IoT Driving Manufacturing Efficiency
What if you had the ability to employ sophisticated analytics to predict events before they occurred, use real-time actionable information to boost output, and assist your operators in making the best option at any given time? Would you do it? The Industrial Internet of Things (IoT) drives these capabilities, allowing unprecedented operational effectiveness, productivity, and efficiency on a previously seen scale.
According to experts, IoT is expected to greatly influence modern production by enhancing efficiency, enabling simplified maintenance and asset monitoring, among other things.
Manufacturing businesses have traditionally attempted to boost customer value while also reducing time to market by implementing plans to enhance operations, quality, and logistical efficiency, among other things. When it comes to effective product development, actionable data is the most important as it is collected and analyzed to streamline everything from the design process through manufacturing and distribution.
Many new technologies have been implemented in manufacturing to generate new business models and efficiency. These technologies include the Internet of Things (IoT) as well as analytics and artificial intelligence (AI). Many corrective actions and advantages can be found. A recent research found that AI and analytics solutions powered by IoT and other data sources are enabling and favorably impacting various areas of improvement that a typical organization focuses on, including Industrial Automation, as shown below:
The Joining of IoT And Industrial Automation
In the Industrial Automation industry, enterprises that continue to implement IoT initiatives have the best opportunity to break down organizational and process barriers and data and system silos through automating data collecting across divisions and activities. They will be in a good spot to examine and utilize all of the data, so ensuring more effective and lucrative operations. To go a little more specific, the implementation of an end-to-end IoT strategy can have the following advantages:
- Better accuracy and improved efficiency
- Fewer power needs
- Cost efficiency
- Fast processing
- Error reduction
- Better manufacturing control
Use Cases of IoT
Predictive maintenance is one of the most often recognized use cases. It determines the state of assets through the use of analytics. In most cases, it integrates sensor/IoT inputs with external data sources and then does predictive analytics on the results. The objectives are to extend the usable life of assets while reducing downtime. The savings and payback period are both lengthy.
In manufacturing process optimization and automation, autonomous assets can imitate human functions in the production process, resulting in cost savings and improved accuracy at the same time. Although utilizing artificial intelligence is time-consuming and difficult, the payoff and cost implications can be significant.
Furthermore, supply chain optimization makes use of artificial intelligence technologies to enhance inventory management by employing predictive analytics to reduce inventory planning time, cut inventory cost, optimize repairs, and locate ideal reorder locations, among other things.
In order to maximize the advantages to the organization as a whole, it is necessary to identify and prioritize use cases based on their end-to-end value and overall benefits. Due to the increasing complexity of the IT and OT environments on a daily basis, the objective must be to stack rank use cases.
For example, it is necessary to assess the ability of immediate and potential implemented technology to support certain use cases, taking into consideration both the present and near-term future technology stacks, as well as the applications relevant to those use cases.