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Brent Dawkins

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 personal experience to help me best understand the concepts and benefits.

Case in point — 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 managed our home temperature much like our previous thermostat. Once connected, however, 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. The result has been lower energy use with reduced heating and cooling costs.

Machine learning can be considered 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. You can also consider machine learning as a type of artificial intelligence where computers have the ability to learn from data without specific programming, such as GPS traffic recommendations or automatic credit card fraud alerts.

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. The benefits of machine-driven learning are proving to be significant with reduced downtime of critical equipment, increased production output, enhanced supply chain performance and other operational benefits.

According to a number of sources, machine learning is already having an impact on manufacturers, specifically in the following three areas:

  • Predictive maintenance – Manufacturers already know that maintenance performed at the right time results in lower costs. For example, fix machinery too late and it breaks down prior to maintenance or fix something too early and incur unnecessary costs. With predictive maintenance supported by machine learning, cost savings improve as maintenance is comprehensively based on reported data and then fully analyzed in real-time. Machine learning delivers the added benefit of reviewing months or perhaps years of data to construct more accurate data models and improve predictive maintenance accuracy. As a result, manufacturers can improve efficiency and lower production costs.
  • Supply and demand planning – It is very difficult to reliably forecast supply and demand in today’s complex and global business world. A number of factors must be taken into account to produce accurate supply chain forecasts. These factors include trade promotion activities, new product introductions, seasonality, country regulations, and demand volatility amongst many others.

For manufacturers, machine learning can provide an answer for enhancing forecast accuracy to maximize supply chain performance and better meet customer demands. Through machine learning, manufacturers can analyze more data collected over time and improve forecasting accuracy and supply chain decision making.

  • New product introduction – New product introductions inherently provide no historical data to accurately predict when and where service parts will be required. For manufacturers, any error in parts availability can negatively impact customer satisfaction and increase service costs. Machine learning can further help manufacturers by incorporating sales data, social media chatter, web traffic and other information sources to better track and determine new product launch performance. This helps manufacturers better meet initial sales goals and confirm product availability across geographies. In the case of after-sales manufacturing service organizations, these teams can better understand where replacement parts need to be stocked to avoid excess inventories and ensure a positive customer experience.

While machine learning is already playing a role in our daily lives with consumer technology, it also has the power to help manufacturers unleash significant business value. Machine learning is reaching a maturation point that can deliver new innovations and create significant positive business outcomes.

Brent Dawkins is director of product marketing at QAD.

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