As the worldwide demand for consumer goods increases by the day, the need for a new type of industry - one which is laced with the latest technologies aimed at optimizing the manufacturing process, while keeping in line with the latest sustainability standards has become vital. This has led to the fourth iteration of the industry, or “Industry 4.0” - one which involves digitization of the current manufacturing systems, using sensors, communication networks, and big data analytics along with intelligent robots and machines that promise increased productivity, customization, decreased wastage of raw materials, and higher quality of products. This fundamental change in the way industries operate has also altered the scenario around current testing and safety standards. As machinery becomes more and more sophisticated, with new technologies replacing the older ones, safety protocols and standards must adapt in tandem.
Understanding the safety concerns in industries
Increased sophistication in today’s scenario almost always equates to the integration of artificial intelligence (AI) and machine learning (ML) with cloud computing, and the Internet of Things (IoT). Artificial Intelligence is displayed by any agent that can optimize the chances of reaching its goals by perceiving its environment. Machine learning is the study of computer algorithms that can improve automatically through experience and data. These systems are being rapidly integrated to help achieve the main goals of the new industrial revolution - improved manufacturing through upgraded systems.
One such example of upgraded machinery is mobile robots. They offer higher flexibility as compared to the standard robots used in the industries today. Using AI and ML, they are designed in a way that enables them to calculate in real-time the most efficient route to finish their tasks. This route will most certainly cross paths with the human elements that share the factory floor with these robots, and hence, a scenario for a potential mishap, which would entail both social as well as economic costs for the company and its workers. This necessitates the drawing of strict safety standards in the context of such robots with embedded AI and ML.
(Source: Green World Group)
Another application of AI and ML wherein the risk of loss is high, are autonomous vehicles. An autonomous vehicle has to process multiple data points (e.g., speed, relative position with respect to other cars, obstructions on the road, etc.) through its algorithms. This calls for robust testing standards to ensure that the said algorithm has errors well within the permissible limits, for any error can act as a hazard possibly for hundreds of vehicles that traverse the roads. Moreover, embedded AI systems adapt their functionality over time, which may lead to the emergence of processes and products radically different from the ones that were implemented initially, consequently requiring revisions in risk assessment over time.
All of these factors in combination have resulted in an environment that is constantly evolving, and this evolution is now putting pressure on the institutions responsible for drawing safety standards, as with every change in the functioning of processes and entities, the steps that ensure the safety of every entity involved changes. Other than the obvious risk of physical damage due to malfunctioning, the interconnected nature of Industry 4.0 brings in new threats that were once paid minimal attention to when considered within the context of an industry - intrusion of privacy, cyber threats, connectivity issues, etc. This means that safety standards will now have to include all these new potential areas of safety issues.
How Industry 4.0 is answering the calls for increased safety
One example of safety systems in the new industry is implemented using edge computing and embedded AI. Edge computing - or computing where the data collection itself happens, has opened new doors in the context of safety systems. Processing data without sending it to the cloud enables real-time decision-making when used with AI and ML systems embedded into these systems. These technologies ensure fast action, and the potential for saving lives is huge. Such embedded safety AI programs themselves will need robust standards in place that will ensure that such critical components have the lowest possible chances of failing.
In terms of technical aspects, risk assessment, testing, and drawing standards that are in line with the relevant definitions of safety are the most crucial steps towards ensuring the feasibility of the new industrial revolution with respect to safety measures. Constantly ensuring that the standards stay up to date with the rapid advancements in technology will become the greatest challenge to the committees that are tasked with ensuring the safety of the entities that employ these new technologies.
A boon in disguise?
Apart from changing the way the machines themselves work, industry 4.0 changes the way one critical resource is managed, too i.e. human resources. Smart machinery and equipment that can, in grossly exaggerated terms, mimic human decision-making have already started to displace human workers in many fields, simultaneously opening up requirements for skilled labor that is equipped to design, navigate, troubleshoot, and maintain the ever-changing landscape of manufacturing machinery.
With the physically laborious tasks along with on-the-floor decision-making and monitoring being delegated to intelligent autonomous machines, the nature of tasks performed by the human element of the industry is rapidly changing. As touched upon previously, more and more roles that involve intellectual tasks such as design and troubleshooting are opening up, forcing the workforce to update their skillset. This shift in the nature of work performed by humans changes two things: one - jobs become less monotonous and more creatively challenging and fulfilling, and two - occupational health and safety standards. The aspect of changing health and safety standards implies that psychological stress, overall wellbeing, etc. would start to gain more gravity as primary health parameters, in the end, leading to an individual whose psychological needs are weighed in at a level same as that of his/her physical health.