Your maintenance team is already experimenting. It’s time to make it official.

Where there’s change, there’s risk, especially in maintenance. Someone could get hurt. Or a machine can break down, costing the company millions. Or you get endlessly blamed for something going wrong. 

It’s understandable, then, that trying new things isn’t the first priority for maintenance teams. If things are okay, or even tolerable, why question the status quo?

Unfortunately, it’s this exact sort of culture that kills any chance for continual improvement. It also means maintenance teams don’t have the structure to quickly adjust to change, whether that’s a different set of specs for an asset or a huge shift in production methods. 

This lack of flexibility is especially dangerous in an era where technology is changing every day, skilled labor shortages are getting worse all the time, and industrial companies are frequently pivoting to counter unpredictable market demands.

That’s why this article explores why maintenance teams should develop an experimental mindset and how they can consistently test new ideas while keeping risk to a minimum.

Key takeaways

• Maintenance teams already experiment when they miss PMs, adjust processes, or rely on technician workarounds. The goal is to make those experiments intentional, documented, and easier to learn from.
• Experimentation helps teams make better use of limited labor by showing which PMs, inspections, procedures, and process changes actually reduce downtime, delays, and risk.
• A lightweight program built around PDCA gives teams a safe way to test ideas, review results, and turn proven improvements into standard work.

You’re probably already experimenting with maintenance

Maintenance teams experiment all the time.

For example, every missed PM is an experiment. If you are not able to get all your scheduled work done and decide to skip the inspection at the end of your list, you’re conducting an experiment on what will happen if that task isn’t done.

The difference between doing this kind of thing and calling it an experiment is that putting a label on it allows you to deliberately evaluate, learn from, and improve those choices. You can put a structure around them and impose more control over an environment that is often uncontrollable.

That doesn’t mean maintenance teams should take careless risks. It means the opposite.

The goal is to stop running accidental experiments and start running controlled ones.

Why experimentation matters for maintenance teams right now

Maintenance teams have less room than ever for wasted effort.

There’s a shortage of skilled maintenance technicians and leaders, which is putting a strain on entire teams and facilities. It’s no longer realistic to hire your way out of reactive maintenance. There just aren’t enough qualified people to cover a huge uptick in PMs or equipment failures.

This makes resource allocation a maintenance strategy issue.

Your team has a fixed amount of labor. Every hour spent on something like a low-value PM is an hour not spent on a higher-risk asset, recurring failure, or storeroom problem.

This is where experimentation can drive real results in a short amount of time because it allows you to get answers to key resourcing questions, like:

• Which PMs are reducing downtime
• Which inspections are no longer finding issues
• Which assets need more attention
• Which procedures are actually followed
• Which process changes reduce delays, rework, or confusion
• Which data helps you make better reliability decisions

That kind of learning helps maintenance leaders protect uptime, control costs, and make a stronger case for resources.

Use the PDCA cycle to make experimentation a habit

A strong experimental mindset starts with the PDCA framework: Plan, Do, Check, Act. This structure keeps experimentation grounded, practical, and moving forward. Let’s take a look at how to create an experimentation program around the PDCA framework.

1. Plan: Outline what you want to do, how to do it, and what success looks like based on available information and experience. For example, you might plan PMs based on OEM recommendations.

2. Do: Follow through with your plan. Consistency is key if you want to know if your plan is working. For example, complete the PM you outlined for five cycles, the same way every time.

3. Check: Look at results and compare them to your definition of success. This shouldn’t be a strictly pass or fail evaluation. Some elements might work while others don’t. Or your plan could be working, but have room for improvement. For example, you might see the PMs you planned aren’t finding any failures, but the machine is breaking down anyway, which means a change is needed.

4. Act: Adjust your plan based on the insights from the previous step. For example, you can reduce PM intervals or update the tasklist on the PM to inspect for specific failures that are occurring.

This framework can fit almost any process, whether it’s adopting new technology or reorganizing the storeroom. It’s also simple enough that it makes adaptability a habit instead of something forced on the team in spurts.

An example of experimenting in maintenance

Here is what the PDCA method can look like in practice, using the example of creating a better shift handoff process:

Plan: Identify information that gets lost between shifts, such as undocumented failures, temporary fixes, asset conditions, or operator complaints. Build a short handoff checklist to capture it.

Do: Test the checklist for 30 days on one shift or one line. Keep it short enough that technicians can complete it without feeling like they have been handed another admin task.

Check: Review work orders, talk to technicians, and look for changes. Are failure notes clearer? Are repeat issues easier to spot? Are crews spending less time tracking down context?

Act: Remove fields that are not useful. Add fields for information that is still missing. Then decide whether to expand the checklist to other shifts or assets.

How to build a lightweight experimentation program

Structure creates routine, and routine creates culture. Below is a simple, five-part structure you can use to create an experimentation program for your maintenance team.

1. Create a standard way to surface ideas

Make it easy for team members to suggest improvements. A simple brief is enough:

• What problem are we trying to solve?
• What change do we want to test?
• Where should we test it?
• What result would make this worth adopting?

For example, a technician might suggest improving the parts request process because reactive work orders are delayed while crews wait for parts.

2. Prioritize ideas by impact and effort

Not every idea should be tested right away. Score ideas based on expected impact and effort. High-impact, low-effort ideas should move first. These are the experiments that can create early wins without putting operations at unnecessary risk.

A parts request change might rank high because delays are frequent, costly, and easy to test on one shift for a short period.

This step also helps leaders show the team that experimentation is not a free-for-all. It is a disciplined way to decide where attention goes.

3. Test one idea in a limited scope

Define the parameters of the experiment before any changes are made. Those guidelines might include a single:

• Question
• Owner
• Area, shift, asset type, or process
• Timeframe
• Success metric

For example, one shift uses a revised parts request process for two weeks and logs how long it takes to receive parts. The small scope is the point. You are trying to learn quickly without creating unnecessary disruption.

4. Review the results in a regular meeting

Give each test a standing five-minute slot in an existing meeting. Cover three questions:

• What did we test?
• What happened?
• What do we do next?

For the parts request example, the team may find that parts retrieval improved, but emergency requests became confusing. That does not mean the test failed. It means the process needs one more adjustment before it becomes standard.

5. Adopt, adjust, or close the experiment

Every experiment needs an ending. If it worked, update the standard process. If it partly worked, adjust and retest. If it did not work, document the learning and close it.

This is how experimentation becomes safe and useful. Teams know that not every idea needs to become permanent. They also know good ideas will not die just because they started small.

Experimentation is innovation in small doses

Experimentation does not need to start with a major program or a risky change. Start with one recurring problem your team already talks about, like a missed PM, slow parts request, unclear shift handoff, or procedure technicians rarely follow.

Define one question. Test one small change. Review what happened. Then adopt, adjust, or close it.

That is how experimentation becomes useful instead of risky. It gives maintenance teams a practical way to improve with the resources they already have, while building better habits around data, decision-making, and continuous improvement.