There contains been a considerable amount in hype around Artificial Intelligence (AI) and Machine Learning (ML) systems in the last five or even so years. So much so that AI has become somewhat of a buzzword – full of ideas and promise, but something that is pretty tricky to execute in practice.
At present, this means that the challenge we run into with AI and ML is a healthy dose of skepticism. For example, we’ve seen several large companies adopt these capabilities, often announcing they intend to revolutionize operations and output with such technologies but then a deep failing to deliver. In turn, the ongoing evolution and adoption of these technologies is consequently knocked back. Although potential applications for AI and ML it can be daunting to identify opportunities for technology adoption that can demonstrate real and quantifiable return on investment.
Many industries have effectively reached a sticking point in their adoption of AI and ML technologies. On average, this has been driven by unproven start-up companies delivering some sort of open source technology and placing a flashy exterior around it, and then relying on a customer to act as a development partner for it.
However, this is the primary problem – customers aren’t going to be looking for prototype and unproven software to run their industrial operations. Instead offering a revolutionary digital experience, many organisations are continuing to fuel their initial skepticism of AI and ML by providing poorly planned pilot projects that often land the company in the stalled position of pilot purgatory, continuous feature creep and a regular rollout of new beta versions of software. This practice of the never ending pilot project is driving a reluctance for customers to then engage further with innovative organizations who are truly driving digital transformation in their sector with proven AI and ML technology.
Innovation with direction
A way to overcome these challenges is almost always to demonstrate proof points to the customer. It implies showing how AI and ML technologies are real and are same as we’d imagine them to be. Naturally, some companies have better adopted AI and ML than the others, but since much of this technology is very new, many are still struggling figure out when and where to apply it.
For example, many are keen to use AI to track customer interests and needs. Actually even greater value can be discovered when applying AI in the form of predictive asset analytics on pieces of industrial process control and manufacturing equipment. AI and ML can provide detailed, real-time insights on machinery operations, exposing new insights that humans cannot necessarily spot. Insights that can drive huge impact on businesses bottom line.
AI and ML is now incredibly popular in manufacturing industries, with advanced operations analysis often being driven by AI. Many are taking these technologies and applying it with their operating experiences to see where economic savings can be made. All organizations want to save your self money where they can and with AI making this possible. These same organizations are usually keen to invest in further digital technologies. Successfully implementing an AI or ML technology can dramatically reduce OPEX and further fuel the digital transformation of an overall enterprise.
Understandably, we could seeing the value of AI and ML best demonstrated in the manufacturing sector in both process and batch automation. For example, using AI to figure out tips to optimize the process to achieve higher production yields and improve production quality. For example, in the food and beverage sectors, AI is being used to monitor production line oven temperatures, flagging anomalies – including moisture, stack height and color – in a continually optimized process to reach the coveted golden batch.
Lack of this is to use predictive maintenance to the behavior of equipment and improve operational safety and asset reliability. A mixture of both AI and ML is fused together to create predictive and prescriptive maintenance. Where AI can be used to spot anomalies in the behavior of assets and recommended solution is prescribed to remediate potential equipment failure. Predictive and Prescriptive maintenance ease reducing pressure on O& M costs, improving safety, and reducing unplanned shutdowns.
Both AI, machine learning and predictive maintenance technologies are enabling new connections to be made within the production line, offering new insights and suggestions for future operations.
Now is the time for organizations to understand that this adoption and innovation is offering new clarity on the relationship between different elements of the production cycle – paving the way for new methods to create better products at both faster speeds and lower costs.
Source: Manufacturing World wide
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