Published date: 6 September 2024- Author: SSG Insight
Published date: 6 September 2024
Author: SSG InsightWe’ve said it lots of times before, but effective maintenance management is crucial for optimising operations, minimising downtime, and extending the lifespan of equipment. More often than not, we see organisations collecting huge amounts of data, and not analysing it. To achieve these goals, it is crucial to collect the right data, and ensure you are analysing it appropriately. This guide will outline the key data that we recommend maintenance teams should focus on, and our following guide provides insight into where to start with the analysis.
Firstly, what data should you be collecting to ensure optimal performance and reliability?
- Asset Data – the Foundation of Maintenance Management
Asset Register: Maintaining a comprehensive register of all assets, including make, model, age, location, and specifications, is fundamental. This information provides a clear picture of your equipment landscape and helps in planning maintenance activities.
Asset History: Detailed historical data on equipment usage, repairs, and maintenance activities enable you to track performance over time and identify patterns that may indicate emerging issues.
Warranty Information: Keeping track of warranty details, including coverage and expiration dates, ensures that you can leverage warranties to reduce maintenance costs.
- Maintenance Activities Data
Work Orders: Recording all maintenance tasks, including creation dates, completion dates, task descriptions, and resources involved, is crucial for monitoring the efficiency and effectiveness of your maintenance processes.
Preventive Maintenance (PM) Schedules: Adherence to planned maintenance schedules is key to preventing unplanned downtime. Tracking and analysing PM schedules helps ensure that maintenance tasks are performed on time.
Corrective Maintenance Records: Data on unplanned repairs, including root causes and corrective actions taken, is essential for understanding why failures occur and how to prevent them in the future.
- Operational Data
Performance Metrics: Monitoring efficiency, productivity, and output data allows you to gauge equipment performance and identify opportunities for improvement.
Environmental Conditions: Tracking conditions such as temperature, humidity, and vibrations that can impact equipment helps in creating maintenance schedules that account for these factors.
- Failure Data
Failure Rates: Understanding the frequency of equipment failures is crucial for identifying problematic assets and addressing underlying issues.
Mean Time Between Failures (MTBF): This metric provides insights into the reliability of your equipment by measuring the average time between failures.
Mean Time to Repair (MTTR): This metric helps to understand how long it takes to fix assets and equipment.
Our previous KPI blog provides more detail on where to get started with understanding failure data can be found here.
- Cost Data
Maintenance Costs: Costs associated with maintenance activities, including labour, parts, and downtime, are essential to track for budget management and identifying cost-saving opportunities.
Life Cycle Costs: Analysing the total cost of running and repairing assets helps to make informed decisions about repairs, replacements, and upgrades.
- Labour Resource Data
Resource Skills and Availability: Data on the skills, certifications, and availability of maintenance resources ensures that the right people are assigned to the right tasks.
Labour Hours: Monitoring the hours worked by maintenance staff on various tasks helps in managing workloads and improving productivity.
- Inventory and Spare Parts Data
Inventory Levels: Avoid not having critical spares parts in stock, or being overstocked by keeping track of stock levels.
Parts Usage: Analysing parts usage rates ensures that you always have the necessary parts on hands, without tying up too much capital in inventory.
- Safety and Compliance Data
Incident Reports: Recording safety incidents and near-misses related to maintenance activities helps in improving safety protocols and reducing risks.
Regulatory Compliance: Tracking compliance with industry regulations and standards ensures that your operations meet all necessary legal requirements.
- Predictive Maintenance Data: Leveraging Advanced Technologies
Condition Monitoring Data: Data from sensors and condition monitoring tools (e.g., vibration analysis, thermal imaging) provides real-time insights into equipment health.
Predictive Analytics: Analysing patterns and predicting potential failures using advanced analytics enables you to perform maintenance proactively, reducing the likelihood of unexpected breakdowns.
By focusing on these key data types, maintenance managers can make informed decisions, optimise maintenance strategies, and improve the efficiency and reliability of their operations. Implementing a CMMS solution will support you to record the data mentioned above, although this is not a comprehensive list. If you have any queries about how this data can be stored in Agility, or if you need support from our consultancy team on organising this data, please get in touch at info@ssginsight.com, or complete the form below. Our article on where to start with all of this data can be found here.