Lubrication is a maintenance process that is traditionally run through intuition. Seasoned technicians and engineers rely on their first-hand experience with a machine to make lubrication decisions.
However, relying on your gut feeling to lubricate or inspect your lubrication points is a process that is prone to inefficiencies and risks of harming machinery and equipment. While intuition and experience can certainly be helpful, it’s not wise to base all your lubrication decisions on a gut feeling.
Entering data analytics, an approach that is currently revolutionizing every area of business, including lubrication management. Gone are the days of lubrication guesswork and reactive maintenance. With the power of data analytics, organizations can optimize lubrication schedules, predict failures before they happen, and ensure that every machine performs at its best.
Through data-driven decision-making (DDDM), equipment insights can be gathered and analyzed in real-time to minimize failure and the resulting downtime and operational losses.
This article explores how data-driven decision-making is transforming lubrication management from being reactive and intuition-based to a proactive and strategic process.
Here, you’ll learn what DDDM means and how it can be applied in lubrication management. Finally, you’ll look into the concept of bad data and how it can prevent you from reaping the benefits of practicing DDDM in lubrication management.
What Data-Driven Decision-Making (DDDM) Means?
Data-driven decision-making refers to the practice of using data analysis and interpretation to make decisions rather than relying on intuition, experience, or anecdotal evidence. The key aspects involved in DDDM include:
Data Collection - Gathering accurate facts, metrics, and other information from relevant sources such as sensors, databases, and surveys.
Data Analysis - Using analytical techniques such as statistical methods, machine learning, and data visualization to extract meaningful insights from the data collected.
Informed Decision-Making - Evaluating data insights to make decisions in optimizing operations, developing strategies and programs, and solving problems.
Continuous Improvement - Revisiting decisions and refining current strategies as new data becomes available and new insights come to light.
Data-driven Decision-making in Lubrication Management
In lubrication management, data-driven decision-making can have the following valuable applications:
1. Monitoring Lubricant Health
Using in-line sensors, lubrication management software, and similar technologies, maintenance teams can collect real-time data on lubricant temperature, viscosity, moisture content, and contamination levels. This data provides insights into indicators of lubricant health, such as the degree of degradation, contamination, and wear.
These insights, in turn, allow for early and preventive actions. Furthermore, by charting and analyzing the historical data on lubricant health, teams can predict when lubricants are more likely to require replenishment or replacement and implement actions accordingly.
2. Predictive Maintenance (PdM)
Predictive Maintenance strategies rely on real-time and historical data to monitor and predict failures before they happen. Data-driven decision-making helps establish lubricant performance trends and other critical data to predict equipment failures.
Thus, DDDM helps prevent costly breakdowns by addressing issues before they escalate. In addition, DDDM improves PdM by supporting root cause analysis, a process that analyzes data before, during, and after failure to identify underlying causes and implement long-term fixes rather than short-term solutions.
3. Asset Performance Management
DDDM plays a critical role in asset performance management by gathering insights on equipment reliability. Using software and similar tools, teams can assess the impact of lubrication quality on machine reliability.
These tools provide actionable insights on whether a change in lubricant or adjustment in application techniques could improve equipment efficiency and lifespan. One of these specialized software is Redlist’s Lubrication Management Software.
Redlist can also track key performance indicators (KPIs) like downtime reduction, failure rates, and maintenance costs, offering a clear picture of the ROI from lubrication practices.
4. Integrated Maintenance
Through the comprehensive data collection and evaluation established by the practice of DDDM, lubrication management can be easily integrated with other maintenance areas. This integration creates a holistic view of equipment health and maintenance needs, enabling data from lubrication management to feed into broader maintenance strategies.
The result is a more cohesive approach to asset management that balances lubrication, repairs, and inspections.
Bad Lubrication Data in Data-Driven Decision-Making
Because DDDM relies on accurate and high-quality data to make effective decisions, bad lubrication data can lead to poor outcomes, inefficiencies, and increased costs. Bad data can be any data that is incomplete, inaccurate, outdated, or inconsistent.
Bad data can undermine the value of DDDM in lubrication management and even mislead teams into making the wrong decisions. To fix bad data, you must first understand its potential sources, including:
Data Entry Errors - Bad data can come from human errors in manually inputting data, such as typos, omissions, or misinterpretations.
Outdated Data Collection - Bad data can be caused by systems that are incapable of capturing real-time and accurate information.
Inconsistent Standards - Bad data can happen when different departments, teams, or locations use varying formats or standards in reporting, organizing, and analyzing data.
Incomplete Data - Bad data can also mean having data points that are missing or incomplete, resulting in gaps that limit the full understanding of an equipment's lubrication performance or health.
Redlist for Data-Driven Lubrication Management
Data-driven decision-making can be one of the most valuable practices in lubrication management. However, DDDM can only be effective with high-quality and reliable data. Redlist’s Lubrication Management Software, a powerful and comprehensive data analytics software, effectively addresses the challenge of mitigating bad data and improving decision-making in lubrication management.
Redlist can automate data collection, provide real-time monitoring, and offer standardized workflows to ensure that the data used in DDDM is accurate, consistent, and actionable. Also, Redlist allows teams to perform data validation and cleaning procedures, making it easy to review and correct data.
Thus preventing outdated or irrelevant information from negatively affecting decision-making. With its mobile accessibility, Redlist enables lubrication teams to collect and access data in the field, ensuring that data is captured in real-time and on-site, reducing delays or errors associated with delayed manual data entry.
Let Redlist improve your data quality and get the most out of your data-driven decision-making practices. Schedule your free demo with our lubrication management experts today!
Comments