Desafíos de implementación de CMMS | 7 maneras de superarlos

We’ve all heard the term “garbage-in, garbage-out” when it comes to data. At the same time, more and more organizations rely on gigabytes of data every day to make decisions. It’s therefore critical for companies to understand how to collect high-quality maintenance data to build the foundation needed to make the right business decisions on a daily basis.

Many maintenance teams rely on computerized maintenance management systems (CMMS) to collect, track, store, and analyze their data. Unfortunately, such a system has the danger of becoming a black hole of unassigned work orders, erroneous status tags, or simply an abyss of incorrectly assigned work. When implemented correctly, however, a CMMS can provide an organization with some truly incredible data. 

Define Clear Objectives

Before you start collecting or auditing your data, you need to know what you want to achieve. Take a step back, bring together key members of a multi-functional team and define your objectives and strategy. Are you aiming to reduce downtime, extend the life of equipment, or optimize maintenance schedules? 

By considering your big-picture business strategy, you’ll be able to better understand what you’re hoping to accomplish with your data collection initiatives.

Choose the Right Metrics

Hundreds of key performance indicators (KPIs) are available today, but it’s critical at this point that you select only three to five that clearly align with your objectives. Some common metrics include:

• Mean Time Between Failures (MTBF): Measures the average time between equipment breakdowns.

• Mean Time to Repair (MTTR): Indicates the average time it takes to repair equipment.

• Equipment Uptime: The percentage of time the equipment is operational.

• Maintenance Costs: Tracks spending on maintenance activities.

Most importantly, you want your selected metrics to be relevant, measurable, and actionable for your business, its current strategies, and within a specific time period.

Audit Your CMMS Records

Now that you understand your objectives and have selected your relevant KPIs, you’ll want to see where you’re starting from. Assuming you are already using some kind of CMMS to collect data on your critical assets, begin the process of focusing on high-quality data by conducting an audit of service requests, work orders, maintenance costs, and purchase orders. Audit not only the different records in the CMMS, but ensure your collection processes for these records in the future are effective and actually being followed.

Here are some areas to consider during your audit:

• Records that fall outside of standardized queries

• Additional data needed to support KPIs

• Data needed to make smarter daily decisions

• Accountability and responsibility assignments

• Recent configuration or system changes

Schedule regular follow-up audits to identify, discuss, and plan ongoing issues so they can be properly addressed in a timely manner. Be sure to prioritize and categorize these issues so they can be tackled in a manageable manner.

Establish Expectations

Whether you’re starting from scratch or from a completed audit, start again. Esure that everyone is on the same page when it comes to inputting quality data. The maintenance manager should work closely with the CMMS implementation team to define and understand what “quality data” means. 

For example, a maintenance technician might think that entering every work order into a system is the right direction, yet that may not address critical asset issues. Instead, use real-life examples and walk through how you expect your team to input the data. This will avoid data that is too vague and unhelpful or too specific and time-consuming to enter. It will also build confidence in those responsible for data entry, increasing the likelihood of compliance.

Ongoing Training

Be sure you offer ongoing training in using the CMMS so that your team has the knowledge and skills they need to use the system effectively. You may want to work with your CMMS partner to take advantage of the training resources they offer.

Be sure to revisit what types of training your team needs on an ongoing basis so they are always upskilling and using more and more of the CMMS system effectively. Many tools today can help streamline data collection and ensure accuracy. Modern systems often come with features like automated data entry, real-time monitoring, and analytics.

Some can use sensors and IoT devices to gather real-time data on equipment performance and condition while others will require manual data entry. In the latter case, ensure that maintenance personnel record data consistently and accurately by providing clear guidelines and training to avoid errors.

Build a Data-Driven Culture

By establishing a solid foundation and implementing ongoing training, you are on your way to building a culture that values solid data. Be sure to illustrate to your team why quality data is important. Show them how they can use the resulting reports to do their own jobs better, which should be then tied to company objectives around revenue and compensation. Tie their efforts in maintaining high-quality data with rewards that make their own job easier and positively impact their own recognition and compensation.

Make sure individuals work as a team. Help them see that high-quality data will affect the overall business and build buy-in from the beginning. Address questions and collect feedback from those on the front lines. This will help encourage them to engage if they feel that they’ve been part of the process. 

Once a culture is put in place, your team will begin uncovering more data-related issues and inefficiencies and begin to think how they can be improved. Employees will hopefully gain a better understanding of the overall processes, leading to more productive discussions and improvements. 

Use Data Analytics

Leverage data analytics to gain insights from the collected data. Advanced analytics can help identify trends, predict failures, and improve maintenance strategies. For example, descriptive analytics can help you understand past performance and historical trends while predictive analytics can help you forecast potential issues before they occur based on historical data. Prescriptive analytics can recommend actions to improve maintenance strategies.

Conclusion

Collecting high-quality maintenance data is not just about gathering numbers; it's about creating a reliable foundation for making informed decisions that drive operational excellence. By defining clear objectives, standardizing processes, investing in the right tools and training, and continuously improving, you can ensure that your maintenance data serves as a valuable asset for your organization.