Alternator Assembly Line Becomes More Mature

As alternator assembly line approaches sometimes the complexity of a biologic cell, it’s becoming increasingly tough to track, understand and optimize the flow of what’s going on. Getting isolated pictures of a process at certain points of time is not enough: to compete and understand it as a whole, you need to connect all this information, similar to making a film from individual frames enabling you to have the full story. And that’s where artificial intelligence (AI) can help.

Production lines aren’t linear sequence of operations anymore, and they’ve become progressively arduous to optimize. Difficulties are numerous: variable raw material quality, complex isolated processes, intricate plant and lines layouts with parallel tracks, loops and processes dependent on completed tasks, problematic tracking of the material in real time, inconsistent operative procedures, distinct designs across locations, constrained adaptability - streamlining operations is harder than ever. And that’s not even including information constraints, where knowledge and data are siloed across units of the organization.

Although classic methods and software are becoming outdated for such complex systems, innovative technologies such as artificial intelligence are now production-ready. Able to consider an almost infinite number of variables with the power of cloud computing and machine learning algorithms to explore new lines of thoughts, it learns from past data to get the full picture in real time, creating and adapting models to reality of the production lines.

The AI predicts what’s going to happen following present actions, even going as far determining optimal rules and suggesting the best course of actions to achieve the objective you defined. With these advanced AI techniques, you’re transforming a still, picture-like understanding of production processes with standard models to a film view that ponders future consequences, including time and space constraints on the production lands.

The plant manager and plant operators, assisted by an AI, can then optimize their production in an efficient, dynamic and holistic manner, with a machine that considers the implications of localized actions in the future across the complete process, not limiting itself to optimization of local sub-processes that can sometimes hamper the global production in unintended ways.