Manufacturers can build on nearly any PMM, MES or CMMS platform with machine learning and computer vision capabilities


It’s an eight-letter word certain to make manufacturers cringe. If your systems are idle, you’re likely losing money.

How much money? The International Society of Automation estimates that globally, manufacturers lose $647 billion each year to machine downtime. Gartner sets the cost as high as $540,000 for every hour that assets, workers, infrastructure, systems or networks are unavailable.

That cost—coupled with the sheer unpredictability of idle systems—are two reasons the global manufacturing sector is eager to embrace machine learning. Harnessing your manufacturing data in new ways is a game-changer for productivity, maintenance and of course, your bottom line.

Machine learning moves mainstream

The opportunity for manufacturers is to move from reactive to proactive. In this case, from unexpected repairs and unscheduled downtime to predictive, planned maintenance that minimizes disruptions, reduces costs and streamlines operations. Machine learning provides the tools to make it happen.

While it sounds complex and expensive, companies have deployed machine learning for years; today, it’s the widespread availability of cloud computing power that’s bringing the technology into the mainstream.

We interact with machine learning every day:

    • Navigation suggestions that avoid traffic jams
    • Fraud detection that knows if “you” are really making those online purchases
    • Vehicles with lane departure warnings and accident avoidance alerts
    • Customized playlists based on your personal musical tastes

The term applies to any computer system that leverages algorithms and statistical models to perform specific tasks—without requiring explicit programming or instructions.

    • First, data scientists teach the system to recognize patterns and make inferences, or conclusions and predictions based on data.
    • Next, a supervised training period occurs where staff actively monitor and adjust the system’s performance.
    • Finally, the system moves to unsupervised training and truly lives up to its name: a machine that learns on its own. The more data it consumes, the stronger the insights it produces.

Employing machine learning to solve manufacturing challenges is a logical extension of its capabilities.

Preventive techniques lead to over- or under-maintenance

Today, most manufacturers rely on preventive maintenance management (PMM) to keep equipment operating smoothly. Preventive maintenance employs time, usage and event data, such as hours in operation or replacement dates of a component. Tracking those milestones enables manufacturers to perform maintenance activities on a set schedule.

Dozens of platforms exist, including manufacturing engineering systems (MES) and cloud-based maintenance management systems (CMMS), to help manufacturers monitor their equipment. However, analyzing PMM data and making decisions still falls to humans. Operators must log information. Analysts must crunch numbers. Managers must make decisions—often weighing the value of PMM data against their gut instincts.

Preventive maintenance also excludes numerous important inputs. For example, issue logs from the company ticketing system, images of wear-and-tear on internal components or vibration patterns from machines in operation. All are key variables that affect performance and maintenance timing—but difficult to track and integrate in a typical PMM platform.

As a result, preventive maintenance often leads to over- or under-maintenance. Both situations can be costly. Unnecessary maintenance adds up with the cost of replacement parts and idle systems. Infrequent maintenance may extend the usable life of a component, but raises the risk of unscheduled outages. Neither is ideal.

Predictive maintenance increases uptime, reduces costs

Predictive maintenance using machine learning and computer vision is the next step forward for manufacturers. It builds on the foundation established with preventive techniques, with several key differences.

Predictive maintenance:

    • Leverages more data sources
    • Requires less human intervention or analysis
    • Allows customization to your specific manufacturing environment
    • Yields far greater precision in maintenance recommendations

For example, predictive maintenance systems can analyze qualitative data, such as ticketing system entries, alongside quantitative metrics from sensors and operation logs. Examining multiple data points provides more accurate maintenance insights.

These smart-systems can also detect correlations faster than their human (or PMM) counterparts. Machine learning intelligence may spot previously undetected patterns that lead to equipment issues or failures, such as vibration levels in equipment.

