In AI, Digital Manufacturing, Manufacturing Analytics

Few industries today are experiencing more dramatic changes than automotive manufacturing. Thanks to a volatile economy, consumers are buying fewer new vehicles, keeping them longer, or purchasing used ones. Other trends are making it a tough time for the industry: the current push toward sustainable vehicles; new tariffs driving up costs; shorter product lifecycles; and the rise of Uber, Lyft, and Zipcar.

As a result, the ability to adapt to market changes is more important than ever before. Auto manufacturers have historically had a strong culture of Continuous Improvement or CI. To continue to enhance production processes into the future, automakers need to be able to leverage insights derived from artificial intelligence.

AI and machine learning now offer access to factory and supply chain data in amounts that are simply too large for humans to evaluate. Technologies such as Sight Machine consistently monitor production equipment, manage scrap, and predict and manage downtime. This lets manufacturers meet jobs per hour (JPH) targets, defined by the required takt time with the best quality, lowest cost, and shortest possible delivery.

Improving on Continuous Improvement

You’re probably thinking, “But we have perfectly good systems that we’ve used for years, providing the necessary data.” And this may be true – up to a point. In fact, the main problem we see is that while an enormous amount of data is available from PLCs, sensors, robotic systems, databases, and other sources, it tends to remain siloed within those systems. Individual functions – from machine operations and process engineering to quality assurance – are limited to the data that they themselves collect.

To improve production, we must instead look at the data from a holistic perspective, combining SKUs, product data, the impact of one part on another, difference in quality on the same line, the performance of different machines, and many other types of information. In such an environment, manufacturers achieve new levels of profitability as:

  • AI enables more accurate, predictive maintenance on equipment to reduce unplanned downtime. Managers can get ahead of a possible fault, and use historical data to understand factors leading to an event.
  • AI-driven data helps to minimize cycle variation. Variation is the enemy of consistent production, and understanding every aspect of dozens of automated and manual steps is a key element of JPH.
  • AI is used to track and trace each vehicle throughout production. In this highly regulated industry, issues are identified in case of a possible claim, limiting its scope and impact on the brand.

And the outcomes can be remarkable. Auto manufacturers saved almost $2.5 million in reduced downtime and in labor costs, and have seen a 25% improvement in rejects. None of this would be possible without AI.

How Can You Achieve CI with AI?

Every auto manufacturer understands the importance of gathering critical data, but it’s fair to say that each is in a different phase of the journey. But you don’t have to boil the ocean. Begin by creating the infrastructure: By starting with real-time (not historical) data, you can focus on analyzing information that can drive immediate improvements across the production process, facilities, partners, and suppliers. This, in turn, helps you to achieve quick wins, build credibility, and use it to influence other teams. Remember, this is a program, not a single project, and the proof is what drives cultural change.

Please contact us for a live demonstration of how Sight Machine’s AI-driven platform and CI services can achieve these goals.

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