AI Transforming the Manufacturing Industry with “Intelligent Design”

Manufacturers across a broad range of sectors are finding that artificial intelligence (AI) systems can benefit their firms in a variety of ways. In the manufacturing section, most of the early AI systems were intended to help design optimal manufacturing processes, and to increase operational efficiencies at large factories, chemical plants, and refineries.

Today’s 2nd and 3rd generation AI systems in manufacturing are being used in an increasingly wide range of applications beyond optimizing plant processes. This includes predicting demand for manufacturing output and even suggesting new products/services based on customer demand and feedback. Modern AI systems are now involved in all aspects of the manufacturing process, providing “intelligent design” in developing products, sourcing raw materials, making products more useful and more reliable, as well as creating a safer workplace.

Leaders of firms in the manufacturing sector are definitely aware of the importance of AI. In a Forbes Insights survey on artificial intelligence taken earlier this year, 44% of respondents from the automotive and manufacturing sectors believed AI was “highly important” to the manufacturing function in the next five years, and another 49% responded that AI was “absolutely critical to success.”

AI Fine-Tuning Predictive Maintenance

AI is becoming increasingly important in predictive maintenance for all types of plant equipment. A variety of sensors are installed to monitor operating conditions and performance at factories, refineries, chemical plants and the like. The data collected by these sensors allows AI systems to predict breakdowns and malfunctions, enabling functional, efficient maintenance as well as preemptive actions in urgent situations.

These sensors are embedded in the equipment at the factory, and also at suppliers’ facilities, making it possible to track inventory for raw materials, parts or other factory inputs, and monitoring product-quality problems with distributors or retail outlets.

AI Helps Create Safer Factories, Chemical Plants, and Refineries

AI also makes the machines on the factory floor smarter and safer. Machine vision is one big reason why. Today we have cameras that are many times more sensitive than the human eye, and AI is increasingly able to make sense of the images and act on what is seen. For example,, a Silicon Valley startup, is developing solutions to manufacturing problems such as exact quality analysis. The AI system uses machine-vision tools to identify microscopic defects in many different products (esp. semiconductors). The system is a machine-learning algorithm trained with just a few dozen sample images.

Also note that robots can even be trained to understand what is going on around on the factory floor so that they can avoid dangerous situations. Similar to the advances in self-driving-vehicles, robot “safety training” has come a long way in the last few years. Now forward-looking plant managers are planning on smart, self-driving forklifts and conveyors to move raw materials and finished goods around to workstations.

Manufacturing sector experts also highlight the increasing demand for highly collaborative robots that can work productively with human beings. AI systems make it possible for robots to be instructed by humans, most importantly, to understand instructions not anticipated in the original programming. Taking the next step to “cobots” on the factory floor means that robots and humans must share a common language for communication, and with AI, this can be human speech.

Continuous Real-Time Modeling of Demand for Factory Output Improves Supply Chain

AI is already having a major impact on the supply chain in the manufacturing sector. For example, AI is much more capable than even the smartest humans in being able to predict patterns of demand for products over time, geographic markets, and across socioeconomic strata, while also taking macroeconomic cycles, political developments, and even weather into account in its modeling.

AI can, for example, model projections of market demand, which in turn enables more efficient raw material sourcing, human staffing, financing decisions, inventory, maintenance of equipment, and energy consumption. With AI, you can drive the “just in time” production model to new levels of operational efficiency.

It seems that the burning question at this point is: What CAN’T AI do?!


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