4 ways Artificial Intelligence can Benefit Manufacturers
A predicted 50% reduction in supply chain forecasting errors and a 65% decrease in lost sales!
Rapidly advancing technology such as Artificial Intelligence (AI) is revolutionizing the manufacturing landscape, helping to lead in Industry 4.0. This exciting technology, enabling organizations to cut costs while increasing production, can improve customer service and streamline processes, resulting in potentially massive profit growth. Let’s take a look at the four biggest ways manufacturers can benefit from harnessing all that AI has to offer.
Reduction of Supply Chain error
Supply chains are at the heart of any manufacturing business. Leveraging AI, companies are better able to streamline procurement related tasks and access intelligent data sets. Chatbots are capable of handling mundane supplier conversations, placing purchasing requests, and answering questions regarding processes; as well as receiving and storing financial documentation in line with governance procedures. The result? A predicted 50% reduction in supply chain forecasting errors and a 65% decrease in lost sales, according to McKinsey (1). In addition, improving forecasting also has the knock-on effect of a leaner inventory by as much as 50%, according to Forbes (2).
Improved Quality Assurance (QA)
Within smart factories, different aspects of quality checks can be automated. By using advanced image recognition techniques, AI can visually inspect and detect faults better than the human eye, all without the risk of fatigue or distraction. Many factories are already employing the use of predictive maintenance solutions; where intelligent edge devices are combined with machine learning to flag equipment faults before breaking point to reduce downtime. Employees freed from carrying out monotonous manual monitoring and testing can focus on tasks where their skills are better used. Also, by integrating AI into a manufacturers QA plan, the number of defects discovered can increase by up to 90% when compared to human QA processes (3).
Informed insights with Digital Twinning
Digital twinning involves creating a digital version of a physical item, such as a machine or machine parts. Usage of this increasingly common approach is on the rise, with up to 85% of IoT platforms predicted to include digital twin functionality by 2022 (4). So, what’s in it for the manufacturer? Using a digital version of a real-world item, engineers can feed data in and analyze the results, giving them insights into its current condition and performance. The digital version can also be used to manage the physical machine by sending instructions and actions based on its own data.
Generative design to increase efficiencies
Generative design is where the power of AI is leveraged to investigate and validate a vast number of design options from a list of criteria, decided by humans. Using this technique, parameters are set such as size, weight, materials and cost. AI then takes over and finds the best solutions. Simultaneously exploring and confirming hundreds or even thousands of design options can lead to cost reductions and better product output. Using generative design, Airplane manufacturer Airbus was able to reduce the weight of an interior partition on its A320 aircraft by 45%, resulting in lower jet fuel consumption and a reduction of hundreds of thousands of tonnes of carbon dioxide (5).
The future is here
Manufacturers that don’t adopt AI technology are likely to struggle to stay competitive against those who do. The data already being captured by smart factories today is building a valuable knowledge base. Moreover, the advantages of AI technologies are already being realized. Increasing output, preventing expensive machine breakdowns, and reducing unnecessary waste, resulting in better product quality and lower costs.