Data analytics in manufacturing has always been important, but it is particularly crucial for businesses today as it allows companies to make better decisions and more accurate predictions as well as work more efficiently. That’s exactly why it is needed in a field like manufacturing. Without further ado, here’s a guide to data analytics for manufacturing.
What Is Data Analytics?
In the context of manufacturing, data analytics refers to the utilization of various operating systems and machine data to improve the way a manufacturing company functions. The data you collect and analyze could be useful for increasing productivity, improving performance, maintaining product quality, optimizing decision-making, decreasing costs, and more.
Experts predict that the Industrial Internet of Things (IIoT) will become even more important in the future than it is already with manufacturing enterprises becoming more connected internally. This is precisely why data analytics will become more critical – it is essential for implementing IIoT technologies successfully.
When analyzing data in manufacturing and pairing it with IIoT, you reduce the risk of human error. All machines and devices in your factory will be connected and set up to perform almost all tasks automatically which ultimately leads to all the positive benefits of data analytics listed earlier.
The most important thing about data analytics in manufacturing is that it provides you with real-time contextual awareness. This means that you can make decisions during the process based on a variety of factors and circumstances that arise rather than making decisions beforehand that might become counterproductive during the process. On the other hand, data analytics also allows you to make predictions which is useful for long-term planning.
Essentially, you can apply data analytics in almost every aspect of manufacturing. From order management and transportation analytics to real-time quality monitoring and inventory management to demand forecasting and real-time equipment and process monitoring. Data analytics is useful at different stages and spheres of the manufacturing timeline.
What Is a Data Analytics Pipeline?
Speaking of timelines, there is actually the so-called data analytics pipeline that you should be aware of to be able to utilize data analytics effectively. The pipeline is circular in nature and consists of five different stages:
- Collect – During the first stage, you need to collect or ingest the data from the different data sources you have chosen. For example, it could be data about your orders or data about transportation (e.g. transportation time).
- Process – During the second stage, you will have to process the data and potentially enrich it (if necessary). This step is important because your system will need to be able to analyze the data, so you need to convert it into the right format for analysis.
- Store – At this stage, you will either need to store your data long-term in an archive or store it for further reporting and analysis. In most cases, you will store data in a special data warehouse.
- Analyze – The fourth stage is quite straightforward. This is when you analyze your data with the help of various analytics tools.
- Deliver – At the final stage, you will have to make conclusions based on the analysis outcomes you get. You can apply machine learning to make predictions or create reports for yourself and your team.
What Are the Types of Data Analytics?
Besides understanding the five-step pipeline of data analytics, you should also understand the different types of data analytics. Michael Welles, an expert from the writing service Trust My Paper, says, “Data analytics is not all the same. Different approaches and methods should be used for different purposes, understanding different types of data analytics is crucial for using it effectively.”
There are four main types of data analytics that you can utilize in manufacturing, descriptive, diagnostic, predictive, and prescriptive analytics. Here’s how they differ from each other:
Descriptive analytics can be summed up in a single question: “What happened?” This is the simplest type of data analytics and the most commonly used one. You will be using data to detect trends and understand what is happening at the moment or what happened in the past. You can gain a lot of insight into your processes which will help you find a better approach to similar processes and problems in the future.
The best thing about descriptive analytics is that it doesn’t require much-advanced knowledge about data analytics which is why it is easier for businesses to adopt. It can already answer most of the questions manufacturers have about their businesses (e.g. regarding performance) without having to perform complex analytics.
Diagnostic analytics aims to find the root cause of an anomaly. This kind of analytics is used to break down data and identify the causes or reasons for a specific problem, event, or behavior. It aims to get to the root of the issue.
Diagnostic analytics is usually performed in three stages. First, you will need to identify the anomalies you are interested in. Second, you will need to drill into the information or data available about the anomalies. And finally, you will need to establish the causal connections to determine what was behind the anomalies.
Predictive analytics is similar to descriptive analytics, but rather than looking solely at the past, it looks into the future. This is when you would be asking the question, “What is likely to happen?”
A good example of predictive analytics is real-time monitoring which can alert you about any significant changes in trends. As a manufacturer, you can use predictive analytics when ordering materials or scheduling production so that you can avoid stockouts or waste. In this case, you would be using customer demand during the decision-making process.
Prescriptive analytics is best used for suggesting a particular course of action. It can help you prescribe a specific path that you want your organization to stick to, but it can also be used to overcome various challenges.
Out of the four different types of data analytics, prescriptive analytics is by far the most advanced one. But if you are able to master it, you will be able to detect the points that have to be improved in your processes so that you can get the greatest and most immediate impact as a result.
How Should You Use Data Analytics in Manufacturing?
As you can see, data analytics is an important part of manufacturing. This is why it is necessary to fully realize its potential. You can use it for things such as:
Predictive Maintenance – Maintain your machines and software in a smarter way
One way to use data analytics in manufacturing is through predictive maintenance. By monitoring and analyzing equipment data, manufacturers can identify potential issues before they become major problems and schedule maintenance proactively.
Higher Productivity – Increase the productivity of your manufacturing business
By analyzing data from production processes, manufacturers can identify bottlenecks, optimize workflow, and improve overall efficiency. This can help businesses to produce more goods in less time and with fewer resources, ultimately increasing their bottom line
Improved Security Quality – Improve the quality of your safety and security
Data analytics can also be used to improve safety and security in manufacturing. By analyzing data from safety systems and security cameras, manufacturers can identify potential hazards and security threats, and take proactive measures to mitigate them.
Reduced Costs – Decrease the costs associated with different processes
Finally, data analytics can help to reduce costs in manufacturing. By analyzing data from different processes, manufacturers can identify areas where they can save money and reduce waste.
All in all, streaming the data in the manufacturing process can be a valuable tool for manufacturers. By collecting and analyzing data, manufacturers can identify trends, patterns, and insights. This can lead to better decisions and improved processes. Whether it’s improving supply chain management, predicting equipment maintenance, or monitoring quality control, data analytics can help manufacturers optimize their operations and improve overall efficiency
Nancy P. Howard has been working as a journalist at an online magazine in London for two years. She is also a professional writer in such topics as blogging, SEO, and digital marketing.