Many manufacturers invest in sensors but lack a system to act on the data. This is where a computerized maintenance management system (CMMS) can act as a bridge to the data. A CMMS centralizes all sensor data and provides a place where manufacturers can turn data into action. A CMMS can help streamline operations by managing worker orders, scheduling preventive maintenance and tracking inventory. This helps organizations shift to proactive predictive maintenance plans from reactive, break-fix strategies.
Predictive maintenance approaches are increasingly implemented by manufacturers due to the vast benefits that they offer. Predictive maintenance plans can yield:
- A significant reduction in unplanned downtime
- Longer mean time between maintenance events
- More efficient, targeted maintenance
- More effective inventory management
- Lower maintenance and production costs
- Improved productivity and quality
CMMS implementation is a key component of achieving all these benefits. CMMS should be considered a core predictive maintenance software component and should be part of any predictive maintenance implementation plan. A CMMS is a digital, centralized repository for all data and metrics related to your maintenance plan, with features including:
- Tracking of maintenance events, including all relevant data
- Preventive maintenance scheduling
- Asset management
- Equipment condition monitoring
- Inventory management data
- Equipment performance data
Predictive maintenance programs can struggle to scale without a CMMS, as the system helps make sense of the wealth of sensor data that accumulates. In this article, we will explore CMMS maintenance software implementation steps and CMMS predictive maintenance functions in greater detail. Read on to see our recommendations for CMMS implementation at your facility.
The role of CMMS in predictive maintenance
A CMMS transforms raw, unstructured machine data from sensors embedded in equipment into actionable items. The CMMS acts as the interpreter between industrial internet of things (IIoT) sensors and maintenance teams. The CMMS translates the temperature, vibration, acoustics, and oil level data from equipment into automated maintenance actions, alerts and work orders. The maintenance team can then act on this data and incorporate it into workflows.
A CMMS performs several vital functions and tasks as part of predictive maintenance. These tasks—and their purpose—include:
- Scheduling preventive maintenance: Preventive maintenance goes hand in hand with predictive maintenance and is an important way to keep equipment in optimal shape. Preventive maintenance can also serve as a data collection touchpoint, which helps to facilitate more effective predictive maintenance. CMMS can help with maintenance scheduling, assigning maintenance tasks, and developing a maintenance strategy. When using a CMMS, you can ensure maintenance activity is not overlooked.
- Recording maintenance data: When maintenance—planned or unplanned—occurs, it is a valuable opportunity to collect and review data about the conditions surrounding the maintenance. This information can be recorded in the CMMS, providing a data signpost of sorts for maintenance schedules, maintenance management, and as to the conditions that might relate to maintenance events. CMMS software also serves the important function of storing, tracking and reporting on the data gathered for facility management. This data can be historically reviewed over time to track failure rates, repair frequency and machine life expectancy. Reviewing historically recorded sensor data can also aid in root cause analysis and trend identification over a period of time.
- Recording performance data: Industrial sensors are a critical component of predictive maintenance and can gather massive amounts of data that enable the benefits of this approach. Data is only as good as its storage and analysis, however, so it is important to be sure that sensors and the CMMS can “talk” to each other so that data is fed through this central collection point.
- Facilitating data analysis: With data collected in the step above, it can then be further manipulated and analyzed to really get into the useful metrics, predictive analytics, and signifiers that enable predictive maintenance to take place. Organizations can use the data to set key performance indicators (KPIs), uncover mean time between failures (MTBF) and mean time to repair (MTTR), and reveal overall equipment effectiveness (OEE). These metrics can help organizations gain greater insight into the reliability of equipment, the average time repairs take, and the overall efficiency of operations. The CMMS helps organize all this wealth of data, while data analytical tools help interpret it and turn the data into actionable insights.
- Managing and overseeing inventory: Aside from direct involvement in maintenance, the CMMS can also be used as an inventory management system, enabling greater accuracy in planning, ordering and spare part availability—all of which benefit the bottom line and facilitate faster more effective maintenance.
How CMMS enables predictive maintenance
CMMS works in a partnership with predictive maintenance by translating raw data into actionable insights. The process begins with IIoT sensors connected to equipment. These sensors collect raw, real-time data on machine conditions. The data is then streamed into the CMMS, providing a centralized dashboard on machine health, uptime, and conditions.
Using data analytics, artificial intelligence (AI), and machine learning, the CMMS organizes, sorts, and interprets the data for actionable insights. The CMMS is programmed to send alert, actions, and workorders to operators when predefined thresholds are exceeded. This helps identify actions operators may have missed and removes delays in assigning workorders. Technicians then perform the maintenance work and report the results to the CMMS. The data flows back into the CMMS and is stored for historical analysis. This aids in the process of machine learning.
IIoT process flow
1. Sensors collect machine data
2. Data flows into CMMS
3. CMMS organizes and logs data
4. Analytics identify anomalies
5. CMMS triggers work orders
6. Maintenance is executed
7.Data is fed back into system
Tips for a successful CMMS implementation plan
The following tips can help with a successful CMMS implementation in a manufacturing facility:
- Understand—and communicate the benefits: Do not fall into the trap of implementing a CMMS—and predictive maintenance—just because you think you should. Take the time to fully understand the benefits of the approach and develop a specific strategy for your facility. This deeper understanding will enable you to communicate the benefits of the CMMS more easily throughout the organization, and a comprehensive strategy will help with benchmarking and ROI tracking.
- Develop comprehensive standard operating procedures (SOPs): The CMMS, and the systems that feed into and out of it, should have SOPs readily available for anyone who wants or needs to review them. This should go beyond the standard out-of-the-box documentation furnished with the software and should be tailored to your operation and goals. Standardization is key to data consistency within a CMMS. This helps ensure consistent, reliable, and actionable reporting and data analytics.
- Guarantee that cross-training occurs: By introducing the CMMS throughout the organization, and assuring that all personnel, from staff to management, are familiar with it, you can achieve a more thorough buy-in to the technology investment. This is all while gaining the benefit of a more effective workforce since use of the CMMS will not be limited to only specialized personnel.
- Take a long-term view of technology investments: The CMMS, and related technology such as industrial sensors, should be part of a long-term investment strategy. To this end, while you may see some immediate results, they will likely only improve and build over time—at which point you will begin to see a positive ROI.
- Understand your technology options: Technology such as the industrial sensors that feed data to the CMMS does not need to be a majorly costly capital investment. While newer equipment is likely to include onboard sensors, you can retrofit sensors to existing equipment at a fraction of the cost of equipment replacement, enabling you to begin implementing CMMS and predictive maintenance in a cost-effective manner.
ATS uses interactive technologies such as CMMS to increase your equipment’s reliability, generate production efficiencies and improve productivity. With our Reliability 360® services and industrial sensor-based maintenance plans, we are ready to discuss your maintenance objectives and goals, and how we can help you achieve them while improving your overall bottom line. To learn more, contact ATS today.
