5 Benefits of Real-Time Analytics and Smart Manufacturing
It’s a common scenario: A manufacturing facility is running smoothly until a critical piece of equipment fails and production comes to a screeching halt. Unplanned downtime caused by catastrophic failure can easily cost a manufacturer thousands of dollars per hour in lost production time and equipment repair or replacement. Fortunately, expensive downtime can be avoided with real-time analytics and smart manufacturing.
What is Real-Time Data Analytics in Manufacturing?
Real-time data analytics relies on sensors and software to continuously collect machine data, analyze it and present the findings to facility staff in an easy-to-understand format.
The difference between real-time data analytics and previous iterations of data collection is simple: Rather than gathering tons of difficult-to-understand data, real-time analytics collects data on events as they unfold, quickly analyzes it and presents it in a digestible format that allows manufacturers to make timely, on-the-fly decisions to help optimize operations.
Having real-time insight into the process can improve performance, increase throughput, maintain quality and decrease costs. Visibility also helps minimize costly downtime by alerting manufacturers to bottlenecks, poor asset utilization and potential maintenance issues. As a matter of fact, Exasol research states that 75% of manufacturers’ operational decision making would be disrupted without data analytics.
This is because real-time analytics helps manufacturers answer key questions like:
- Why does this event keep happening?
- What is likely to happen in the future?
- How can I act upon this information to prevent problems and optimize production?
5 Benefits of Real-Time Analytics and Smart Manufacturing
Using a data-driven approach leads to process optimization and cost savings. Here’s how.
1. Reduced Downtime
Instead of reacting, you can monitor real-time data from sensors to identify issues before failure occurs. This enables a more proactive, predictive maintenance approach, saving thousands of dollars an hour in lost productivity.
2. Higher Productivity
Real-time analytics identifies bottlenecks and other inefficiencies that impede production. Armed with visibility into the process, you can make decisions to respond to issues and achieve higher throughput.
3. Reach Energy Efficiency/Sustainability Goals
Analytics can monitor energy consumption and identify opportunities for saving energy, reducing costs and lowering your carbon footprint.
4. Prolonged Equipment Lifespan
You can track equipment utilization, condition and usage patterns. This knowledge allows manufacturers to prolong equipment life by redistributing workloads, ensuring routine maintenance and intervening before catastrophic failure.
5. Reduced Costs
Investing in data analytics provides an excellent ROI. By preventing catastrophic failure, enabling predictive maintenance, reducing energy consumption and prolonging asset life, manufacturers can save hundreds of thousands of dollars a year. Applying actionable insight to optimize operations can increase revenue and boost the bottom line without the addition of new production lines or equipment.
Your 4-Step Journey to Real-Time Data
Harnessing real-time data is a clear, step-by-step journey. It’s about moving from reacting to past events to proactively optimizing future outcomes. Here’s how to get started.
Step 1: Start with Descriptive Analytics (What happened?)
This is the simplest and most fundamental step. Before you can predict the future, you must understand the past.
How to start: Begin by collecting and studying data to detect trends. This answers the question: What went wrong?
The benefit: Studying descriptive analytics allows you to understand past issues, providing a baseline so you can find a better approach for similar issues in the future.
Step 2: Evolve to Diagnostic Analytics (Why did it happen?)
Once you know what happened, the next logical step is to find out why. This is where you move from simple reporting to root-cause analysis.
How to start: Use diagnostic analytics to study the root causes of maintenance issues. Investigate specifications like temperature settings, vibrations, lubrication schedules or performance indicators to determine exactly why an event took place.
The benefit: Your maintenance team can identify and address underlying issues, which helps prevent the problem from occurring again.
Step 3: Implement Predictive Analytics (What is likely to happen?)
This is the critical shift from reactive to proactive. Here, you use historical data from Steps 1 and 2 and machine learning algorithms to forecast future events.
How to start: Implement a system that identifies anomalies and alerts operators and technicians when equipment is likely to fail or process upsets may occur.
The benefit: Your team can look at the current data, determine when a breakdown is likely and schedule proactive maintenance before failure. This avoids catastrophic outages and prevents lengthy and costly downtime.
Step 4: Optimize with Prescriptive Analytics (What should we do?)
The final step takes predictive analytics even further. The system not only predicts a problem but also provides guidance on the best course of action.
How to start: Use prescriptive analytics to suggest specific maintenance activities, such as repair, replacement or redundancy, that are intended to prevent failure altogether.
The benefit: You fully optimize your production and throughput by letting the data guide your team toward the most efficient and effective solution every time.
Embrace Smart Manufacturing with JHFOSTER
Embracing real-time analytics and smart manufacturing to make data-driven decisions fosters continuous improvement, proactive decision making and cost savings. In today’s competitive landscape, using real-time data analytics is essential to success. Find out how JHFOSTER can help you harness real-time analytics so you can start saving money and boosting your bottom line. Contact us today to speak with an automation expert.
