We are living in the age of data and data mining. It’s not an exaggeration to state that we are excessively exposed to all kinds of data at various levels – professional or personal. However, not all data is useful, and we process only a small percentage of that data. The same thing applies to business and industrial segments as well.

Businesses nowadays harness large amounts of data, where only a small portion of data makes sense to derive meaningful inferences. Let’s take a simple example. Let’s assume an electrical manufacturing plant deploys huge machinery to produce electrical cables. To keep these machines in good condition and ensure their longevity, their maintenance personnel should regularly inspect and maintain these machines properly to avoid any unexpected breakdowns or production losses. So, how do you know when these machines need maintenance? Or more precisely, which type of maintenance is preferred – preventive, predictive or a combination of both? Is it a guesswork, intuition or based on any evidence?

While guesswork and intuition no more work, it’s the evidence that drives your decision making. So, what is this evidence? This is where your data comes into picture. The means you need to have proper data to support your argument. Okay, even if you gather proper data about the machinery, so, how are you going to use that data to derive meaningful insights?

This is what our blog is all about where we explore the basics of maintenance data, data mining, and techniques & methodologies that we can deploy to derive meaningful insights. Furthermore, we will also discuss how a mobile enterprise asset management (EAM) solution can help in this data mining process.

Overview:

What is Data Mining and Its Importance

Data mining is a scientific process to dig valuable insights from large sets of raw data using varied analytical techniques. These include identification of patterns, correlations and trends that can help in processing better decisions. With data, you eliminate guess work and speculation as your decisions are more data driven, rather than intuition driven.

If applied to industrial maintenance, data mining helps an organization to dig vital data about machinery such as date of manufacture, warranty, manufacturer’s code, running cycles and other technical parameters. This data can be used to predict when equipment is going to fail, or when critical maintenance is required to prolong the life of the machinery. If the machines have already undergone the maintenance cycles a couple of times, this historical data can be leveraged to derive better insights to make informed decisions. These data driven decisions help an organization to ultimately reduce the overall maintenance frequency, thereby cutting costs and improving the overall efficiency.

Core Techniques Used in Data Mining

As defined earlier, data mining follows a scientific methodology to extract meaningful information from the raw data. It comprises of various techniques with each technique targeted for a specific outcome. Let’s discuss these techniques in brief:

Classification: This technique assigns items in a dataset to target categories or classes. Let’s assume you have different categories of machines that needs maintenance. So, based on the historical performance data, this technique groups the equipment as “at risk” or “healthy”.
Regression: Using regression analysis, you can predict a continuous outcome variable, taking into account one or more predictor variables. This helps in easily predicting equipment failures, taking into consideration the past performance.
Clustering: Clustering helps in grouping similar items that do not carry any labels. This way, you can identify patterns within various types of equipment failures.
Association rule learning: This technique predicts interesting relationships between variables in big datasets. For example, it may predict that few categories of machines would fail frequently under certain conditions.

3 key Methodologies for Data Mining in Maintenance

There are a variety of methodologies that can be leveraged for mining maintenance data. These methodologies are as follows:

1. CRISP-DM (Cross Industry Standard Process for Data Mining)

This is an iterative process that comprises of six stages – comprehend the business problem, understand the data, prepare the data, model, evaluate and deploy the data. Each phase logically leads to the next phase, providing scope for continuous improvement.

2. KDD (Knowledge Discovery in Databases)

As the name suggests, KDD comprises of the complete process of uncovering useful information from data. It consists of various steps such as selection, preprocessing, transformation, and interpretation.

3. SEMMA (Sample, Explore, Modify, Model, Assess)

This methodology has been developed by the SAS Institute. This SEMMA methodology concentrates on sampling the data for analysis and exploration to identify patterns, before you model and assess the effectiveness of those models.

Benefits of Mining Maintenance Data

Mining maintenance data offers a plethora of benefits for your maintenance personnel. These include:

Predictive maintenance: This is one of the popular maintenance types that is gaining traction in recent times. This maintenance type relies on analyzing historical data that later helps in predicting when equipment is going to fail or needs routine maintenance. This way, you will be in a better position to schedule the maintenance in advance, letting you draw a fool-proof maintenance strategy.
Cost minimization: With proper data insights, you can optimize maintenance schedules, thereby minimizing unplanned downtime. With minimized unplanned downtime, you tend to reduce costs, resources and of course your valuable maintenance personnel’s time.
Improved safety: When potential failures are identified at an early stage, you tend to reduce risks associated with equipment failures. This way, you foster a healthy and a safe environment for your workers, boosting their morale and overall productivity.
Better decision-making: Data is the power and with this power comes the confidence to take right decisions. Whether it is resource allocation, purchasing new equipment, hiring new personnel or any other operational strategies, you will always be at the forefront to take the right decisions at the right time.

How a Mobile Enterprise Asset Management (EAM) Helps in Mining and Analyzing Maintenance Data

Mobile solutions have literally transformed how we work and deliver results. The same holds true for industrial maintenance as the adoption of EAM solutions have transformed the way maintenance data is collated, accessed, processed and utilized. By adopting an EAM solution within your industrial maintenance framework, you can facilitate:

Real-time monitoring: A mobile EAM solution that leverages advanced technologies like IoT and AI can detect any anomalies in the equipment, thereby providing valuable insights about equipment health. This continuous monitoring of equipment health triggers instant responses to potential issues.
Data integration: An EAM solution can integrate with your in-house ERP system (SAP or Oracle) offering a two-way data synchronization process. This means, your maintenance personnel can have immediate access to ERP data, while they can also instantly submit and process the data related to equipment maintenance using a mobile EAM solution.
Intuitive interfaces: Most of the EAM solutions come with intuitive and user-friendly dashboards that lets decision makers and leaders to visualize data trends and patterns, enabling informed decision making.

Having appreciated the key benefits of a mobile EAM solution in data collation, analysis and decision making, the next big question that arises is which mobile solution to choose among the plethora of options. To take the right decision, evaluate your current organizational requirements, users’ needs, ERP solution that you’ve currently deployed and the budget. From this perspective, explore a right mobile EAM solution like that offered by Propel Apps that ticks all the right parameters with respect to offering the best value for your investment.

This solution transforms plant maintenance and asset management operations using Oracle EAM, design thinking, and enterprise mobile implementations. To know more about this solution and how it can help foster a data-driven culture and effective decision making within your industrial maintenance eco-system, schedule a free demo with us.

Final Thoughts

Mining maintenance is perceived as an art that integrates analytical methodologies with technological innovations. This synergy drives efficiency and fosters data-driven decision making in industrial operations. By appreciating the importance of data mining and deploying the right methodologies, you can accrue substantial benefits with respect to cost savings, empowering the maintenance workforce, fostering good safety practices, and above all building the brand value of your organization.

When it comes to adopting technology in maintenance, a mobile EAM solution plays a critical role for any organization, aiming to optimize its maintenance strategies. A mobile EAM solution leverages advanced technologies like IoT and AI to drive effective data insights to secure a future-proof maintenance system for your organization. Remember, a mobile EAM solution is no longer an option, but a necessity in today’s data-driven world.