Like every industry, the manufacturing industry has transformed over the past few years – and the pace of transformation continues.
However, despite the technological advancements, the manufacturing industry faces challenges such as talent scarcity, disruption in the supply chain, increasing competition, and rapidly evolving customer needs. To add to the woes, the pandemic has led to further instability.
Legacy systems and processes are not equipped to address these challenges. Manufacturing companies need to be agile and modernize their systems and processes. Digital transformation has become the need of the hour for manufacturing companies. Advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) are playing a significant role in this transformation.
How AI, ML, And IoT are Powering the Manufacturing Industry?
Improves quality of work
Maintaining the quality throughout the process can be cumbersome and labor-intensive. According to Capgemini, leveraging AI and IoT can help manufacturing companies reduce product defects by 2.4% and improve plant productivity by 5.7%. Machines can be trained to find issues at the early stages of manufacturing and fix them before the product is fully ready. For example, manufacturing companies can use a camera and AI tools to analyze the products. The data from the camera can be analyzed to find out if there’s any deviation from the standards. If the AI detects any problem, it alerts the quality manager to stop production. This can help the company reduce rejected batches and improve the quality of products.
Helps with predictive maintenance
Manufacturing companies cannot afford unexpected downtimes. A one-hour breakdown of a machine could result in a loss of $260000. Unforeseen downtimes could happen due to various reasons. But 70% of manufacturing companies cited a lack of knowledge on the machine’s condition as the reason for unplanned downtimes. They don’t know when the equipment needs to be serviced, replaced, or upgraded. Predictive maintenance can help companies to address this problem. IoT devices, sensors, and ML can help predict breakdowns and alert the production manager to schedule maintenance before a breakdown occurs. The company can ensure that the machines work in top-notch condition and prevent unexpected downtime or revenue loss through regular maintenance.
Optimizes supply chain
Supply chain management has become complex as companies have started dispatching products globally. This has led to an increase in demand from customers. However, companies are having a tough time meeting these demands. The pandemic was a clear example of the mayhem that can happen due to disruption in the supply chain. AI and ML can help to bring a balance in supply and demand and enable faster product delivery. AI can help optimize the inventory by checking the available components, expiry dates, and the need for replenishing the stocks to ensure every process step occurs smoothly. It removes all the guesswork from the process. IoT can track shipments using GPS to maintain transparency in the supply chain process.
Improves and accelerates product development
Manufacturing companies need to make product development smarter to keep pace with customer demands. The processes need to be optimized and improved continuously to manufacture high-quality products quickly. AI can be useful as it constantly learns from the production data it receives and enhances the process accordingly. AI algorithms can also be used for generative designing, allowing the designers and engineers to focus on innovation and other complex tasks that could cause barriers in product development. Designers and engineers can do several trials and errors with generative designs to improve the designs and build a better-quality product.
Optimizes costs
A manufacturing company can optimize its costs in several ways. AI and IoT can help companies to achieve it. To begin with, these technologies can be used to predict the maintenance or replacement of machines and enable the product manager to make the right decision at the right time to avoid extra costs. Another area that’s often sidelined is energy consumption. According to a study, manufacturers need 37% of global energy. AI helps companies monitor energy consumption, analyze it, and enable companies to optimize usage. This helps the company minimize operational costs and spend that money on building better products. Most importantly, AI and IoT help boost productivity and efficiency, which helps the company maximize the ROI.
Unlocks the power of analytics
The manufacturing industry is becoming smarter and more data-driven. Companies have started using analytics to understand how the processes and systems work and gain actionable insights to improve them.
For instance,
- Predictive analytics helps identify future risks and make forecasts based on current and historical data. It helps companies to order materials and schedule production.
- Diagnostic analytics help identify the root cause of a problem such as a shipment delay or inventory shortage. It enables the companies to solve it and prevent repeated problems in the future.
- Manufacturers analyze the data in real-time to identify patterns and develop strategies in descriptive analytics.
Analytics depends on IoT, AI, and ML to gather data and make data-driven decisions to improve the workers’ productivity and enhance the product’s quality.
Conclusion
Manufacturers may need to undergo a complete overhaul while implementing AI, ML, and IoT. They need to modernize their systems, re-engineer the processes, and train the workers to use the new systems and processes. But before doing that, they need to define some use cases and demonstrate value to the management and key stakeholders to secure sponsorship and support. Companies can hit a roadblock if they lack expertise in digital transformation. That’s why it’s important to work with an expert who has deep domain knowledge and capabilities in implementation. It will help the company to transform seamlessly and deliver value to customers.