Manufacturing industries have been categorised for their volatility and continuous technological advancement which changes the industrial landscape every now and then. Smart manufacturing also known as IIoT has promised to make the operations and processes much more efficient. With the improvement in technologies like cloud storage mechanisms and predictive analytics IIoT is no longer a distant reality. The machinery and other assets can be controlled remotely which can reduce the machine downtime and can bring the resources to maximum use.
But with new technologies, new and unforeseen challenges have come up which need to be tackled before we can achieve the benefits from Industry 4.0
The technological divide: To make IIoT fully functional, a well-developed framework of Operational Technology is needed. Manufacturing processes for quite a long time have been controlled by humans and thus to converge Information technology with these manufacturing processes will pose a challenge in future.
Data Analysis: Although we have tread a long path in the field of data processing but still it could be extremely difficult and time-consuming to transfer and process the huge amount of data which will be collected by the sensors. Thus, there could be a threat of security to sensitive data being transmitted and stored on the internet. The machines and sensors which are sharing the information directly with the cloud systems become susceptible to security threats too, which can result in the leak of the sensitive data being stored or transferred. Moreover, it is not impossible to hack such assets which are controlled remotely.
Protocol Problem: The manufacturing industries have tons of different machines which run on different command and protocol system and it is extremely difficult to make these systems interoperable. Sharing of data and remotely accessing such machinery through a different protocol system altogether is not an easy job and is the biggest challenge to IIoT.
The solutions to all such problems have been identified, but still, the implementation of such solutions at such a large scale could be pretty complex.
The Gateway Clustering: Gateway clustering makes sure that different machines can continue to share their information with the cloud networks without any interruption or security threats. Most of the hardware of machinery have been upgraded to SCADA systems which can be integrated with CRM or ERP applications for making it possible for IT software to interact with OT systems.
Distributed Edge Computing: One of the biggest problems smart machines face while transmitting the data to cloud servers is Latency. Much of the redundant data or useless information is given by the sensors or machines to the cloud but this can be tackled by forming a cluster of gateways which could filter the information just at the node of the information. This technology of Distributed Edge computing form different functions to filter the unrequited data and send only meaningful data to the cloud.
Gateways: Machine to machine communication can be established with changing the existing protocol of a particular system by establishing IIoT gateways to connect DCS or other systems to cloud gateways. Multiple protocols like MODBUS, PROFIBUS, OPC etc. can be used to maintain secure connectivity.
Security: Security protocols still remain a big problem as hackers usually find a way to breach the protocol and can reach the sensitive information being shared. Trusted Network Module (TNM) and Trusted Perception Module (TPM) are being used to overcome such issues. Many Web Firewalls ensure that the data remains encrypted during transferring and storing.