Embracing AI in Manufacturing: A Key to Staying Ahead of the Competition

As the demand for increased productivity, efficiency, and quality continues to grow, many manufacturing companies are turning to Artificial Intelligence (AI) to achieve these goals. AI has the potential to revolutionize the way manufacturing companies operate by improving production processes, reducing downtime, and increasing the accuracy of quality control.

1. Manufacturing Process Automation

• Human-operated factories could meet costly problems because of human mistakes of manually adjusted equipment settings.
• Monitoring multiple signals across numerous screens, operators sometimes take shortcuts, incorrectly prioritise activities, and don’t necessarily focus on adding economic value.
• The reasons are the human factor and work overload. Control systems put the responsibility of many tasks such as troubleshooting, running tests, etc. on the operators.

Two problems arise with this approach:

1. Equipment flaws, thus reducing the efficiency of manufacturing processes resulting from human factor;
2. Experienced and qualified human operators are a “special or edition” and hard to replace. When such a specialist leaves the place, it could result in knowledge and operational efficiency loss.

With machine learning AI systems, a manufacturing company can boost overall productivity, keep all the details of the processes in-house, and at the same time diminish workforce budget. In addition, other applications provide the following benefits:

1. Assembly Line Integration & Optimization

Much of the modern equipment sends a vast amount of information to the cloud. However, this data can often be of different types and is incompatible. Consequently, human operators must be highly qualified for inventory management to monitor several dashboards and understand the whole picture. AI applications can pull data from the internet-connected equipment and make a clear view of the operations.

Furthermore, this technology allows a variety of automation. For example, the system can alert supervisors when equipment operators show fatigue signs. Likewise, in case of a piece of equipment breakage, the system can not only notify supervisors but also automatically activate emergency plans.

2. AI Based Connected Factory

• Smart factories (also known as connected factories) are systems with minimum human involvement in monotonous tasks, plus entire plant data automation via cloud solutions. Those factories run almost touchless, from the product design stage to customer support.
• Centralized information minimizes errors, signals duplications, and misleadings, as all the processes use the same data source.
• Fast information exchange (for example, via 5G) enables real-time actions. Thus, the machines and human workers can monitor all factory floors, assembly lines, production, and distribution in real-time.
• The intelligent factory systems will automatically alert when orders arrive, inventory runs short, and whatever particular KPI you set up. In addition, various cloud techniques allow easy scaling of the manufacturing processes alongside fast tech integrations.

2. AI in Logistics

Manufacturers can sometimes reduce dependency on distant but cheap manufacturing facilities. AI in manufacturing can solve the logistic problem by producing serial parts in-house or at near-shore facilities with 3D printing, thus, managing inventories more efficiently.

• 3D printing is the basis of additive manufacturing. As its name implies, the technology directs hardware to add layer by layer to create particular objects with the help of data computer-aided-design (CAD) software or 3D object scanners.
• Contrarily, a traditional way to create an object often requires removing material through milling, machining, carving, shaping, or other means.
• In 2022, AI usage in additive manufacturing focuses on design enhancement, efficiency improvement of the 3D printing processes, and autonomous manufacturing. Soon, advanced AI solutions will reduce design complexity and decrease the operator knowledge requirements for the additive manufacturing industry.
• The manufacturing industry can leverage artificial intelligence as delivery robots to minimise human touch(a helpful feature during pandemics) and provide uninterrupted last-mile deliveries.
• An example from Estonia is Starship. Their robots are advanced autonomous devices using mobile technology and computer vision to carry items over short distances.
• The delivery platform accepts requests via mobile phones to transport parcels, groceries, and food. In addition, the client can monitor the robots’ entire journey and location after placing the order on a smartphone.

2. AI for Predictive Maintenance

Predicting and reducing failures can yield significant cost savings. McKinsey claims that predictive maintenance is AI’s most significant value in manufacturing, which accounts for $0.5-$0.7 trillion in value worldwide.

AI uses vast data provided by sensors; this is the part of IoT (internet of things), the technology that connects and exchanges sensors’ data with other devices and systems via communication networks.

Final Thoughts

Finally, supply chain optimization using AI can help manufacturing companies to better manage their inventory levels, reduce lead times, and improve customer satisfaction. By analyzing historical data, AI can predict demand and optimize inventory levels, resulting in better use of resources and reduced costs.

As the manufacturing industry continues to evolve, companies that embrace AI and other advanced technologies will gain a competitive advantage. Join the AI revolution and stay ahead of the curve!

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