From the first harnessing of economies of scale to the introduction of the assembly line, the search for new efficiencies has always been at the heart of manufacturing. Today, the greatest new gains come from the innovative combination of hardware and software. In particular, robotics has revolutionized manufacturing, allowing for greater output from fewer workers.
While robotics has made significant impact for decades now, machine learning (ML) is just starting to realize its full potential. In fact, a 2017 survey by PWC found that only around half of all companies were already using it. Yet, when implemented, machine learning can have a massive impact on companies’ bottom lines.
Machine Learning Is Revolutionizing Manufacturing in 2019
Ultimately, the biggest shift has been from a world where the business impact of machine learning has been largely theoretical to one where it is now quite real. The proven impact of machine learning models has pushed more investment toward their development
Still, there are plenty more gains to be realized. To better understand the potential and how you can harness it for your business, we’ve highlighted 6 key ways that machine learning will impact manufacturing in 2019.
1. Revamp Quality Control
You’ve likely seen plenty of clips showing workers sifting through products whizzing by on an assembly line, looking for flaws. This is overwhelming and exhausting work, and you probably wonder how anyone can maintain the focus necessary to find small flaws for hours at a time.
In a real world example, quality control was crippling GM throughout the 1970s. This led them to take the Toyota Manufacturing Technique and implement it in many of their factories.
Even when advanced manufacturing techniques are implemented, using humans to spot defects and errors is inherently limiting. Our senses and attention span simply have natural limits far below what machine sensors can offer. What does that difference add up to?
Forbes found that machine learning increased defect detection rates by up to 90%.
Quality control is often done by humans because it’s usually visual. If weight or shape is the main quality factor, it’s far easier for a machine task. Scanning for misaligned labels, off-colors, shine levels, and even cracks is fairly simple for a human but very difficult for a machine. Machine learning, however, allows algorithms to visually inspect products and identify flaws more quickly.
This steel manufacturing case study realized the impact that machine learning has when defects are identified earlier in the process – less waste and ability to identify possible causes of the defects. Besides the products themselves, machine learning can even improve the machines that make the products.
2. Minimize Equipment Failures
Determining when to conduct maintenance on equipment is an exceptionally difficult task with huge stakes. Each time a machine is taken out for maintenance, it’s not doing its job and may even require factory downtime until it is repaired.
Frequent fixes mean losses, and infrequent maintenance can lead to even more costly breakdowns. Global costs of equipment downtime adds up to $647 billion dollars annually. Looked at another way: The average international cost of said downtime is $5,600 per minute.
With those costs in mind, it’s no surprise that preventing even a single unplanned outage can pay for the cost of implementing machine learning. How does machine learning minimize these issues, exactly?
Machine learning algorithms are excellent at balancing multiple sources of data to predict and determine optimal repair time. This can be done simply by identifying errors and defects as they occur so they are addressed immediately – not once a human has discovered them at a later time.
In addition, machine learning algorithms utilize historical data to identify patterns of equipment failure, helping them determine when regular maintenance should occur.
Data can also be taken automatically from inside the equipment, eliminating the need for a manual check. Increased speed and efficiency – plus decreased manpower costs – translate into substantial ROI for most firms, but the biggest gains come from a change in how maintenance is conducted.
3. Predict Maintenance Needs
This data boils down to a shift from reactive to proactive repair work. Generally, maintenance is conducted once a problem occurs, due to the high cost of taking equipment offline to have it manually checked for potential problems. When this occurs, managers constantly face an impossible choice: Take equipment offline and incur a loss now, or risk even greater losses down the line.
The role of machine learning is to identify the ideal moment to make that choice, and remove the costly and stressful guesswork. By using machine learning to predict when equipment breakdowns are likely to occur, your company can be far more proactive and ensure they 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.
