In the digitised industrial world, data rules. Intelligent data is crucial for companies in the industrial OEM sector to make faster business decisions. With the growth of the Industrial Internet of Things (IIoT) and other data-driven technologies, industrial OEMs can now collect more data from their machines and equipment than ever. However, this abundance of data can be overwhelming and cause a paralysing analysis of the massive amounts of data generated.
Industrial OEMs now recognise that the key to growth and success lies in their ability to harness all this data and turn it into actionable insights. By gathering and analysing data, industrial OEMs can identify areas for improvement, increase efficiency, reduce costs, and enhance overall business performance.
This article explores how industrial OEMs can transform data into faster business decisions.
Harvesting data from connected machines and equipment
The first step in transforming data into faster business decisions involves collecting data through advanced sensors and machines. Industrial OEMs need a reliable network connection to their machinery to collect data for practical analysis. With the field sensors communicating with cloud technologies, it is now easy to gather data from equipment in real-time and process them in many different ways. Clean and meaningful data enable OEMs with powerful insights that help critical functions like remote monitoring and predictive maintenance.
Systems and networks must extract a wide variety and mix of industrial data from several devices in the field. It could include operating parameters like temperature, pressure, voltage, and amperage data. Collecting real-time data from the equipment will allow OEMs to monitor the machine’s near real-time performance and identify improvement areas.
Analysing data to identify patterns, trends, and insights
Once the data is gathered, the next step for industrial OEMs is to analyse it to extract valuable insights. Such studies involve using machine learning algorithms to identify patterns and trends that would otherwise be difficult to spot. By leveraging the power of artificial intelligence, machine learning technologies, and predictive analytics, industrial OEMs can quickly identify shifting trends, anticipate potential problems, and make rapid decisions based on that data.
The advent of data analytics has made this process easier by providing tools and techniques that make it easier for analysts to analyse data quickly. This analysis could include examining data at a granular level to identify unique patterns, forecasting future trends using predictive modelling, and even identifying potential issues before they become critical. Data mining, a technique that uses algorithms and statistical models to extract essential information from large data sets, can be used to identify patterns and insights.
Transforming data into actionable insights
The ultimate goal of data collection and analysis is to generate actionable insights. Industrial OEMs need to convert data into information that can be used to drive faster business decisions. With error-free data, industrial OEMs can identify inefficiencies, evaluate performance, and make process improvements that lead to lower costs and increased efficiency.
By leveraging predictive analytics, OEMs can predict future outcomes and adjust course as needed. For example, identifying the most common cause of equipment failure can help OEMs plan maintenance ahead of time to avoid costly breakdowns, which increases uptime and revenue.
Intelligent data systems help data-driven decision-making for everyone. Live dashboards or mobile applications are a great way to achieve critical decisions. This, in turn, makes it easier for operators to take necessary actions.
Leveraging data to optimise operations
The insights generated from data analysis can help industrial OEMs optimise different aspects of their business. This information allows manufacturers to adjust production lines, reduce machine downtime, and improve supply chain efficiency.
For example, analytics can identify bottlenecks in production lines, enabling manufacturers to make changes that increase productivity. Similarly, logistics and supply chain operations could be optimised using data to reduce logistics costs, improve supplier performance, or optimise inventory levels.
With the right DataOps, organisations can make data democratisation a reality. Automated analytics in real-time enhances data usability and readability. Such a single source of truth also enables data security and compliance which is non-negotiable from both performance and workforce-safety points of view.
Implementing ongoing improvements and monitoring performance
Improving the use of data is a continuous process. Industrial OEMs must regularly track results, monitor performance, and assess progress. Based on the priorities and goals, operators, supervisors, and field engineers can collectively take necessary steps for improvements.
Monitoring and reporting should be part of the business process to monitor performance regularly. Such practices, when implemented regularly, ensure maximum productivity. On these lines, the decision-makers can set the organisation’s KPIs to monitor the progress and performance of the overall infrastructure.
Based on recent studies by global Institutes:
- Data-driven organisations are 3X more likely to report significant improvement in decision-making
- Data-driven companies are 58 per cent more likely to beat revenue goals than others
- Data-driven organisations are 23 times more likely to acquire customers
Conclusion
In today’s digital age, data analytics is no longer an optional component for businesses in the industrial OEM sector. To remain competitive, industrial OEMs must leverage data insights to drive faster business decisions, reduce costs, increase efficiency, and accelerate growth.
By focusing on data collection, analysis, and transformation, industrial OEMs can identify where improvements need to be made, implement process enhancements, and ensure compliance. This enhanced visibility can enable them to make informed decisions quicker and provides a competitive edge. Additionally, leveraging data analytics enables customers to understand their machines’ performance, enabling informed decision-making.