing to do at the time, but use the data on a larger scale to make decisions about business, manufacturing and technology strategies.
Let the data help drive decisions about where to go in the market, how to better serve a customer, how to deal with competitors, how to take advantage of opportunities and more. Smart manufacturing is where real-time data can optimize processes and impact a manufacturer’s business.
Keywords: Real-time data, smart manufacturing
Real-time data drives how decisions are made to improve manufacturing processes and operations.
Manufacturers need to consistently interpret the data to create an agreed-upon set of facts.
Real-time data needs to be fast, actionable, but above all: smart.
How is your company improving real-time data processing, and what are the expected results?