The Advantages of Machine Learning Go Beyond the Factory
Even after machine learning has helped with quality control and machine maintenance, the resulting product still has a long way to go. For storing and shipping, machine learning has a role to play in identifying inefficiencies, and here’s how:
4. Optimize Supply Chain
Whether you’re looking at replacement parts for your factory equipment – or the products that equipment produces – reliable supply chains are essential for any manufacturing business. As the global economy becomes more complex, so does the challenge of optimizing these supply chains.
A single shift in weather, damaged ships, or change in fuel prices can reverberate throughout your supply chains, greatly impacting your business. Remember that the average time of equipment that’s down is $5,600 per minute. This cost applies just as much if you’re waiting for raw materials as if the equipment is broken.
Machine learning takes this complexity and, in response, optimizes the many factors and elements of your supply chain to make the best possible decision for your business – extra time to give a shipment based on delays or financial impact, deciding where to ship a product from based on weather patterns and many other potential hurdles.
For a successful manufacturing organization, having efficient and reliable production is essential.
5. Optimize Inventory
Closely connected with supply chain optimization, machine learning can have a similar impact on optimizing inventory. Holding costs (the cost of storing inventory) are massive, usually hovering around 20-30% of the cost of a product.
A small reduction of 10% in holding cost can reduce your per-unit costs by 2-3%. For businesses, unsold or undelivered products means paying for storage space, which may not sound like a major issue but the impact on cash flow is immense!
Machine learning can be used to calculate when it makes the most economic sense to hold on, sell or even change the production levels of inventory. In addition to monitoring the supply chain elements above, this is done by closely monitoring market prices, holding costs and production capacity.
Carefully considering and balancing all of these elements has traditionally been a human’s job. With the ever-increasing amount of data reflected in each of these areas, however, humans are a poor choice for the task.
Therefore, the role of machine learning is an obvious one. By analyzing thousands or even millions of bits of information to make decisions, these algorithms go far beyond anything a human analyst is capable of. No surprise then that the results on overall efficiency can be substantial.
Factory-Wide Efficiency Gains from Machine Learning
There are also applications for machine learning that fall further outside of the areas already mentioned. Factories have more inputs than raw materials for production or information for analysis: Factories also run on commodities like electricity.
6. Electricity Consumption
Obviously, one of the greatest inputs for any factory is electricity. While most factories operate 24/7 for peak efficiency, it’s possible for businesses to plan energy-intensive activities for different times and when power is cheapest.
There are obviously many factors that must be considered (energy and labor costs, equipment maintenance, inventory) to make smart decisions around energy consumption, which is where machine learning comes into play. ML algorithms realize the ideal time to perform energy-intensive activities to help businesses save money.
These algorithms can also help invest in electrical infrastructure more strategically by allowing you to precisely quantify the value of your factory’s electricity in real-time.
With ML, you can make smarter investment decisions, allocate resources better, and improve the efficiency of your factories.
Bringing Greater Efficiency to Manufacturing
It’s not surprising that machine learning continues to impact manufacturing in 2019, but it might be more shocking that does so at nearly every stage.
Machine learning can offer substantial cost savings in many areas, from buying raw materials to maintaining equipment. The flexibility of this technology explains its rise in popularity as it has become far more user friendly and less reliant on hiring teams of data scientists.
What’s stopping you from using machine learning today? RapidMiner makes data science more accessible than ever. 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.
We have a lot of experience working with manufacturing organizations, and know that each company offers its own unique set of complexities and challenges. Download RapidMiner Studio today for all of the capabilities to support the full data science lifecycle.
Want to learn more about the latest machine learning trends in manufacturing? Check out our article on 10 Ways Machine Learning Will Reshape Manufacturing in 2021 & Beyond.
Looking to drive real business impact with AI?
Sometimes the most difficult thing is simply knowing where to start. Identifying impactful use cases is one of the most cited roadblocks for organizations seeking to leverage AI.
Learn from these 50 use cases across all industries.
Looking to adopt AI in your manufacturing organization? Start with predictive maintenance – it rises above other use cases in terms of feasibility & impact.