Five Key Issues Facing Industry 4.0
The fourth industrial revolution aka industry 4.0 is the optimisation and automation of industrial processes using modern smart technology and analysis.
Driven by the Internet of Things and an explosion in connected devices, it is set to be a defining trend of the next decade, with estimates putting the market size as large as $156 billion (€129.9 billion) by 2024.
Information that governs and affects logistical processes is being recorded at an astonishing rate and moreover, can now be accessed and shared in an automated fashion.
Different components within operational systems can adjust accordingly as and when a change occurs to optimise efficiency. It includes anything from the volume of a particular commodity available, all the way to devices that use sensors to measure real-time data such as weather or traffic.
The information is seeing huge opportunities in healthcare, autonomous vehicles, smart cities, infrastructure such as power grids and many more.
The sheer volume of data being used has led to a challenge in its interpretation and conversion into business actions. Accessing the numerous benefits requires solving five key challenges facing Industry 4.0 and Digital Twin technologies.
- Abundance of Data
Having so many connected devices produces a whopping amount of data. This makes for a noisy environment, where only a select amount of information is actually relevant at one time.
A network relevancy solution might address this by using spatial data structures to efficiently query which information is relevant to each client. Entities found using these queries are scheduled to be sent to the client based on metrics that correspond to the importance of that entity, enabling less important entities to be sent less frequently and reduce bandwidth.
- Unpredictable Loads
Making sense of the noise is one problem, but this data doesn’t always flow in an even manner. Surges often occur and they can produce a sudden high demand for computing power.
Simulation systems that are ill-equipped to deal with this may be prone to crashing. Dynamic scaling is a fitting solution to this issue. This is where a system scales across different processors and physical machines, utilising more computing power as the simulation grows in complexity and size.
Virtual space is mapped to “CPU space”, with more cores allocated to areas with a high number of entities, or in non-entity simulations, a high compute density.
- Restricted Number of Connected Devices
With the huge number of IoT devices driving this change, simulation infrastructure needs include a sophisticated networking model to connect them all. Without one, systems can be prone to crashing when they have a limited number of active connections.
Having an asynchronous architecture can deal with this problem effectively. It handles the actions of thousands of devices and distributed load balancing ensures the network always has enough CPU to handle large influxes.
This eliminates the need of having a single thread per device and handles the control events sent by each one, forwarding them back to the simulation - the events are then reconstructed into a complete world state and then stored in a data structure.
- Latency Issues
The flexibility of the cloud provides a number of advantages, from cost-efficiency to greater computing power. However, the physical distance between users and the servers naturally increases latency.
Time-sensitive variables are highly common in these simulations and so require a system that can supply real-time data streaming. Edge computing offers a solution by processing the data closer to the originating source.
- Disconnected Data Sources and Types
Often information gathered in the simulation comes from disparate sources and differs hugely in its makeup. Things like social media, IoT devices and supply chain analytics can provide important insights, but they have to be synthesised together into one cohesive structure.
This interoperability of simulations is essential to reflecting the complex nature of modern workflows.
Modern solutions will ultimately need to plug into existing architectures to reduce the barrier to entry. Solutions will need to manage the configuration of all infrastructure, server management and maintenance regardless of the specific process it underpins.
It is likely this will reduce upfront costs, simplify development processes and ensure immense scalability.
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