Work Order Best Practices: What to Track and Why It Matters

What makes a good work order

Most maintenance teams track too little or too much. They either write “fixed pump” and close the ticket, or they fill out 15 fields that nobody reads. A useful work order captures exactly seven things:

1. Clear title — Describes the problem or task in plain language. “Hydraulic leak at cylinder #3 on Press 2” beats “Press 2 issue” every time.
2. Asset — Which machine, vehicle, or piece of equipment. Not “the one in the back corner” — the actual asset name or ID.
3. Priority — A simple three-level system works for most shops: Emergency (production stopped), High (will stop production within 24 hours), and Normal (schedule it this week).
4. Due date — Every work order needs a deadline. Open-ended tasks get done “eventually,” which means never.
5. Checklist or steps — What needs to happen, in order. This turns tribal knowledge into a repeatable process.
6. Time tracking — How long did the work actually take? This is the single most undervalued field on a work order.
7. Failure code — What caused the problem? Bearing failure, electrical fault, operator error, wear, contamination. Five to ten codes cover 90% of failures.

The five mistakes that make work orders useless

If your team dreads filling out work orders, the problem isn't laziness — it's that the work orders are designed poorly. Here are the five most common mistakes:

1. Vague titles

“Machine down” tells you nothing six months from now. When you're trying to figure out why Press 2 has been down four times this quarter, you need specific titles that describe the actual symptom or failure. Train your team to answer: what is broken, and where?

2. No time logging

Without time data, you can't calculate labor cost per asset, compare repair time across techs, or justify hiring another maintenance person. The tech doesn't need to track to the minute — rounding to the nearest 15 minutes is fine. But zero is not fine.

3. Skipping failure codes

Failure codes feel like extra work when you're closing a ticket at 11 PM after a breakdown. But they're the raw material for every smart decision you'll make later. Without them, you can't run a Pareto analysis. You can't spot patterns. You're just reacting.

4. Too many required fields

Every field you add to a work order form is friction. If a tech has to fill out 20 fields to close a 15-minute task, they'll start skipping fields or avoiding the system entirely. Keep required fields to the seven listed above. Everything else is optional.

5. No review step

Completed work orders that go straight to “closed” miss the chance for a supervisor to spot patterns, verify quality, and coach techs on better documentation. Add a quick review step — it takes 30 seconds per work order and dramatically improves data quality.

How work order data drives decisions

The real value of good work orders shows up three to six months after you start collecting data. That's when you have enough history to see patterns that were invisible before.

Pareto analysis of failure codes

Pull your failure codes for the last quarter and rank them by frequency. In most shops, you'll find that two or three failure codes account for 60-70% of all breakdowns. Maybe it's bearing failures on your conveyors, or electrical faults on your oldest press. Once you see the pattern, you can build targeted PMs to prevent those specific failures.

This is how reactive maintenance teams become proactive ones. You're not guessing at PM schedules — you're building them from actual failure data from your own equipment.

Labor hours by asset

When you track time on every work order, you can calculate total maintenance labor cost per machine. This answers the question every plant manager eventually asks: “Should we repair or replace this machine?” If Machine A consumes 40 hours of maintenance labor per month while Machine B takes 5, that's a clear signal.

Repeat failures

When the same asset generates the same type of work order three times in two months, you don't have a maintenance problem — you have a root cause problem. Good work order data surfaces these repeats automatically so you can investigate before the fourth occurrence.

The work order lifecycle

Every work order should move through four stages. Skipping stages is how data quality degrades.

1. Open — The work order is created with a clear title, assigned to a tech, and given a priority and due date. This is the “backlog” stage.
2. In Progress — A tech has started the work. The clock starts on time tracking. If the tech needs parts, the work order stays in progress with a note, not closed and reopened later.
3. Completed — The work is done. The tech logs time spent, selects a failure code (for reactive work), and adds any notes about what they found. Photos are worth a thousand words here.
4. Reviewed — A supervisor or maintenance lead reviews the work order for completeness and accuracy. Did the tech log time? Is the failure code reasonable? Is there enough detail to be useful six months from now? This step takes 30 seconds and prevents garbage data from polluting your reports.

Getting your team on board

The hardest part of work order management isn't the system — it's the habit. Here's what works:

• Start with reactive work only — Don't ask techs to create work orders for PMs, inspections, and reactive work all at once. Start by tracking breakdowns. Once that's a habit, add PM tracking.
• Make it fast — If completing a work order takes more than 2 minutes on a phone, the tool is the problem, not the tech.
• Show the value — After 30 days, pull the data and show your team the top failure codes and highest-labor assets. When techs see their data being used to make their jobs easier (better PMs, fewer emergency calls), adoption follows.
• Don't punish incomplete data — In the first month, celebrate every completed work order, even imperfect ones. You can coach on data quality once the habit is established.

The bottom line

Work orders are not about compliance or creating a paper trail. They're about building a maintenance history that lets you make data-driven decisions. Every vague title, skipped time entry, or missing failure code is a decision you won't be able to make later. Keep the form simple, make it mobile, review the data, and act on what it tells you.