Predictive Maintenance in Manufacturing: The Ultimate Guide

If your current maintenance strategy relies heavily on guesswork, your company could find itself unprepared to deal with unexpected machine failures and expensive repairs. If you’re managing mission-critical equipment, reducing the number of unknowns involved in maintenance planning and budgeting is essential, especially when guesswork and uncertainty can expose you to increased financial and operational risks. 

This is where being proactive can make all the difference. Predictive Maintenance (PdM) is a type of condition-based monitoring (CBM) that monitors and diagnoses mechanical failures using machine-level sensors, Internet of Things (IoT) technology, and advanced data analytics. PdM can detect early signs of machine failure and predict when equipment failure will occur in the future, maximize equipment uptime, and significantly reduce maintenance costs. 

This guide to predictive maintenance in manufacturing will examine the benefits of PdM, its implementation challenges and solutions, and specific use cases. We will also present LLumin’s Computerized Maintenance Management System (CMMS+) as the market’s best solution for companies looking to implement predictive maintenance in manufacturing and fully optimize their maintenance processes.

What Is Predictive Maintenance in Manufacturing?

The manufacturing industry depends significantly on the reliability and performance of its equipment. Any equipment failure, malfunction, or poor performance can have severe consequences, leading to substandard end products, heightened costs, increased liability, and reputational damage. PdM enables companies to collect real-time measurement data on equipment performance (i.e., pressure, temperature, vibration, etc.) and take a data-driven approach to maintenance. 

It’s important to note that if a company is already using industrial or machine control systems, those solutions could be used as data sources for implementing PdM. In some cases, a company may need to allocate resources to purchase new equipment, software, and employee training to implement a new PdM strategy. PdM, like condition-based monitoring, relies on machine-level sensors, IoT technology, equipment monitoring systems, and sophisticated data analysis tools to achieve peak performance. As a result, investing in these areas might be necessary to achieve the best ROI and results. 

How Does Predictive Maintenance Work?

To implement a PdM program, a company will either work with its existing machine control systems or install machine-level sensors and start collecting data. At this point, it is critical that a company establishes conditional baselines to serve as control values against which collected data can be compared. 

Following this, a business will monitor operational equipment and analyze data collected to identify patterns and trends in equipment behavior over time.

A predictive maintenance in manufacturing flowchart

This process analyzes equipment performance using infrared analysis, vibration analysis, acoustic monitoring, and other techniques while considering factors such as equipment age, usage patterns, and other performance variables. 

PdM enables a company to set normal operational parameters, predict when maintenance is required, detect anomalies, and respond quickly when working equipment deviates from normal working parameters. 

What Are the Benefits of Predictive Maintenance in Manufacturing?

There are several advantages to using predictive maintenance in manufacturing over other reactive strategies. Taking a data-driven approach to maintenance puts a company in greater control over maintenance budgeting and maintenance, repairs, and operations (MRO) inventory optimization while reducing unplanned downtime and extending equipment lifespan.  

It enables companies to respond quickly to deviations from the norm and contributes to the safety of maintenance personnel by lowering the likelihood of accidents and emergency maintenance. We’ve listed the advantages of predictive maintenance in manufacturing below.

Benefit Description
1. Reduced Downtime PdM enables companies to reduce unplanned downtime and optimize maintenance schedules. 
2. Increased Productivity PdM can improve the productivity levels of your employees and the output of working equipment.
3. Lower Maintenance Costs Predictive maintenance reduces the costs of other preventive maintenance protocols that may involve unnecessary and costly repairs. 
4. Equipment Reliability PdM enables companies to identify problems before they occur and quickly respond to developing issues, improving reliability.
5. Increased Equipment Lifespan Less wear and tear and effective maintenance will extend equipment’s lifespan.
6. Reduced Risk Predictive maintenance prevents equipment failures that increase the risk of accidents. 
7. Increased Overall Equipment Effectiveness (OEE) PdM improves equipment performance and increases overall equipment effectiveness.

Predictive Maintenance Use Cases in Manufacturing

PdM has many use cases in manufacturing that can be applied to various equipment and processes, and we have listed a few use cases below.

  • Pumps, motors, and conveyor belts: A manufacturer can use vibrational analysis to detect wear and tear on pumps, motors, and conveyor systems to predict when maintenance needs to be performed.
  • Heating, ventilation, and air conditioning systems (HVAC): A company might use infrared analysis to monitor equipment temperatures and use predictive analytics to determine the best maintenance schedule for their equipment.
  • Injection molding machines: PdM can be used to monitor equipment performance, optimize equipment performance, and optimize maintenance schedules. 
  • Quality control: PdM can be used to identify defects in products before they leave the production line. This maintenance strategy can alert maintenance managers when a defect is likely to occur by analyzing data from machine-level sensors and machine logs.

