16 November 2020


10 Ways Machine Learning Will Reshape Manufacturing in 2021 & Beyond

When the manufacturing industry has been transformed in the past—whether through assembly lines or economies of scale—the final result has always been greater efficiency and faster production.

The manufacturing transformation we see today is being driven by new and cutting-edge technology, but the results are familiar—higher volumes of output, lower costs, and more reliable operations. According to Accelerate Your Data-Driven Transformation, a commissioned study conducted by Forrester Consulting on behalf of RapidMiner, only 2% of manufacturers indicated that machine learning, artificial intelligence, and advanced analytics was the most important investment area 2-3 years ago. But in the next two to three years, 56% of the same manufacturers anticipate it becoming the most important investment area.

Machine learning is at the heart of this revolution.

Simply put, machine learning (ML) is actionable intelligence derived from data. More technically, it’s a branch of artificial intelligence focused on creating computer programs that can learn from experience, and thus adjust their decision-making ability over time. ML can actually combine different types of algorithms, from linear discriminant analysis to neural networks to clustering.

While robotics has been making an impact for decades now, machine learning is just beginning to live up to its full potential. That’s because ML relies on data produced from other systems. And with the ongoing digitization of manufacturing (especially through the industrial Internet of Things), that data is finally available.

10 Ways Machine Learning Will Change Manufacturing in 2021

To better understand the full potential and how you can harness it for your business, we’ve highlighted ten important ways machine learning will reshape the manufacturing industry in the upcoming year.

1. Processing Unstructured Data

Manufacturers can use machine learning to leverage their core production data (think inventory data and shipping data) in countless ways. In fact, if you can think of a manufacturing dataset that fits in a spreadsheet, you can probably plug it into machine learning software and learn something interesting from this structured data.

The most underappreciated application of machine learning, though, is in analyzing unstructured data. This is the kind of raw data that doesn’t fit cleanly within a spreadsheet but can be tremendously valuable when unlocked.

For example, written maintenance logs can be analyzed for common reasons that equipment fails, or images from the shop floor can be mined to look for irregularities or other problems (see, for example, number 3 below). With ML and tools like natural language processing (NLP), this unwieldy data can be transformed into structured and analyzable formats.

Potential applications include all the examples discussed ahead, as well as sales, marketing, and customer service.

2. Predicting Maintenance Needs

Every hour a machine is down for maintenance or repairs is an hour that your facility isn’t working at peak efficiency (or possibly even working at all).

Until recently, manufacturers have had to choose between conducting maintenance on a regularly scheduled basis—whether it be based on time or cycles—or simply waiting until failure occurs. The former option certainly seems more appealing, but still involves a period of offline assessment that might not have been needed, and possibly longer downtime for any necessary repairs.

Thankfully, data from sensors embedded in your equipment (or from sensors monitoring your equipment, for example via video feed) can be fed into machine learning algorithms to create a predictive maintenance model that will remove guesswork from maintenance decisions. With ML, you can be alerted of pending failures immediately, and work orders (including replacement parts shipments) can even be triggered automatically.

Models can also compare live operating data with historical data to predict when equipment breakdowns are likely to occur. That means your company can be far more proactive and ensure machines are serviced before that happens. This results in fewer errors, less downtime, and lower human-capital costs because managers and other workers need to be less involved in routine maintenance tasks.

What do those benefits add up to? A study by Deloitte found that poor maintenance can decrease production by 5-20%. That’s why putting ML at the core of equipment care is essential to avoid costly inefficiencies.

3. Revamping Quality Control

Quality control is often done by humans because it usually requires a visual inspection. That’s why it’s still common to see workers inspecting products whizzing by on an assembly line, looking for flaws. Human senses (and attention spans) have natural limits though, which is why errors can still happen.

Machine learning can remove any possibility of error while optimizing your quality control efforts. Programs can be trained to analyze images and detect any anomalies.

The real value of this is ensuring products are correctly packaged and labeled and otherwise free of flaws. In fact, a McKinsey study found that image recognition programs may increase defect detection rates by up to 90% when compared to human inspection.

A case study involving steel manufacturing uncovered the impact that machine learning has when defects are identified earlier in the process, leading to less waste. Factories are also able to efficiently identify possible causes of these defects.

4. Improving Workplace Safety

Manufacturers have countless reasons to keep their workplaces as safe as possible at all times. Safe environments keep workers healthy, boost morale, and prevent expensive production stoppages.

What’s the best way to maintain a safe workplace? With data—or more specifically, with the kind of data analysis that machine learning provides.

For instance, ML can help augment video surveillance systems to recognize workers not wearing proper PPE or otherwise engaged in unsafe practices. And when combined with video and sensors, machine learning can reveal important insights about safety-system performance and even identify the underlying causes of shutdowns.

Additionally, ML can be used for predictive maintenance, which helps address and prevent machinery issues before they lead to malfunctions, accidents, and potential work stoppages.

5. Optimizing Logistics

As supply chains have become increasingly global, they’ve also become more complex. A single shift in weather or change in fuel prices can reverberate throughout your operation, greatly impacting your business.