Computer vision is a particularly useful element. This tool combines one or more cameras mounted on critical equipment, with a machine learning system that’s been trained to recognize minuscule differences in images. The applications are numerous. For example, manufacturers can inspect product quality and detect defects in areas where it’s not feasible for human viewing. Computer vision provides high-speed operation in multiple locations on a manufacturing line.

Computer vision can be especially valuable for medical device manufacturers or those in regulated industries, where any equipment modification triggers a costly and time-consuming recertification process. Innovative medtech companies can now identify visual changes on a manufacturing line without requiring recertification. This approach accelerates the ROI for predictive maintenance systems.

Reduce maintenance costs by 25 percent

Predictive maintenance shows great promise across a wide spectrum of manufacturing sectors. McKinsey estimates that manufacturers with predictive capabilities can increase asset availability by 5 to 15 percent, and reduce maintenance costs by 18 to 25 percent.

Predictive maintenance can improve quality, reduce defects and scrap, and detect suboptimal performance in manufacturing equipment.

The technology can also complement the work of human quality inspectors. For example, every hour, a quality inspector may review hundreds of finished products for subtle differences in finish quality, color, hue, or other qualitative attributes. The inspector then flags a few dozen each shift for further review.

Computer vision with machine learning could then examine the flagged products at a deeper level—and with greater precision—to identify the exact source of the discrepancies. In this scenario, quality inspectors gain more time to focus on higher-value concerns.

Advantages of predictive maintenance:

    • Increase uptime and availability of key equipment
    • Decrease maintenance and repair costs
    • Reduce defects and scrap
    • Improve overall quality of production

Predictive maintenance tools have manufacturing applications beyond day-to-day operating performance. Imagine having a near real-time knowledge of your inventory levels in the field—without needing an expensive RFID system or changes to your packing model. You could configure a computer vision and machine learning system to measure inventory use, expirations and redeployment triggers across the supply chain. The resulting insights would increase efficiency and provide a competitive advantage.

It’s another reason the global manufacturing community (and we at Agosto) are so excited about machine learning’s potential.

Early adopters will gain a competitive edge

Despite all its potential, some manufacturers are hesitant. Common concerns include complexity, data quality and system integration.

The good news? Getting started with predictive maintenance is easier—and less complicated—than you may think.

We recommend these four steps:

  1. Take an iterative approach.You can “go big” with machine learning and an enterprise-wide business transformation that takes years—or you can start small, prove the technology’s effectiveness and gradually grow machine learning’s footprint across the organization.At Agosto, we’re big fans of an agile approach where we work quickly and collaboratively, learning as we go. In this fashion, manufacturers can accelerate value and see results in weeks or months, rather than years.
  2. Start with your existing data.Data is another perceived obstacle that doesn’t need to be a hurdle. No one’s data (repeat, no one’s) is perfect! A good technology partner will assess your data readiness and help build out from your current foundation.If you’re like most manufacturers, you already have a rich store of data for machine learning to use, including MES, PMM or CMMS data, your ticketing system and other databases across the company. Machine learning can even access unstructured data that’s “trapped” in PDFs, emails or other documents.

    Another key point, you don’t need to move your data offsite to deploy predictive maintenance. Today’s flexible, cloud-based systems can typically access your data from anywhere, which helps you get up-and-running quickly.

  3. Deploy a structured proof of concept (POC).A POC uses just enough data and functionality to show stakeholders (and skeptics) the value of a machine learning application. It’s a great way to see the tool in action, in your own environment, without a large investment of time, money or resources. From the POC, you can identify requirements and build the business case for a full-blown predictive maintenance project.
  4. Enlist the experts.There’s an art—and a science—to structuring your data for optimal machine learning. Outside resources with experienced data scientists and proven manufacturing expertise are a wise investment. They’ll pay for themselves in strategy, best practices and hands-on implementation that’s efficient and intelligent.

Machine learning shows great promise for manufacturers—especially those who start now. The earlier you harness your data, the more time your machine learning application has for supervised and unsupervised training. Early adopters will gain a definite competitive edge.

Learn more about Predictive Maintenence