The manufacturing industry is evolving to comply with Industry 4.0, the digital industrial revolution. At this crucial junction, we look at these 6 critical problems that can be resolved using modern technology in manufacturing.
After steam, electricity, and computers, the manufacturing industry is looking at another developing trend that aims to change its dynamics completely - Information. Manufacturing 4.0 seeks to introduce computers and automation in an entirely different way to the industry. It will do so by equipping computer systems with machine learning algorithms to conceive a ‘smart factory’ that generates insights and solutions based on real-time data. The Manufacturing Leadership Council has analyzed that this transition will not be smooth. It has listed out some critical issues that the industry faces in the coming years. This article will take you through these glaring problems, apart from the conventional ones, and also present to you how modern technology in manufacturing can help tackle these issues.
1. Widening skill gap tackled by machine learning
The ever-growing domains of knowledge and technology require the skill set of your workforce to stay on pace with global developments. Alternatively, dynamic algorithms can be built with machine learning applications to perform complicated tasks that would otherwise require a specifically trained workforce. E-commerce giant Amazon has tied up with Kiva Robotics to employ about 30000 fulfillment robots in its gigantic warehouses.
2. Supply chain complexities managed with IoT and blockchain
Various factors come into play while deciding upon supply chain parameters such as safety stock levels, delivery schedules, logistics expenses, etc. Warehouses can use blockchains and Internet-of-Things applications to make supply chains more efficient and reliable. Volvo currently utilizes IoT and cloud services to improve traceability of its supply chain.
3. Disastrous product recalls evaded using AI and simulation
Product Recalls occur due to the inflexible nature of manufacturing processes that discourages improvisations in products. With the aid of simulation software and artificial intelligence applications, we can flex manufacturing processes to correct possible product flaws dynamically. Digital Twins are now predicting product failures during prototyping.
4. Disrupting equipment failure fixed using advanced analytics
Technical failures in machinery can completely disrupt delivery schedules, thereby damaging reputation. These can also inflate the maintenance budgets. However, by analyzing historical data of technical snags using statistical methods employed by big data tools, we can predict future failures and schedule precautionary maintenance without hurting delivery schedules. Tata Consultancy Services was hired by an automotive OEM to increase their overall equipment effectiveness which subsequently rose from 65% to 85% using advanced analytics.
5. Misleading expectations corrected using modern technology in manufacturing
Industries often fail to meet the stakeholder expectations which are usually based on historical data extrapolated to present market conditions. It happens because conventional statistics do not take into account many other factors such as natural calamities, logistic disruptions, and political scenarios. Advanced analytics and big data tools can take into consideration a wide range of factors to provide more reliable and accurate expectations.
6. Mischievous cyber-attacks prevented with AI
Now that the Internet has inevitably penetrated into our industry systems, we should be wary of potential hackers trying to gain access to information or control of these systems. Apart from robust firewalls and layers of security, machine learning and artificial intelligence can provide a reliable solution to cyber-attacks. Darktrace, headed by Nicole Eagan focuses on using artificial intelligence to filter suspicious activity from a network. Such systems can detect events such as an ‘inside job’ that antiviruses cannot. Technologies such as Artificial Intelligence, big data, Internet-of-Things, machine learning and blockchain are leading us to more reliable and accurate solutions to critical issues. In conclusion, while modern technology in manufacturing may have been an optional value-added utility before, it is now providing solutions to the most acute problems in manufacturing.
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