Using Technology to Improve Manufacturing: 4 Ways Big Data and AI Affect Manufacturing Processes
The manufacturing world continues to rebound after shutdowns and allied disruptions of the COVID-19 pandemic. Competition remains intense in most industries, so businesses must make every effort to be as efficient and as productive as possible.
Emerging technologies are playing an increasingly important role in efficiency-related strategies. Artificial intelligence (AI) may be well-known, but a precise definition is still helpful: AI is the simulation of human intelligence processes by machines, in particular IT systems. AI encompasses systems such as machine learning (ML), natural language processing (NLP), and computer vision (CV).
AI capabilities have led to an explosion of Big Data, which Oracle refers to as: "data that contains greater variety, which arrives in increasing volumes and with more velocity, which arrives in increasing volumes and with more velocity." The result is far more data in more complex data sets. AI-enhanced algorithms can make sense of all the data, providing invaluable insights across multiple business functions.
With the above in mind, this article will explore four ways in which Big Data and AI can improve manufacturing processes.
Improved Production Efficiency
Big Data and AI are needed more than ever to improve the efficiency of manufacturing. A Deloitte survey found that 45% of manufacturing executives expect that increases in operational efficiency will be derived from investments in the industrial Internet of Things (IIoT), whereby digitally interconnected machines communicate with each other on the plant floor. 50% of the respondents were convinced that investments in robots and cobots would improve their efficiency in 2022.
Further efficiencies soon will also be gained with 5G, the next generation of cellular communications. The ultra-reliable, low-latency connections (goodbye, buffering!) offered by 5G will be a boon for manufacturers. 5G will enable the proliferation of IIoT on production floors and the widespread use of small, cost-effective sensors across machines and processes. According to the Manufacturer's Alliance, 5G has "the potential to become the core communication platform for many manufacturing companies".
Few things negatively impact production costs and revenue targets in a manufacturing facility as much as unintended downtime does. According to Deloitte, unplanned downtime costs industrial manufacturers as much as $50 billion a year in the US alone.
Furthermore, poor plant maintenance can reduce productivity by as much as 20%.
The beauty of IIoT is that it provides always-on, always-monitoring capabilities that enhance maintenance. The maintenance reach of IIoT is immense.
However, IIoT can be immensely data-heavy, which is why it makes sense to pair it with a computerized maintenance management system (CMMS). This software provides a facility with a centralized, AI-enhanced platform that can store and effectively manage all the incoming data regarding physical assets.
Examples abound of what can be achieved. In Germany, the country's national railway company, Deutsche Bahn, has partnered with Siemens to devise AI and Big Data solutions that help improve the railway company's preventative maintenance regime. One such example is intelligent braking systems that can be monitored for optimal replacement time, while sensors monitor the state of the track to predict needed repairs.
It gets even more exciting: soon, machines will have self-maintenance abilities. AI, coupled with technology such as 3D printing, will take maintenance even beyond the already-impressive capacity of IIoT applications.
Improved Risk Management
AI and Big Data can dramatically improve risk management, in everything from occupational health and safety to security-related risks and environmental impacts. These enterprise risks can sometimes be disastrous and difficult to predict. The cognitive capabilities of AI can therefore be invaluable in reducing risk. For example, ML algorithms can assess past risky behaviors of employees in hazardous locations and build predictive models to reduce the risk.
Although not a manufacturing facility, one of Canada's largest medical research facilities provides an excellent case study of the power of AI: the facility was experiencing failures with its air-handling units. A medical research facility simply cannot have 'downtime' due to malfunctioning ventilation systems. An AI solution was selected that provided live data on the condition of fans within air extraction units. Among multiple benefits was the fact that the solution provided 100% uptime of a critical ventilation system that ensured acceptable laboratory air quality at all times.
Improved Tackling of the 'Big Issues'
Manufacturers cannot only be concerned with production costs and efficiency rates. Today, sustainability is imperative, both strategically and operationally. AI and Big Data can do much to help a manufacturer tackle its sustainability goals and initiatives. The United Nations itself advocates the use of Big Data in reaching its Sustainable Development Goals (SDGs). The UN notes how AI-enabled smart metering can help attain affordable and clean energy (SDG 7) by allowing utility companies to manage electricity or gas consumption levels more intelligently, at both peak and non-peak levels.
Climate change mitigation and carbon management are also more easily attained with the assistance of AI, particularly regarding the all-important energy efficiency targets. The Indiana Economic Development Corporation has collaborated with Amazon Web Services (AWS) to develop Energy INsights, which is being rolled out at over 100 manufacturers in the Hoosier state. The Indiana program integrates the I4.0 Accelerator from AWS, which gathers data from legacy factory equipment and energy systems. It then optimizes energy efficiency by using AI and data analytics, with projected energy reductions of between 8 and 20%.
Production efficiency is paramount for any manufacturing business. It ensures that production costs are minimized relative to revenue. However, operational costs have been impacted by adverse factors beyond the control of manufacturers, such as labor shortages and supply chain instabilities. The war in Eastern Europe has only exacerbated costs. These inflationary factors are expected to continue well into 2023.
As seen, AI and Big Data improve production and will be key in making manufacturing increasingly sustainable as well.
Manufacturers will do well to appreciate the positive ROI of investing in these fast-evolving technologies.
Bryan Christiansen, founder, and CEO of Limble CMMS.
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