Predictive Maintenance Data Readiness Assessment
Predictive maintenance works only on top of reliable asset data, failure history and a process for acting on findings. This ten-question assessment shows how ready you are and what to fix first.
1. How complete and accurate is your asset register?
Unique tags, hierarchy, make and model, criticality
2. How are failures recorded in work orders?
3. How much failure history do you have in usable form?
4. What sensor or inspection data do you capture on critical assets?
5. Do you link sensor readings to asset records and work orders?
6. Are baselines and alarm limits set for monitored points?
7. Do you know the P-F interval for your key failure modes?
8. Who owns turning a finding into a work request?
9. How reliable are your timestamps, runtime and operating context data?
Running hours, load, product, shift, speed
10. Can you measure the result of the program?
How to use it
Answer each question based on what you can show, not on what is planned. Ask the planner and a technician to check your answers, since asset registers and failure codes are often better on paper than in practice. The score is converted to a percentage and matched to a band with a verdict and recommended next steps. Export the result to share with your maintenance manager or IT team.
What the questions measure
Questions cover four areas: asset data (register, hierarchy, criticality), history (failure codes and years of records), sensing (route and online data, linkage to the asset), and process (limits, ownership, metrics). Weakness in any one area limits the others. For example, perfect sensors with free-text failure records cannot tell you what a signal predicted, and good analytics with no owner will produce alerts nobody acts on.
Reading the result
A low score is not a reason to delay all work. It tells you where the cheapest improvements are, often code discipline and asset tagging. A middle score fits a pilot on a narrow scope. A high score means the main risk is not data but adoption, so invest in review meetings and measurement. Re-take the assessment every six months to see progress.
Common mistakes
Buying a platform before fixing the asset register is the most common. Starting with too many assets, expecting models to learn from data with no coded failures, and ignoring operating context such as load and speed also lead to disappointing results.
Frequently Asked Questions
How much failure history do I need for predictive models?
Machine learning models need many examples of each failure mode, which most plants do not have. Simpler approaches such as limits, trends and rules need far less data and often deliver most of the value for the first year.
Is predictive maintenance only for large plants?
No. Small plants often benefit from basic condition monitoring on a few critical assets. The key is a clear failure mode with warning time and a process to act on it, not the size of the site.
Do I need a CMMS first?
You need a reliable way to record assets and work, whether in a CMMS or an ERP maintenance module. Without it, there is no place to attach findings or to measure results.
How often should I re-run this assessment?
Every six to twelve months, or after major changes such as a new CMMS, a new sensor program or a reorganisation of the maintenance team. Compare scores to see where progress has stalled.
If the gaps are in how your ERP or CMMS holds asset and failure data, we can review your setup and suggest practical fixes. Ask for an expert review.
Related Tools
Condition Monitoring Program Readiness Checklist
A practical checklist for starting or auditing a condition monitoring program, from asset selection to closing the loop in the work order system.
Maintenance & ReliabilityCondition Monitoring Alarm SQL Query Generator
Fill in your table and column names and get SQL that finds sensor readings over limits and flags assets with no open work order.
Maintenance & ReliabilityCondition Monitoring Trend and Time-to-Alarm Python Script Generator
Get a standard-library Python script that fits a trend to recent sensor readings and estimates how many days remain before an alarm limit is crossed.
Discrete ManufacturingPredictive Maintenance ROI Calculator
Compare baseline unplanned downtime cost against predictive maintenance monitoring cost to see net annual savings across your asset fleet.
More Maintenance & Reliability tools
See all- TPM Autonomous Maintenance CIL Standard Template
- Vibration Fault Signature Diagnosis Reference
- ISO 4406 Oil Cleanliness Code Reference
- Infrared Thermography Severity and Emissivity Reference
- Condition Monitoring Finding and Recommendation Report Template
- MTBF and MTTR Worksheet
- TPM Autonomous Maintenance Standard Template
- TPM Pillars and Autonomous Maintenance 7 Steps Reference
- Preventive Maintenance Schedule Template