How is Data Analytics Transforming Production?

Author: Tom Lin

The Industrial Internet of Things (IIoT), a core pillar of the Fourth Industrial Revolution, is surging with unparalleled momentum – accelerating machine connectivity, producing data, and unlocking potential like never before.

 

Since connected equipment produces vast amounts of production data, you need a strong data analytics strategy to merge information technology (IT) with operational technology (OT).

 

By utilizing the power of advanced data analytics techniques with the data generated in the production environment, you can take data-driven measures to optimize your production processes, improve performance, and ultimately boost your bottom line.

 

 

Production opportunities that can be unlocked through data analytics

Considering how production data fuels the manufacturing industry, many manufacturers are turning towards data analytics to maximize productivity. With a solid data analytics strategy, the abundant production data contributed by sensors, machines, and various systems can be transformative.

 

Let’s dive into the specific advantages that data analytics can bring to your production floor:

 

 

1. Throughput optimization

Data analytics plays a pivotal role in improving manufacturing throughput. By capitalizing on a large amount of data, you can formulate and implement targeted strategies to streamline workflows, enhance productivity, and reduce downtime. Real-time insights into manufacturing operations can also help you ensure production remains on track and ship more products without increasing costs.

 

Optimizing throughput with data analytics allows manufacturers to:

 

• Improve operational efficiency: By accessing and analyzing large volumes of factory data, you can identify and eliminate bottlenecks and inefficiencies in the manufacturing process, leading to increased throughput.
• Enhance productivity: You can take proactive measures to improve overall productivity by identifying resource utilization metrics for raw materials, energy, and labor.
• Reduce costs: Identify areas of potential savings by optimizing the production process, minimizing waste, and improving resource utilization.
• Improve product quality: By analyzing data from various quality control checkpoints, you can apply corrective actions to fine-tune products.

 

2. Asset optimization

Data analytics can help you overcome various challenges in asset performance management. From evaluating material inventories to orchestrating an optimal production schedule, a range of new possibilities will be opened to achieve overall operational excellence.

 

Using data analytics for asset optimization helps to:

 

• Monitor asset performance: By monitoring operational parameters in real time, you can track asset use and costs and proactively identify underutilized assets.
• Optimize cost: By analyzing data related to labor schedules, production cycle times, and material and part inventories, you can optimize production schedules and reduce employee and machine downtime.
• Improve asset utilization: You can identify underutilized/overutilized assets, balance workload distribution, and make rational decisions related to asset allocation, leading to improved efficiency and throughput.

 

3. Predictive maintenance

By leveraging data analytics, you can transform your maintenance activities and reduce unexpected machine failures. The union of data analytics and predictive maintenance will allow you to go beyond static maintenance schedules and help you predict when a machine may need upkeep. With access to real-time data, you will be able to predict when proactive or preventative maintenance is required to maximize plant productivity.

 

Implementing data analytics for predictive maintenance can:

 

• Increase equipment uptime: You can predict equipment maintenance needs by examining sensor data, historical maintenance records, and other related data. This will allow you to strategically schedule maintenance activities and minimize downtime.
• Extend asset lifespan: By addressing maintenance needs and performing timely repairs, you can prevent deterioration of assets, thereby prolonging their usefulness.
• Promote continuous improvement: You can drive ongoing enhancements in maintenance practices by analyzing equipment performance and optimizing maintenance strategies.

 

Selecting the right solution

Data analytics has revolutionized the way manufacturing companies operate and optimize their processes. However, capitalizing on the full potential of data analytics comes with its own set of challenges. Particularly with managing and optimizing real-time data to maximize production capacity.

 

This is where edge computing comes out as a crucial enabler. As an alternative to cloud computing, it helps you implement automation across the factory floor through machine-to-machine communication, keeping data closer to its source rather than sending it to a cloud server for analysis and response.

 

For example, if a sensor finds defects in a beverage canning line, an edge computing system will instantly process this information and signal the assembly line system to pause production. This way, the faulty product could be removed without any delay.

 

Acting on the real-time data on the factory floor will reduce downtime and improve overall product quality, ultimately resulting in higher yield, reduced waste, and lower costs.

 

Data analytics is a transformative force in production manufacturing, and edge computing helps businesses overcome the challenges brought by processing large volumes of data. So, it’s crucial to get help from experts who can implement the technologies required for successful edge computing and analytics strategies.

 

Wavicle’s broad portfolio of solutions helps manufacturers unlock the full power of their data. Contact us to learn more about how to boost your production prowess with data.