Machine Learning in Manufacturing

It wasn’t until this past year that I was introduced to the concept of machine learning. As with most advanced technologies, it took a home experience to help me best understand the concepts and benefits. A couple of months had passed before I fully connected a Nest thermostat to our Wi-Fi network and the internet. Before this connectivity, the well-designed thermostat had managed our home temperature much like our previous thermostat. Once connected, it better understood our preferences and patterns for managing home comfort and reducing energy requirements. Benefiting from new capabilities allows the thermostat to automatically lower shades based on direct sunlight or dim smart bulbs to take advantage of natural light.

Defining Machine Learning

Machine learning is defined as the leading edge of artificial intelligence (AI). It’s a subset of AI where machines can learn using algorithms to interpret data from the world to predict outcomes and learn from successes and failures. For manufacturers, the power of machine learning is exciting with the understanding that any business process, production operation and strategic decision can be made better and more accurate. These decisions can include predictions about what a customer is likely to buy next, the best response to an unexpected supply chain disruption or when an expensive shop floor asset is likely to break down.

Manufacturing Machine Learning Use Cases

According to a number of sources, machine learning is having an impact on manufacturers today. A few use case examples include:

  • Increasing production capacity up to 20 percent while lowering material consumption rates by four percent. Smart manufacturing systems designed to capitalize on predictive data analytics and machine learning have the potential to improve yield rates at the machine, production cell and plant levels. Source: General Electric and cited in National Institute of Standards (NIST).
  • Providing more relevant data so finance, operations and supply chain teams can better manage factory and demand-side constraints.
  • Improving preventative maintenance and Maintenance, Repair and Overhaul (MRO) performance with greater predictive accuracy to the component and part-level.
  • Machine learning is impacting product and service quality by determining which internal processes, workflows, and factors contribute most and least to quality requirements.
  • Using machine learning, buyers and suppliers can collaborate more effectively to reduce stock-outs and improve forecast accuracy to improve on-time delivery performance.
  • Accurately monitor supplier performance and predict potential supply disruptions to avoid inventory shortages.
  • Optimize complex manufacturing processes by better determining where to dedicate resources to reduce bottlenecks and improve cycle time.

Is Now the Time for Machine Learning?

While machine learning is already playing a role in our daily lives with technology like the Nest thermostat, it also has the power to help manufacturers unleash significant business value. Machine learning, along with other advanced technologies like artificial intelligence and the Internet of Things (IoT), is reaching a maturation point that can deliver new innovation and positive business outcomes. So where does your company stand in considering new technologies like machine learning? Now is the time to consider fresh thinking for managing global supply chain complexity, improving production efficiency, increasing asset utilization and becoming an Effective Enterprise.