Artificial intelligence in the manufacturing sector

We are privileged to be witnessing the rise of newer applications using machine learning. They are producing relatively modest reductions in equipment failures, better on-time deliveries, slight improvements in equipment, and faster training times in the competitive world of industrial robotics. Some of these advancements include greater industrial connectivity, more widely deployed sensors, more powerful analytics, and improved robots.
The upgrades may appear to be little, yet when included and spread over such an extensive division, the aggregate potential recoveries are huge. According to the UN, worldwide value added by manufacturing (the net outputs of manufacturing after subtracting the intermediate inputs) was $11.6 trillion in 2015. This is why companies are spending billions on developing AI tools to squeeze a few extra percentage points out of different factories.

Combining real-time monitoring and machine learning is optimizing shop floor operations, providing insights into machine-level loads and production schedule performance.

Knowing in real-time how each machine’s load level impacts overall production schedule performance leads to better decisions managing each production run. Optimizing the best possible set of machines for a given production run is now possible using machine learning algorithms.

It’s easy to get caught in the “robot mentality” when looking at AI within manufacturing, but AI will influence manufacturing in numerous areas outside of robotics.

  • • In the supply chain for example, algorithms can perceive patterns of demand for products across time, geographic markets, and socioeconomic segments while accounting for macroeconomic cycles, political developments, and even weather patterns.

  • • The output can be a projection of market demand, which in turn could drive raw material sourcing, human staffing, financing decisions, inventory, maintenance of equipment, and energy consumption.

  • • Enhancing the supply chain is one aspect, but AI can be used to predict maintenance cycles of machinery and even preemptively determine demand for products lines from social media posts.

Albeit numerous laborers will be replaced by robots for the time being, the end game will be to retrain specialists to perform larger amount configuration, programming, or upkeep undertakings. The genuine driver will be to create applications for AI in assembling that don't simply computerize errands, yet make completely new business forms plausible.