In manufacturing, decision-makers must take into account specific industry considerations before they can realize the true transformational power of AI in the enterprise. AI is already transforming manufacturing in many ways. Navigation systems in cars, fitness apps, Alexa and Siri, Amazon, Netflix, weather forecasting, and high-speed stock trading are among current must-have AI applications. Now, even manufacturers with heavy assets, including cement companies, are launching pilot projects to determine if and how AI might benefit their operations.
The case for manufacturers with heavy assets to apply AI
For decades, companies have been “digitizing” their plants with distributed and supervisory control systems and, in some cases, advanced process controls. While this has greatly improved visualizations for operators, most companies with heavy assets have not kept up with the latest advances in analytics and in decision-support solutions that apply AI.
Operators still rely on their experience, intuition, and judgment. For example, Today’s downsized teams of control-room operators are expected to manually monitor a multitude of signals on numerous screens and adjust settings as needed. At the same time, they must troubleshoot and run tests and trials, to name just a few of the tasks that strain the limits of their human capacity. As a result, many operators take shortcuts and prioritize urgent activities that don’t necessarily add value.
This heavy reliance on experience makes it difficult to replace a highly skilled operator at retirement. Since variations in operators’ qualifications can affect not only performance but also profits, AI’s ability to preserve, improve, and standardize knowledge is all the more important. Moreover, since it can make complex operational set-point decisions on its own, AI is able to reliably deliver predictable and consistent output in markets that have difficulty attracting and retaining operator talent.
With respect to operational improvement and dynamic adaptability, artificial intelligence can outperform conventional decision-support technologies. Also, thanks to new, high-performance software tools, processing power, and cheap memory, AI enables companies to cost-effectively create and maintain their own algorithms and intellectual property in-house, which is cheaper, more versatile, and more adaptive to constantly changing equipment and market conditions. AI can fully automate complex tasks and provide consistent and precise optimum set points in autopilot mode. It requires less manpower to maintain, and—equally important—it can be adjusted quickly when management revises manufacturing strategy and production plans.
What Sankey has done!
Sankey Solutions is fast transforming itself as a reliable brand when it comes to customized, novel Digital Solutions. Especially in the Sand crushing space Sankey building muscle for a leading Crushing firm, a complete ERP solution which also has massive impact in improving efficiency of Leads Data collection and generation.
Apart from this Sankey is in engagement with a leading Automobile manufacturer in India and is providing immense value by leveraging IOT of the same nature which can be extended to the issues highlighted above.
Other AI Applications include but not limited to Price forecasts
To manufacture products, you first need to purchase the necessary resources, and sometimes the prices can get a little crazy. For example, if you buy stainless steel, its price is affected by a variety of factors, including the listings of Metal Exchange or the prices of other elements, some of them not listed on the metal exchange. With the rapid changes in prices, sometimes it may be hard to assess when it’s the best time to buy resources. Knowing the prices of resources is also necessary for companies to estimate the price of their product when it’s ready to leave the factory. Let’s stick to the example of stainless steel: the prices can vary, depending on the current listings of e.g. nickel or the price of ferrochrome. The system is able to provide accurate price recommendations just like in the case of dynamic pricing that’s used by e-commerce businesses like Amazon where machine learning algorithms analyse historical and competitive data to always offer competitive prices and make even more profit.
When you think about customer service, what industries come to your mind? Hospitality, retail, banking? They deal with customers directly, so customer service is a huge part of their business. In manufacturing, however, the importance of customer service is often overlooked – which is a mistake as lost customers can mean millions of dollars in lost sales. AI solutions can analyse the behaviours of customers to identify patterns and predict future outcomes. Observing actual customers’ behaviours allows companies to better answer their needs. In 2018, Nokia unveiled the latest version of its Cognitive Analytics for Customer Insight software, providing powerful new capabilities so service provider business, IT and engineering organizations can consistently deliver a superior real-time and personalized customer experience. The software allows service providers to quickly identify issues and prioritize improvements. There’s a variety of ways artificial intelligence can improve customer service – read more about this topic here.
Human factors play a huge, , factor in workplace safety, with fatigue and stress readily contributing to accidents. So, one major benefit of AI is its inability to get stressed, tired or unwell. In other words, AI safety can scale down human factors in the workplace.
Cleverly detects if employees are wearing the correct PPE for each working area by blending video footage, innovative algorithms and machine learning. If a worker is not suitably dressed, alert is sent and restricts access.
Systems includes inspection stations and software tools that enable engineers to remotely review images of any unit, while virtually tearing down a device to understand what went wrong, take measurements, communicate with the global team, and make fixes or specification changes to stop delays before they start.
Triage defective units automatically
- Restart downed lines hours or days faster
- Identify root cause in minutes
- Test hypotheses without building more units
- Monitor and set cosmetic specifications remotely
- Keep teams aligned around the globe
Other areas in production and manufacturing which can find AI applications are Inventory Management, staffing, predictive maintenance, educating new workers, using a combination of AI-CAD and cloud technology to find bottlenecks, limitations mistakes, and much more! Stay tunedFind out more