CMMS and Business Intelligence: Extracting Actionable Insights

If you are a maintenance manager, you understand the importance of having accurate and timely information about your assets, operations, and performance. Business intelligence (BI) is the process of turning data into insights that can aid in decision-making and enhance the results of maintenance activities. 

But how do you get the data you require for BI? A computerized maintenance management system (CMMS) is useful in this situation.

A CMMS is a software application that assists you in managing your maintenance activities, assets, inventory, and resources. Additionally, a ton of information about your maintenance operations is gathered and stored, including work orders, asset histories, downtime, costs, and more. You can use this data for BI purposes, but you must understand how to properly extract, process, and present it. 

According to a study, the advantages of widely used CMMS systems include a 20–50% decrease in downtime, a 5–10% decrease in maintenance costs, an increase in asset life of 20–40%, a 20–25% increase in labor productivity, and a 20–30% increase in inventory accuracy.

In this blog post, we'll look at how you can use CMMS and BI in tandem to get useful maintenance management insights.

Understanding Business Intelligence in CMMS

Business intelligence in maintenance refers to the use of data analysis tools and techniques to comprehend your maintenance performance, identify issues and opportunities, and optimize your maintenance strategies. You can use BI to find the answers to questions like:

• How efficient and effective are your maintenance processes?

• How reliable and available are your assets?

• How much are you spending on maintenance, and what is the return on investment (ROI)?

• What are the main causes of equipment failures, and how can you prevent them?

• What are the trends and patterns in your maintenance data, and what do they imply for the future?

The extraction of useful insights from maintenance data to support decision-making is referred to as business intelligence in CMMS. It involves converting unprocessed data into useful information by using analytical tools, data visualization techniques, and reporting features.

Data Collection and Integration

To extract actionable insights, it is crucial to collect relevant maintenance data. This includes information about:

• Equipment performance, 

• Maintenance tasks, 

• Work orders, and 

• Asset lifecycle. 

By capturing data at various stages of the maintenance process, organizations can gain a holistic view of their operations and identify areas for improvement.

Data from various sources must be integrated into the CMMS to ensure thorough data analysis. Data from sensors, Internet of Things (IoT) gadgets, enterprise resource planning (ERP) systems, and other pertinent sources may be included in this. Organizations can get rid of data silos and get a unified view of their maintenance operations by centralizing all the data.

Data Warehousing and Data Modeling

A strong data warehousing capability should be offered by an efficient CMMS in order to store and arrange maintenance data. This guarantees data accessibility and integrity for BI analysis. Organizations can speed up the analysis process and facilitate effective data retrieval by structuring the data in a standardized format.

Effective maintenance data analysis relies heavily on data modeling techniques. Organizations can gain valuable insights and find correlations between various maintenance factors by establishing relationships between various data entities. Advanced analytical methods like predictive analytics and root cause analysis are made possible by data modeling.

Data Visualization and Reporting

An essential component of business intelligence in CMMS is data visualization. Organizations are able to present maintenance insights in a clear and concise manner by creating visually appealing dashboards and reports. Stakeholders can quickly understand complex data patterns and make wise decisions with the aid of visual representations like charts, graphs, and heat maps.

An easy-to-understand format for insights should be the goal of data analysis. Organizations can make sure that maintenance insights are available to stakeholders with various levels of technical expertise by using intuitive visualizations and succinct narratives. Clear and concise reporting promotes effective communication and data-driven decision-making.

Key Performance Indicators (KPIs) and Metrics

The performance of maintenance operations is evaluated using key performance indicators (KPIs). Organizations should choose and monitor KPIs that fit with their maintenance objectives and goals. Equipment availability, mean time to repair, planned maintenance compliance, and overall equipment effectiveness are a few examples of common maintenance KPIs.

Organizations should define specific metrics to measure and evaluate maintenance performance in addition to KPIs. Metrics give organizations detailed information about various aspects of maintenance operations, allowing them to spot trends, monitor progress, and make informed decisions. Failure rates, asset utilization, and maintenance expense per unit produced are a few examples of maintenance metrics.