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Predictive Maintenance Implementation: Challenges & Solutions

Predictive maintenance is becoming a more popular approach to maintenance in manufacturing, but its implementation can be challenging. In this section, we will look at some of the challenges manufacturers face when implementing PdM and solutions to help overcome them.

The table below outlines manufacturers’ most common challenges when implementing a predictive maintenance program, which includes data volume, technical expertise, equipment complexity, cost, and security.

Challenges Description
Data Volume PdM involves collecting and storing large amounts of equipment data and will require the appropriate tech infrastructure to support these processes. 
Technical Expertise PdM requires technical expertise in data analytics, artificial intelligence (AI), machine learning (ML), and hands-on experience with working equipment.
Data Analysis Data analysis can be challenging to perform when working with large volumes of data, even with a team of data analysts.
Cost Implementing a PdM program can be expensive, requiring investments in sensors, data analytics tools, and technical expertise.
Security Ransomware attacks are always a potential on manufacturing facilities. This has opened up new concerns for these facilities that utilize PdM. 

We have also outlined several potential solutions to overcome these challenges, such as cloud computing, training, advanced analytics, prioritization, and security. 

Solutions Description
Cloud Computing Cloud-based platforms can give manufacturers the computing power and storage capacity they need to manage large amounts of data without requiring large upfront infrastructure investments.
Training Providing your team with the necessary technical expertise can help your company implement and manage a new PdM program. Also, working closely with your PdM software provider, like LLumin, can help users confidently navigate a new implementation. 
Advanced Analytics Working with advanced analytics tools, like AI and ML, can help manufacturers analyze complex data, identify data and trends, and act on this data to improve outcomes.
Prioritization Prioritizing maintenance based on criticality can help companies focus their maintenance efforts on equipment that will most likely benefit from PdM and save costs.
Security Conduct a security risk assessment. Ensure secure data transmission, implement access control, and consider zero-trust protocols. Keep software and tools current with the latest security updates. 

LLumin’s Cutting-Edge CMMS+ Predictive Maintenance Software and Your Company

“LLumin’s CMMS+ software Provides Superior Benefits That Utilize a Rule-Based Engine That Incorporates Data From Operations, Personnel, and Machines.”
A screenshot from LLumin’s CMMS+ of asset statistics.

Utilizing the best CMMS software is essential for operations to run at peak efficiency while elevating the bottom line. 

LLumin’s CMMS+ is 100% HTML5 compliant, browser independent, and mobile-friendly. 

LLumin’s CMMS+ Top Features

Improved Collaboration: Optimized Machine Uptime: Optimized Personnel Management:
LLumin’s industry-leading CMMS+ software integrates asset management processes with manufacturing systems, personnel, and supply chain partners.This integration streamlines maintenance and asset management tasks, optimizes the use of staff resources, reduces costs, and ultimately results in satisfied customers, personnel, and supply chain partners. Machine uptime can be significantly increased while maintenance efforts are optimized by enabling data exchange between maintenance operations and production operations.
LLumin’s CMMS+ software can also monitor real-time machine health data and generate alerts to repair machines before larger problems occur.
LLumin’s CMMS+ software can help you manage maintenance and engineering personnel scheduling by accurately tracking the time required to complete tasks and providing analytics and continuous learning tools to streamline ongoing labor and skills coordination.
Automated Report Generation: Adaptability:
Many industries’ communication scopes include compliance reporting and service level agreements (SLA) commitments. Data and history from machine operations, maintenance work orders, and supply chain vendor transactions may be required for example reports.
These reports can be automated, scheduled, and shared via email notifications, reducing the risk of communication breakdown and the need for manual tasks, follow-up, and administration.
LLumin’s rule-based software is adaptable. Rules can be created to meet the specific needs of a business, and the outcome of these rules can be automated. These rules can be modified as business needs change.

LLumin’s CMMS+ Solution Provides Support to the Following Industries:

LLumin: Advanced CMMS+ Solution for Predictive Maintenance

Whether you are a small manufacturer or a large industrial enterprise, LLumin’s CMMS+ solution can help you optimize your maintenance operations and production processes. Our CMMS+ solution can help you track and manage your assets, plan and schedule maintenance activities, and track the real-time performance levels of your manufacturing equipment.

LLumin prioritizes excellent customer service throughout the entire process. Our implementation team will tailor the software to your company’s specific goals and business processes, providing you with a complete implementation plan and a support team to accelerate and guide your digital transformation.

So, if you are looking for a cutting-edge CMMS+ accompanied by a seamless implementation process and an expert customer support staff, then LLumin is a perfect fit. Join the many satisfied customers who have experienced the benefits of LLumin’s CMMS+ software today.

Getting Started With LLumin

LLumin develops innovative CMMS software to manage and track multiple assets and facilities for industrial plants, municipalities, utilities, fleets, and facilities. To get started and improve your CMMS ROI, we encourage you to schedule a free demo or contact the experts at LLumin to see how our software can help you reach your management goals.