Industry 4.0 – The Value of Data Analytics in the Smart Factory

Today’s manufacturers must perform operations at rapid speed to maintain pace with the ever-changing customer needs, market trends as well as global competitors in a continuously changing marketplace. Embedded sensors and connected production equipment, likewise conveyors, demands and fastening tools, can give manufacturing engineers and plant managers lots of data. That data allows better and faster decision-making within factories and makes constant improvements more disciplined.

Data analytics can also assist manufacturers to improve productivity, minimize lead times, remove errors and speed time to market. But, most of the data is left unused without having software to gather, monitor and display it. The surge in data-driven manufacturing is also generating many new challenges and trouble for manufacturers.

Data analytics is one of the latest trends in manufacturing today. It can benefit large as well as small manufacturers in all types of organizations.

As per McKinsey & Co. with data analytics, manufacturers can go in-depth into historical process data, recognize patterns and relationships among discrete process steps as well as inputs and then improve the factors that have a significant effect on yield.

Data analytics basically is the process of taking out insights from a set of data. With accurate technology, this data can be collected and explored from just about any process or any section of production equipment.

Numerous Benefits of Data Analytics in the Smart Factory

Utilizing the potential of data analytics, manufacturers can accumulate, sort and monitor the huge volumes of data generated by the four M’s ( Man, Machine, Materials, Method) of manufacturing, integrate structured and unstructured data and transform this data into actionable insights. Following are some of the benefits:

Enhancing Quality

Uncertain product or component quality can minimize yields, generate costly waste and ruin production capability. For instance, a manufacturer whose polymer mixing process continues to manufacture products of inconsistent quality. They must remove poor batches which will result in immense costs and compromised production capacity. In the worst case, if left undetected, then this material issue generates inferior product quality down the line and can tend to losses of millions of dollars in revenue.

A data analytics platform that combines a huge range of production and sensor data could visualize, monitor and recognize the mixing process. As an outcome, the production engineering team can interpret the correlations and can cause/effect, leading to uncertain or poor-quality output from a huge range of variables.

This kind of data analytics removes poor-quality output. It supports manufacturers to improve average yields and minimize operating costs as the focus shifts from eliminating poor batches to optimizing manufacturing processes.

Amplify Performance of Critical Assets

Equipment failures can charge manufacturers millions of dollars in downtime as well as productivity loss. The survey of the auto industry manufacturing executives by Thomasnet.com revealed that every minute of stopped production costs near an average of $22,000. Advanced data analytics support to neglect production shutdowns by analyzing sensor, event as well as historical data to recognize trouble spots. With the help of prescriptive analytics, manufacturers can dynamically recognize machinery requiring attention and direct necessary repairs under controlled conditions before it stops working.

If we consider air compressors, a key part of most manufacturing and production processes. By using a condition-monitoring system that uses data analytics, manufacturers can track the machine’s overall performance in real-time. The system will also send alerts about machine conditions, that could lead to failures and unscheduled production interruptions. Recognizing inaccuracies at the initial stages of a production cycle can also give a critical benefit.

Increasing asset uptime can also remarkably enhance plant productivity. An IoT perspective can result in a 20% to 25% improvement in production volume and a 45% reduction in downtime. By removing unknown problems likewise equipment failure, manufacturers can optimize production schedules as well as minimize lead times, for enhanced quantity and quality of end goods.

Utilizing analytics to analyze production line quality can support recognizing quality problems instantly and improve yield. Additionally, monitoring historical equipment performance data and real-time data can minimize maintenance costs and improve equipment availability. Manufacturers can also more easily stick to factory security and regulatory compliance standards by taking the time to transform IoT-generated data into actionable insights, likewise how to prevent system vulnerabilities.

Improve Productivity 

Data analytics can support manufacturers to boost productivity and streamline assembly operations. With the help of IoT-enabled sensors and data analytics, engineers can estimate equipment vibrations to interpret what ‘normal’ operations look like. Whenever vibrations started to fall out of that fixed pattern, they have prior visibility into a pending failure, which enables time to plan and schedule repairs, avoiding or importantly minimizing downtime.

Streamline Assembly Operations

Data analytics also allows manufacturers to track the amount of time it takes products to move via various areas of the assembly line. If the overall data informs management that one area is moving slower than others, they can search for the root cause and resolve the concern, which can vary from staffing inequalities to standard asset performance. Proactive measures allow manufacturers to perform at their optimal capability.

Analytics passes real-world inputs to real-world outputs leveraging the speed and power of the digital world to combine more information, embrace it and expand operations for better business outcomes, developments in sensor technology, connectivity, communications networks and the cloud mean that manufacturers can address challenges that previously were either too complex, too expensive and time-consuming or lacked sufficient information to discover a root cause and validate.

To maintain pace with fluctuating market trends and meet emerging customer expectations, manufacturers must adopt IoT technologies and leverage data analytics to bring insights into asset health, output quality as well as operational processes. Data analytics allows to capture and monitor data to acquire meaningful insights into manufacturing challenges.

Explore how the hIOTron machine monitoring system allows enterprises to leverage operational data throughout the whole data lifecycle for improved value and minimized risk through data analytics and IoT platform.