What is Industrial Analytics?
Industrial analytics refers to the process of collecting, analyzing, and using data taken from industrial operations. In many ways, it’s the same as other types of advanced analytics, but it relates specifically to operational data and is used to create industrial value. It includes techniques for data collection and statistical and dynamic modeling.
Christoph Groger, enterprise architect for data analytics at Bosch, described it as “an interdisciplinary subject area between data science and industrial engineering and is at the core of Industry 4.0.” The global industrial analytics market is expected to reach $55.3 billion by 2029, increasing at a CAGR of 16.6%.
Industrial analytics is both created by and reliant on digital transformation. The proliferation of Industrial Internet of Things (IIoT) devices increased the mountain of data generated within a process manufacturing plant, and manufacturing companies want to benefit from that data. At the same time, only artificial intelligence (AI) and machine learning (ML) systems can analyze so much data.
Why Does Industrial Analytics Matter to Process Manufacturing Plants?
Process manufacturing plants look to industrial analytics to help them unlock the value that’s lying within their enormous datasets, so they can use the insights to improve plant efficiency, raise product quality, and make better business decisions.
With the help of insights from industrial analytics, process manufacturing companies can:
- Gain a better understanding of what takes place within production operations, including those that were previously impenetrable
- Find new ways to resolve production challenges, maximize resources, and improve plant performance
- Monitor, assess, and reduce consumption of raw materials, water, energy, and other resources
- Refine maintenance schedules (predictive maintenance), extend machine life cycles, and cut downtime to boost overall equipment effectiveness (OEE)
- Understand the root causes of manufacturing errors in order to resolve and eliminate them, so as to improve both product quality and plant efficiency
- Discover new market opportunities for competitive advantage and fine-tune marketing and pricing
- Innovate faster with more reliable and real-time data
How Can Process Plants Implement Industrial Analytics?
Make your data accessible
A typical process plant is already churning out masses of data, but that doesn’t mean that industrial analytics tools can easily analyze it. The first step is to remove data silos that lock data away, store it in formats that your analytics tools can process, and ensure that sufficient context is included to make the data meaningful.
Implement edge computing
By processing and computing industrial analytics in edge systems, you’ll be able to both increase security around your valuable data and speed up analysis to take advantage of the potential of real-time data insights.
Educate your employees
To see the true value of industrial analytics, it needs to be part of a broader shift to digital culture. Industrial analytics requires buy-in from the entire workforce so that everyone is ready to accept and trust industrial analytics insights.
Update your human resources
As a form of advanced analytics applied to manufacturing-specific scenarios, industrial analytics requires deep data science expertise together with a full understanding of the process manufacturing industry. Not every plant has employees with both the necessary technical skills and plant knowledge. You might need to upskill some workers, hire new tech talent, onboard external consultants, and set up ways for plant experts and data scientists to work together smoothly.
How Do Process Plants Benefit from Industrial Analytics?
Applying industrial analytics to your process plant delivers the real-time, deep insights into plant operations that plant managers need in order to boost OEE, cut downtime, raise product quality, and make effective business decisions that ultimately increase revenue and customer satisfaction.