Trend Analysis and Predictive Analytics

In order to help organizations find patterns and anomalies in historical maintenance data, trend analysis is a useful technique. Organizations can find insights that result in better decision-making by analyzing trends. Organizations can effectively allocate resources, improve preventive maintenance schedules, and foresee potential maintenance issues thanks to trend analysis.

Predictive analytics takes trend analysis a step further by using historical data to predict future maintenance needs. Organizations can spot early indicators of equipment failure and implement preventative maintenance strategies by utilizing cutting-edge algorithms and machine learning techniques. Organizations can extend asset lifecycles, lower maintenance costs, and minimize downtime with the aid of predictive analytics.

Root Cause Analysis and Failure Investigations

Root cause analysis is a critical process in maintenance management. In order to avoid recurrent problems, it involves looking into the underlying causes of equipment failures. Organizations can use BI tools to analyze maintenance data and find the root causes, such as poor maintenance practices, poor material quality, or operator errors. Organizations can implement targeted corrective actions and enhance overall equipment reliability thanks to this analysis.

Organizations can conduct in-depth investigations based on data insights thanks to BI tools. Organizations can gain a thorough understanding of the factors causing failures or inefficiencies by focusing on particular maintenance events. Organizations can make well-informed decisions about process enhancements, training programs, or equipment replacements with the help of thorough investigations.

Decision Support and Strategy Development

BI data plays a vital role in supporting decision-making processes within maintenance management. Organizations can allocate resources, prioritize maintenance, and make equipment investments in an informed manner by providing accurate and timely information. BI data serves as a decision support tool that enables businesses to maximize their maintenance plans.

The development of maintenance strategies is guided by data-driven insights obtained from BI analysis. Organizations can identify areas for improvement, streamline processes, and put proactive maintenance practices in place by utilizing the power of BI. Improved operational effectiveness, lower costs, and better asset performance are the results of data-driven maintenance strategies.

Data Governance and Quality Assurance

Utilizing BI in CMMS requires careful consideration of data governance and quality control. Organizations must guarantee that the data entered into the CMMS is reliable, accurate, and consistent. For accurate analysis and decision-making, data integrity is crucial. Organizations can maintain data accuracy and guarantee the reliability of BI insights by putting in place data governance frameworks and quality control procedures.

Frameworks for data governance specify roles, processes, and policies for managing data within organizations. Organizations can guarantee the consistency and integrity of their data by establishing clear rules for data entry, validation, and maintenance. Data errors or inconsistencies can be found and fixed with the aid of quality control procedures like routine data audits and validation checks.

Integration of CMMS with BI Platforms

A CMMS's integration with specialized BI tools has many advantages for maintenance management. Beyond the capabilities of a conventional CMMS, it offers advanced analytics capabilities, improved data visualization, and reporting functionalities. Organizations can fully utilize their maintenance data and gain deeper insights into their operations by utilizing specialized BI platforms.

Organizations should investigate the options and evaluate how well they work with their current systems when thinking about integrating a CMMS with BI platforms. It is critical to choose a BI platform that is in line with the unique requirements of the organization, offers strong data integration capabilities, and offers intuitive user interfaces for data analysis and reporting.

Best Practices for Extracting Actionable Insights

Organizations should encourage a data-driven culture within their maintenance teams to effectively extract actionable insights. This entails encouraging the use of data in decision-making processes, promoting data literacy, and offering training on BI tools and techniques. The full potential of BI can be unlocked by maintenance teams, which can also promote continuous improvement by adopting a data-driven mindset.

To keep up with evolving business requirements and technological developments, BI processes and capabilities should be continuously improved. To make sure they are still useful and in line with their maintenance objectives, organizations should periodically evaluate their BI tools, data collection procedures, and analysis approaches. Maintaining a competitive edge in the dynamic field of maintenance management is ensured by continuous improvement.

In summary, Business Intelligence (BI) is essential for obtaining practical knowledge for improved maintenance management. Organizations, under the leadership of CEO Yogesh Choudhary, can use BI tools to make well-informed decisions, optimize resource allocation, and boost overall operational efficiency by leveraging a CMMS like FieldCircle as a valuable source of data.

Deeper insights can be unlocked by setting up a CMMS with specialized BI platforms, which further improves data analysis capabilities. Businesses are urged to adopt CMMS and BI to make data-driven decisions and take advantage of their maintenance data.