Through cloud-and-edge data analytics synergies, machine learning can boost supply chain optimization, predictive maintenance, machine vision and operational efficiency.

Every day, companies are generating huge troves of data at the edge, storing this information in the Cloud, and using those assets to rethink virtually all of their processes.

To derive more insights from this rich data and ultimately drive faster and more informed decisions, companies in manufacturing, energy, mining, transportation, and agriculture are leveraging new types of machine technology to improve industrial workloads like engineering and design, production and asset optimization, supply chain management, forecasting, quality management, smart products and machines, and more.

From operational efficiency to quality control and beyond, here are four key ways that companies are using machine learning to rethink industrial processes:

  • Forecasting for supply chain optimization
    Today’s modern supply chains are complex global networks of manufacturers, suppliers, logistics, and retailers that require sophisticated methods of sensing and adapting to customer demand, fluctuations in raw material availability, and external factors such as holidays, events, and even weather.

    The repercussions of not prognosticating these variables correctly can be costly, resulting in either over or under-provisioning and leading to wasted investment or poor customer experiences. To help foresee the future, companies are using machine learning to analyze time-series data and provide accurate forecasts that help them to reduce operating expenses and inefficiencies, ensure higher resource and product availability, deliver products faster, and lower costs.

    When electronics manufacturer Foxconn faced unprecedented volatility in customer demand, supplies, and capacity as a result of the COVID-19 pandemic, it developed a demand forecasting model for its factory in Mexico to generate accurate net orders. Using the machine learning model, they were able to increase forecasting accuracy by 8%, a projected savings of US$553,000 annually per facility, while minimizing wasted labor and maximizing customer satisfaction.
  • Predictive maintenance of equipment
    Historically, most equipment maintenance has been either reactive (after a machine breaks) or preventive (performed at regular intervals to help avoid machines breaking), but both are costly and inefficient. The best solution is predictive maintenance that enables companies to foresee when equipment will need upkeep. However, most companies lack the necessary staff and expertise to build their own solution.

    For companies like power solutions supplier GE Power, predictive maintenance is finally within grasp. There are now end-to-end systems that use sensors and machine learning to detect and alert companies of abnormal fluctuations in machinery vibration or temperature, with no machine learning or cloud experience required.

    This type of technology helped GE Power quickly retrofit assets with sensors and connect them to real-time analytics in the Cloud, moving from time-based to predictive and prescriptive maintenance practices. And as they scale, GE Power can now use these systems to remotely update and maintain their fleet of sensors, without ever having to physically touch them.
  • Anomaly detection via computer vision
    Just as important as ensuring that equipment is functioning properly is guaranteeing the quality of the products that the equipment produces.

    The visual inspection of industrial processes typically requires human eyes, which can be tedious and inconsistent. To improve quality control, industrial companies are looking to computer vision to provide greater speed and accuracy in identifying defects consistently.

    Once again, complex barriers had previously prevented companies from building, deploying, and managing their own machine learning-powered visual anomaly systems. Now, companies can use high accuracy, low-cost anomaly detection solutions that are able to process thousands of images an hour to spot defects and anomalies for appropriate action to be taken.

    For example, a household food manufacturer in Sweden, Dafgards, uses computer vision in the production of their prepacked pizza line. When their previous machine vision system failed to detect certain defects, they implemented a new machine learning service that leverages computer vision. Now, they are able to easily and cost-effectively scale their inspection capability. The venture has been so successful that the firm expanded the use of computer vision to multiple pizza varieties as well as other product lines such as hamburgers and quiches.
  • Improving operational efficiency
    Many industrial and manufacturing companies are also looking to apply computer vision to optimize efficiency and improve operations. Today, companies manually review video feeds across their industrial sites to authenticate access to facilities, inspect shipments, and detect spills or other hazardous conditions. But doing this in real time is difficult, error prone and expensive. And while companies may upgrade existing internet protocol (IP) cameras for smart cameras that have enough processing power to run computer vision models, this can be expensive; even with smart cameras, getting low latency performance with good accuracy can be challenging. Instead, industrial companies can use hardware appliances that allow them to add computer vision to existing on-premises cameras, or even use software development kits to build new cameras that can run meaningful computer vision models at the edge.

    Global energy company BP is looking to deploy computer vision at its 18,000 service stations worldwide. The firm is working to leverage computer vision to automate the entry and exit of fuel trucks to their facilities, and to verify that the correct order has been fulfilled. Computer vision can also help alert workers if there is a collision risk; identify a foreign object in a dynamic exclusion zone; and detect any oil leaks.

Fueling the next industrial revolution

Industrial environments, manufactured products as well as logistics and supply chain operations can exploit the potential of machine learning to make processes easier, faster, and more accurate.

This is through the combination of real-time data analysis in the Cloud and machine learning at the edge. With increasing digitalization, industrial companies will be able to turn their aspirations into realities and spur the next industrial revolution.