In the cutthroat world of modern manufacturing, where a minor disruption in the supply chain can create a ripple effect that is felt around the globe, every advantage counts. Firms constantly strive to maximize their output while reducing costs to improve product quality and boost process efficiency.
To do this, they turn to advanced technology like machine learning and data analytics; often using tools like RapidMiner, which provide valuable and actionable insights from vast amounts of their data.
5 Key Use Cases for Predictive Analytics in Manufacturing
Here is a breakdown of five key areas where data analytics and machine learning can optimize your manufacturing processes.
1. Inventory optimization
Supply chain management—stocking up on raw materials, storing finished products, and coordinating logistics and distribution networks— is a complicated business practice that requires extensive training, data from disparate sources, and an aptitude for sound-decision-making.
The stakes are high because simple supply chain operations decisions have the potential to save or cost your organization millions of dollars. Often, there is no discernible pattern or framework on which to base decisions, and many things can and do go wrong that are completely outside of the supply chain managers control.
A computer model trained on your data, however, can support more confident and accurate decisions on the part of supply chain managers. Such a model can, for instance, help ensure you are never overstocked, understocked, or burdened with unsellable goods, even going as far as to determine the optimal placement of items on your shelves.
Machine learning has been shown to work with uncanny accuracy, even predicting huge swings in supply and demand due to events like Black Friday or the holiday season. This helps manufacturers reap the revenue from sudden surges in demand.
The advantages are so compelling that large companies with complex multinational supply chains such as Amazon and Walmart rely on these kinds of models to manage supply and storage challenges at scale within their massive warehouses, and to maximize sales and profits.
2. Increasing workforce efficiency & safety
Surprisingly, computers can sometimes manage people better than… other people. By gleaning invisible information in vast troves of data, a well-trained model or an AI is able to optimize worker performance and reduce delays in processing and delivery.
Some models find correlations between unlikely factors like factory temperature, staff placement, and duty rostering that have a huge impact on overall efficiency. This can reduce the need for extra staff during rush hour periods and eliminate redundant workers.
In some instances, AI can even automate dangerous or repetitive parts of your manufacturing process—the very things that humans aren’t good at, like repeated lifting of heavy weights or handling hazardous chemicals. This reduces the risk of workplace accidents or errors in the manufacturing process. Automating parts of your manufacturing process brings in a valuable data-driven perspective and helps to ruthlessly optimize for efficiency, quality and output.
3. Improving product quality
In every manufacturing process, there are make-or-break steps where overall product quality is determined; these are often handled by humans who are prone to error. AI and machine learning offer cutting-edge solutions to this. Sensitive stages of manufacturing can be ceded to robots powered by machine learning, which are improved with every single use. This leads to superior product quality as compared to human-made products.
Next is product quality assurance; checking finished goods for faults that make them unsuitable for sale. Cameras, scales, and other sensors operated by a powerful computer model can detect these flaws with extremely high accuracy. This leads to fewer product recalls, fewer dissatisfied customers, and a generally better reputation for your company.
In fact, many of the largest manufacturers in the world—from PepsiCo to Toyota—use artificial intelligence in all their quality assurance procedures and are reaping the obvious benefits. PepsiCo, for example, has used machine learning systems to optimize how it produces its famous Frito-Lay’s chips, from estimating the weight of potatoes to using a laser system to examine the texture of each chip. These have improved product quality by as much as 35%.
4. Equipment maintenance
In the fast-paced world of large-scale manufacturing, problems with equipment are usually not detected until there is a major, costly breakdown. On top of that, your equipment might have a very specific maintenance schedule that can be difficult to keep track of.
Here, artificial intelligence shines as well. By using state-of-the-art error reporting software coupled with powerful AI, broken-down machinery can be detected and repaired cheaply and without impairing your manufacturing process.
Automated scheduling tools can help keep you informed on what pieces of equipment need maintenance at a particular time. AI is also very sensitive to hidden factors like overuse due to changes in your production schedule, which could affect your equipment.
Even better, predictive analytics tools like RapidMiner can also tell you when your machines will break down before they do. By crunching data like temperature and pressure figures from sensors, these tools can predict when equipment failure will occur, enabling you to take effective preventive measures, avoid hefty repair or replacement fees, and costly delays in production.
5. Analyzing customer feedback
Yes, artificial intelligence can also help you make better business decisions. By analyzing data from customer feedback, buying patterns, and general market data, predictive analytics tools can uncover what your customers are unconsciously trying to tell you, and help you meet their needs even better.
This kind of information is extremely valuable in manufacturing—perhaps your customers are dissatisfied with a particular feature of your product, for example, or would like a slightly different version of something you already produce. This sort of valuable information is difficult to obtain without machine learning and can be of enormous advantage to your firm when making crucial business decisions.
Predictive analytics can even help you forecast future changes in customer buying patterns and plan accordingly, by suggesting changes in product design or even an entirely new product offering that meets a future need. Customers move very fast, but by applying machine learning tools where they’re needed most, you can be one step ahead of them.
The Future is Even Brighter
The thing with technology is that it is constantly improving—tools and solutions get better year after year. Data science and AI techniques are finding greater applications in every industry, bringing untold value to them.
More manufacturers are seeing the benefits of predictive analytics and are adapting it into their businesses—a Deloitte survey found that 97% of companies are planning on integrating AI into their business activities over the next two years.
Times are changing, and manufacturers and manufacturing procedures are changing with them. A lot of manufacturers are already adapting popular procedures like predictive maintenance and some are going as far as less popular procedures like new product innovation as a way to use predictive analysis. As research continues, we expect to see even more of these adoptions as the technology becomes more mainstream.
If you want the AI manufacturing advantage, check out our Digital Manufacturer: A Blueprint to AI infographic for more information on how to leverage your data and get ahead of the competition.