Being able to produce high-quality products fast at minimum cost has been the ultimate manufacturing goal since forever. Thanks to recent innovations in cloud computing, and big data storage and analysis, artificial intelligence and machine learning are making great strides in improving efficiency in manufacturing environments, leading to better performance. AI provides critical information to help managers make more informed business decisions in a short period of time. Machine learning algorithms, applications, and platforms help manufacturers find new business models, fine-tune product quality, and optimize manufacturing operations.
It is safe to say that among all, the global manufacturing sector is one of the sectors most influenced by AI, machine learning and IoT - collectively termed Industry 4.0 - with great potential for disruption and transformation if these technologies are employed intelligently. And to prove that these are not just words, here are some numbers to back up this statement.
Below are gathered seven use cases of AI and ML that will give you an idea of how to apply these in your own manufacturing business.
By using artificial intelligence and machine learning, systems can test hundreds of mathematical models of production and outcome possibilities, and be more precise in their analysis while adapting to new information such as new product introductions, supply chain disruptions or sudden changes in demand. AI-based approaches to forecasting are expected to reduce forecasting errors by 30 to 50% in some settings. The benefits of applying AI in supply chain management, however, go far beyond that. Lost sales due to product unavailability can be reduced by up to 65%. Furthermore, predictive analytics allows companies to not just ask reactive questions like, “what has happened?” or “why did it happen?” but also proactive questions like, “what is going to happen,” and, “what can we do to prevent it from happening?” These type of analytics can enable manufacturers to pivot from preventive maintenance to predictive maintenance.
Advances in AI and software intelligence are enabling companies to take personalization to the next level by making products and services that are highly relevant to individual consumers. This is important because personalization sells. In a recent survey, 20% of consumers said that they would be willing to pay a 20% premium for personalized products or services. And brands who are willing to personalize products are also able to build greater trust with their customers. According to Accenture, 83% of consumers in both the U.S. and the U.K. are willing to have trusted retailers use their personal data in order to receive tailored and targeted products, recommendations, and offers.
Artificial intelligence combined with IoT gives manufacturers greater visibility into their supply chains with potential to increase productivity and improve planning. Technology is being used to monitor quantities used, cycle times, temperatures, lead times, errors, and downtime to optimize production runs. For instance, real-time tracking of supply vehicles improves fleet utilization, logistics planning, and scheduling, which all lead to more efficient production scheduling. Furthermore, inventory analysis powered by AI can help lower holding costs. The first step in AI’s deployment is an “operator assist” mode, where AI runs in the background and suggests answers to the operator. AI systems will use the operators’ final decisions to learn how the human mind performs so they can be deployed in an “operator replace” mode. It is said that in near future AI will enable manufacturers to transform data into intelligence in a vendor-agnostic environment where all machines speak the same language, increasing production efficiency from machine to machine across the shop floor.
A digital twin is a virtual model of a process, product or service. It can be used to examine how an IoT device operates and behaves throughout its lifespan. Digital twins are being used for process simulation in order to identify “what if” scenarios. This helps manufacturers spot implications of configuration, design or process changes.
AI and ML give manufacturers the ability to predict when potential problems are going to arise before they actually happen. Without IoT systems in place at your factory, preventive maintenance happens based on routine or time. In other words, it’s a manual task. However, with IoT systems in place, preventive maintenance is much more automated and streamlined. Predictive maintenance uses sensors to track the conditions of equipment and analyses the data on an ongoing basis, enabling organizations to service equipment when they actually need it instead of at scheduled service times, minimizing downtime. Machines can even be set up so that they evaluate their own conditions, order their own replacement parts and schedule a field technician when needed. Taking predictive maintenance even further, algorithms based on big data can be used to predict future equipment failures. So, predictive maintenance — as opposed to preventive maintenance — eliminates the guesswork as the machines report their conditions on an up-to-the-minute basis. It also saves businesses valuable time and resources, including labor costs, while guaranteeing optimal manufacturing performance. Organizations are starting to realize that it’s worth investing in predictive maintenance solutions because it’s a sure-fire way to improve operating efficiency and thereby has an almost immediate impact on the bottom line.
Yield losses, i.e., products that have to be disposed of or need to be reworked due to defects, play an important role in complex manufacturing environments. The multistep semiconductor chip production process is a meaningful example because cycle times from the first processing of the wafer to the final chip are typically several weeks to months and include various intermediate quality-testing processes. Testing cost and yield losses within semiconductor production can constitute up to 20 to 30% of the total production cost. Yield enhancement in manufacturing powered by AI will result in decreased scrap rates and testing costs by linking thousands of variables across machinery groups and subprocesses.
Artificial intelligence is also changing the way we design products. One method is to enter a detailed brief defined by designers and engineers as input into an AI algorithm (in this case referred to as “generative design software”). The brief can include data describing restrictions and various parameters such as material types, available production methods, budget limitations, and time constraints. The algorithm explores every possible configuration, before homing in on a set of the best solutions. The proposed solutions can then be tested using machine learning, offering additional insight as to which designs work best. The process can be repeated until an optimal design solution is reached. One of the major advantages of this approach is that an AI algorithm is completely objective – it doesn’t default to what a human designer would regard as a “logical” starting point. No assumptions are taken at face value and everything is tested according to actual performance against a wide range of manufacturing scenarios and conditions.
By aligning automation with data collection and exchange procedures, the adoption of Industry 4.0 concepts undoubtedly provides manufacturers with greater efficiency in their processes.
Streamlining processes and increasing access to helpful data help to maximize productivity and minimize the number of resources used. With less money spent on materials and labor, and fewer customer rejects and manufacturing setbacks, Industry 4.0 also helps manufacturers to boost productivity and revenue growth.
Another important way that Industry 4.0 impacts manufacturing is by driving closer interactions with customers. The technology, data, and information that can help transform manufacturing operations can also make processes and systems more responsive to customer needs. The unique capabilities of interconnected technologies allow manufacturers to respond and adapt more quickly to customer requests and even develop custom orders with less labor and setup time than in traditional manufacturing.