Industry 4.0: dealing with Big Data
Big Data is one of the key themes that arises when discussing implementing Industry 4.0 solutions and practices in the manufacturing sector.
With the digitalisation of equipment, and ever smarter systems that are communicating across the virtual and physical divide, comes ever growing bodies of data. In today’s world there are enormous amounts of data that are spread across all industries and sectors and at almost every interaction in our lives. Being able to take advantage of this in a manufacturing setting can allow value capture in many different forms. Below are just a few examples:
• Improvements in product quality
• Intelligent, data driven design
• Customisation of products, services, and solutions
• Smart preventative maintenance
• Live tracking of production allowing fine-tuned adjustments that are responsive to feedback
• Data driven economic growth
However, before we can start to explore and tap into this value, there are several unique hurdles and challenges that companies need to overcome in order to capture value using Big Data. Modern manufacturing is complex, even in the smallest of scales. In our factories and businesses, we have endless sensors, machines, computers, and people interacting and communicating across various mediums.
One of the primary challenges is addressing the right types of data that needs to be collected, processed and analysed by a system before looking too far forward at the result. For newer players, this can be extremely daunting. Especially when trying to quantify and comprehend data flows at various levels of a company’s operations.
While there is no single solution, a simplified approach can involve looking at smaller sub systems and process operations and asking the question of how to break down or aggregate smaller pockets of usable, relevant data for a new system to be designed around. At the first stage of implementation, data driven systems and solutions should be developed to allow for interoperability and scalability. Such as developing smart tool, part, stock, or supplier libraries.
Another consideration might be using data and digitalisation to assist operational staff on the factory floor. By providing staff on a factory floor live information through work instructions that are fed by data can have enormous benefits in efficiencies. Data can be processed in real time from machinery and equipment sensors and fed through to a worker though a tablet or similar interface. An example is a dynamic augmented reality interface that can guide an operator on the physical placement and setup of a part on a jig or fixture. As processes in manufacture are always changing, an augmented work instruction interface can change in real time alongside small changes or adjustments made in processes without having to stop production and retrain staff.
While relying on and using data has enormous benefits, there are some problems that technology can’t solve. No machine or computer-driven system can beat human intuition and knowledge-based learning that is often embedded and required in many manufacturing processes. Therefore, it is critical that the operational technicians are also trained and involved in the development and implementation of new data driven systems. Engineering technologists can be important assets to include in your team at this will allow an interface between the workshop floor and how data is analysed, processed and fed back down to operations through system design.
Integrating the human element into these systems is very valuable. By giving specific operational staff members the ability and task to input their problems and their opinion, processes can be improved with minimal time required for change to be implemented. Next to that logging of changes and errors in the system can be used as valuable feedback on how the system and processes can be improved.
To find out more about breaking down your big data, get in touch with our team through our contact form or email us at firstname.lastname@example.org.