Machine learning can analyze each variable that impacts your supply chain and adjust your entire operation in response to changes. This could mean calculating how much extra time to allocate for a shipment (to account for weather delays) or deciding which country to ship a product from (to account for rising tariff rates).

Put simply, a machine learning algorithm can take dozens—or even hundreds—of factors into consideration before making the best possible choice for your business.

The importance of minimizing these delays comes down to inventory and cash flow. If, for example, you can increase the efficiency of your supply chain by 10%, that means you can produce 10% more product while decreasing the level of unpredictability in the production process.

Efficient and reliable production is essential for a successful manufacturing business, and ML makes both accessible in a way it never has been before.

6. Forecasting Consumer Demand

Even the best manufacturer won’t be successful if they’re not producing a product that consumers want. Thankfully, AI programs can be used to forecast demand. This can be especially useful for fast-moving consumer goods. 

Demand forecasting is nothing new, of course, but machine learning models bring an unprecedented level of sophistication to the task. These models will aggregate your historical and new data from different sources, including enterprise resource planning systems, point-of-sale systems, and social media marketing programs. This data can be combined with other relevant variables, including raw material prices, supplier issues, and weather disruptions, to help you understand how hot your product is right now

With ML, you can identify the relationship between these variables and the specific factors driving demand. Best of all, unlike traditional forecasting solutions, models will be constantly updated based on new data, allowing you to adapt on the fly.

7. Responding to Consumer Demand

Demand sensing is another promising machine learning application, especially for companies producing fast-moving consumer goods, like food or fashion, that need real-time forecasting.

Demand sensing lets you track fluctuations in market demand and consumer purchase behavior by analyzing data from point-of-sale systems, warehouses, and other asset-tracking sources to identify significant increases or decreases in sales.

Given the amounts of time young people spend online, it shouldn’t surprise you that nearly half of fashion shoppers say their most recent online purchase was inspired by social media. So, when a new look or piece of apparel starts trending, demand can quickly outpace supply.

Demand sensing is less valuable, obviously, for long-term forecasting and planning, but the data you collect can always be used as a historical point of comparison.

8. Improving Inventory Management

Inventory management is a key financial component of supply chain management. That’s because the cost of storing inventory is massive, usually hovering around 20-30% of the cost of a product.

Even a modest reduction of 10% in holding costs can reduce your per-unit costs by 2-3%. Holding unsold or undelivered products means paying for storage space. This may not sound like a major problem, but its effect on cash flow can be sizable.

Here, the role of machine learning is to calculate when it makes economic sense to hold on to or sell inventory, or even increase or reduce production. This is done by monitoring the supply chain elements mentioned above, as well as market prices, holding costs, and production capacity.

9. Improving Energy Efficiency

One of the greatest inputs for any factory is electricity. While most factories operate 24 hours a day for optimal efficiency, it’s possible to schedule more energy-intensive activities for different times. The idea is to ensure those activities occur when power is cheapest. Depending on its source, this could be during the day (if solar power is prominent) or during the night (when demand is generally lower).

There are countless other considerations to factor in as well, and this is where machine learning’s ability to process large amounts of data comes into play. By considering energy prices alongside labor costs, equipment maintenance, and minimizing inventory, these algorithms can schedule the perfect time to perform energy-intensive activities for maximum cost savings.

Working backward, this information can also allow you to intelligently invest in electrical infrastructure, whether that’s energy storage or solar power. Essentially, ML algorithms allow you to precisely quantify the value of your factory’s electricity at any particular moment.

You can more precisely determine where such investments make sense, use your resources more strategically, and get more out of your factories.

10. Enabling Generative Design

Generative design, in the broadest sense, is a way to create an “evolutionary” approach to engineering by rapidly analyzing novel solutions to design problems. What this means in practice is that by providing a set number of inputs (like cost, material, and manufacturing techniques), the machine learning software can show you every manufacturing-ready output.

For example, in a scenario when a prototype product weighed too much, generative design could identify surface areas that could be removed without sacrificing structural integrity. The resulting design options may have an “organic” look due to their complex and topologically optimized shapes.

Generative design can thus reduce costs associated with “trial-and-error” testing by delivering only suitable products, using the minimal amount of material necessary—meaning less scrap and less energy consumption.

Machine Learning Makes Every Stage of Manufacturing More Efficient

Simply put, machine learning can offer substantial cost savings in every phase of the manufacturing cycle – from buying raw materials to maintaining equipment. And you don’t need a team of data scientists working around the clock to implement it.

The technology is more user-friendly than ever, which is why so many manufacturers around the globe are leveraging it for big gains.

So, what’s stopping you from leveraging machine learning to improve your manufacturing operations? Year after year, more case studies and research reports get published, providing concrete evidence for its benefits. The challenge is simply understanding how to best apply it to your business.

Schedule a free AI assessment and learn how data science can help improve core operations so your organization can better drive revenue, cut costs, and avoid risks.

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