Collecting manufacturing data can help optimize operations, reduce costs and improve customer service. Supply chain leaders should determine the best data collection options for their company to ensure their organization is benefiting from the information.
The technologies for collecting manufacturing data include IoT sensors, RFID tags, digital twins, industrial robotics, smart cameras and AI. All can give further insight into a company’s production line operations.
Learn more about these technologies and the type of data they collect.
6 of the top technologies for manufacturing data collection
Here are six of the top options for collecting manufacturing data.
1. IoT sensors
IoT-enabled sensors allow suppliers to collect and analyze data from across their manufacturing operations. IoT sensors are especially useful due to the following factors:
- The ease of installation, maintenance and replaceability.
- The wide range of data that IoT-enabled sensors can measure.
- The real-time nature of data collection.
- The ability to customize data collection based on specific manufacturing needs.
- The ability to remotely access and monitor devices.
IoT sensors can measure a wide range of aspects of the manufacturing environment, from individual machine performance to overall production line outputs. IoT sensors also integrate with a wide variety of manufacturing systems, reporting tools and other technologies, which can simplify data collection and analysis.
2. RFID tags
RFID tags help suppliers track the number of assets and the assets’ locations in their manufacturing facilities. When a special sensor pings an RFID tag, the tag provides information on the quantity and physical location of the item to which the RFID tag is attached.
Manufacturers can use RFID tags to track the following data:
- The location of specific machinery and handling equipment, such as replacement production line machines, forklifts and autonomous robots.
- The location of inventory and supplies for the manufacturing process.
- The stock levels of raw materials and parts.
The speed with which RFID tags can supply information about assets’ locations can help minimize manufacturing delays.
3. Digital twins
Digital twins enable suppliers to represent the key aspects of their manufacturing operations in a digital system. For example, a manufacturer can create digital twins for its end-to-end production lines to gather information about its production lines’ inputs, assembly and outputs.
These digital representations enable manufacturers to model and test different configurations and scenarios without making potentially costly real-world changes.
Digital twins can help optimize manufacturing processes and enable predictive maintenance. Manufacturers can also combine the technology with other data-gathering tools, such as IoT sensors, to track performance.
4. Industrial robotics and automation sensors
IoT sensors are useful for tracking many aspects of the manufacturing process, but built-in sensors are even better for deep data collection and analysis. These preinstalled sensors can help identify data such as varieties in machine tolerances, product quality and consistency, and need for preventative maintenance.
These sensors can share data between robots and machinery and adjust equipment operations to maximize efficiency if needed.
Built-in sensors work well as part of a collaborative technology approach. For example, individual robots can use their sensors to operate as a fleet, making decisions in real time to optimize manufacturing inputs and outputs.
5. Smart cameras and computer vision
Computer vision and smart cameras are advanced sensors that visually monitor individual products and the entire production line, enabling close examination of any part of the manufacturing process. Computer vision can help improve worker safety and can help avoid bottlenecks by spotting problems before delays occur.
Smart cameras are also helpful for quality control and meeting manufacturing standards. Manufacturers can combine smart cameras with other technologies to enable alerts about problems with products and automatic rejection of defective items.
6. AI
AI can analyze manufacturing data in real time and, in some cases, carry out improvements based on those conclusions.
AI and machine learning can carry out the following tasks:
- Identify patterns in manufacturing data, then highlight any potential issues.
- Forecast future scenarios and identify potential risks.
- Align supply, demand and capacity planning to optimize throughput.
- Optimize manufacturing processes using historical data.
- Identify the need for preventative maintenance.
A bigger data set means more insight from machine learning.
3 benefits of manufacturing data collection
Using various technologies to gather manufacturing data can help improve manufacturing operations in a few different ways.
1. Improve manufacturing productivity
Manufacturing data can help suppliers increase productivity by highlighting bottlenecks in the production process.
Supply chain leaders can also use manufacturing data to ensure that employees are using equipment to its full abilities. For example, manufacturing data may highlight that one of the assembly lines could operate more quickly. Employees can improve overall facility productivity by speeding up that assembly line.
2. Reduce costs
Manufacturing data can help cut down on expenses by highlighting resource waste. Examples include employees using extra materials in the manufacturing process and unnecessarily long equipment downtime.
Manufacturing data can also help improve product quality, which can reduce costs because items will be less likely to be rejected in the assembly line or returned by customers.
In addition, manufacturing data will help improve the company’s inventory levels, which will reduce expenses. For example, data may reveal that the company’s assembly lines are producing more items than the company will sell.
3. Improved customer service
Manufacturing data’s ability to improve product quality can also lead to better relationships with customers.
Improved products can lead to reduced complaints, since customers will be less likely to receive a faulty product.
In addition, manufacturing data improves inventory management, and better inventory management can also lead to improved customer service. Supply chain leaders will be able to predict consumer demand based on peak seasons and ensure that inventory levels will match customer orders.
Manufacturing data can give deeper insight into product deliveries, including the fastest route to customers.
Paul Maplesden creates comprehensive guides on business, finance and technology topics, with expertise in supply chain and SaaS platforms.