How to Analyse Work Orders to Find Bottlenecks on the Plant

Most plants run thousands of work orders every year — yet very few teams systematically analyse them to uncover bottlenecks. Instead, problems show up as symptoms: missed PMs, growing backlog, high reactive work, technicians constantly pulled off jobs, or production complaining about slow response times.

Work orders contain everything you need to pinpoint where your plant is slowing down — if you know how to read them. In this guide, we break down exactly how to analyse work orders to find bottlenecks, using a practical method that works whether your CMMS is SAP PM, Maximo, MEX, UpKeep, Fiix or Maintenance Connection.

This is written for maintenance planners, reliability engineers, supervisors and anyone responsible for eliminating downtime and improving flow.

Why Work Order Analysis Reveals Hidden Bottlenecks

Every bottleneck in a plant creates three types of signals:

• Increased work order volume
• Increased work order duration
• Increased work order delay or waiting time

Your CMMS captures all three.

When analysed correctly, work orders reveal:

• Which assets are consuming the most labour
• Which jobs repeatedly stall due to parts, access, permits or manpower
• Where breakdowns cluster
• Which trades are overloaded
• Where jobs get stuck in WIP long before they're completed
• Whether PMs are actually preventing failures

Done well, work order analysis becomes the fastest way to surface the true constraints holding the plant back.

Step 1: Export the Right Work Order Data

Every CMMS has different terminology, but the fields you need are always the same.

At minimum, export:

• Asset / Functional Location
• Work Order Type (PM, Breakdown, Corrective, Safety, etc.)
• Priority
• Status History (Created, Approved, Scheduled, Completed)
• Start/Finish Dates
• Downtime
• Labour Hours
• Parts Used
• Failure Codes / Cause Codes / Activity Codes
• Trade / Craft / Technician
• Cost

These fields unlock the bottleneck patterns we want to find.

Step 2: Clean the Data (The Step Most Plants Skip)

If you skip this step, everything after will be wrong.

Clean for:

• Missing dates
• Work orders with no asset
• Incorrect downtime entries (e.g., 500 hours on a 12-hour line)
• Incorrect status sequences (Completed before Started)
• Duplicate entries
• Missing labour hours
• Work orders with no failure codes

Clean data = reliable insights.

Step 3: Analyse Work Order Volume to Identify Load Bottlenecks

The first question: Where is the maintenance load coming from?

Look at:

1. Work orders per asset – High concentrations of work point to failure-prone equipment, poor PM strategies or end-of-life assets.

2. Work orders per trade – A single overloaded trade (e.g., mechanical) can slow the entire plant.

3. Work orders by type – Too many breakdowns? → Reactive bottleneck. Too many overdue PMs? → Planning/scheduling bottleneck. Too many corrective jobs? → Asset strategy bottleneck.

4. Work orders per production area – Look for hotspots (e.g., Blow Moulder → Filler → Pasteuriser).

If 20% of assets create 80% of work, those assets are your bottlenecks.

Step 4: Analyse Work Order Duration to Find Slow Steps

Next, investigate where work slows down.

Break duration into three components:

1. Time waiting to be approved – Indicates bottlenecks in supervision or workflow.

2. Time waiting to be scheduled – Often shows planning capacity issues.

3. Time waiting for execution – Usually a labour or prioritisation bottleneck.

4. Actual hands-on tool time – If this is low vs total duration, something is blocking progress.

You can plot:

• Mean duration by trade
• Mean duration by asset
• Mean duration by type
• Mean duration per supervisor or planner

Delays always leave fingerprints in work order lead time.

Step 5: Analyse Status History to Identify WIP Bottlenecks

Every plant has a bottleneck in Work-In-Progress (WIP):

• Waiting for parts
• Waiting for permits
• Waiting for access to the machine
• Waiting for external vendors
• Waiting on production windows
• Waiting on specialist trades

Your CMMS status codes show exactly where work is getting stuck.

Look for:

• A pile-up of "Awaiting Parts"
• Jobs stuck in "Scheduled" for weeks
• Large volumes in "Approved" but never started
• Jobs staying too long in "In Progress"
• Work orders reopened repeatedly

If you want to find bottlenecks fast, analyse this table:

WIP Age by Status

Patterns like these reveal bottlenecks instantly.

Step 6: Analyse Downtime Patterns to Locate Production Bottlenecks

Downtime entries tell you:

• Which assets stop production the most
• Which failures are growing in frequency
• Which PM tasks are not preventing breakdowns
• Where you have reliability bottlenecks

Focus on:

1. Total downtime per asset – Find your "bad actors" — the top 5–10 assets causing 70–80% of downtime.

2. Frequency vs severity – High frequency, low downtime → nuisance failures (irritating but recoverable). Low frequency, high downtime → critical failures (shutdowns).

3. Failure codes – Repeat failure codes → bottleneck in asset strategy or PM optimisation.

4. Production line impact – Plot downtime by line → shows load bottlenecks in operations.

Step 7: Analyse Labour Hours to Identify Resource Constraints

Labour bottlenecks are often misidentified as equipment problems.

Look at:

Labour hours per trade – If mechanical fitters are overloaded → bottleneck.

Labour hours per asset – High hours → bad actors or ageing assets.

Labour hours vs breakdown frequency – Low hours + high breakdown frequency → reactive maintenance starving PM.

Labour hours per PM – High PM hours → too many PMs or inefficient task design.

Step 8: Analyse Parts Usage to Find Supply Chain Bottlenecks

Parts data reveals:

• Which components fail most
• Which failures take longest to repair
• Which vendors are slow
• Where lead times are extending
• Whether stocking levels match asset criticality

Red flags:

• Jobs repeatedly awaiting the same part
• Long lead-time items consistently causing downtime
• Expensive critical components not kept in stock

Bringing It All Together: Your Bottleneck Heatmap

Once you run all analyses above, create a simple heatmap:

This is the blueprint for your improvement plan.

Real-World Example

A bottling plant ran 9,000+ work orders a year. Backlog, breakdowns and overtime were all increasing. By analysing work order data:

• 72% of downtime came from three assets (Filler, Depalletiser, Labeller).
• Jobs spent an average of 11 days stuck in "Awaiting Parts".
• PM compliance was only 58% because technicians were constantly pulled into reactive work.
• Mechanical fitters were consistently overbooked while electricians were underutilised.

The plant corrected stocking levels, rebalanced the trades and rewrote three PM job plans.

Within 90 days:

• Downtime fell 22%
• PM compliance hit 82%
• Schedule compliance increased from 48% → 78%
• Backlog dropped from 10 weeks → 6 weeks

Work order analysis made the bottlenecks impossible to ignore.

Quick-Start Templates

Bottleneck Signals to Look for

• Jobs stuck in a single status
• High downtime assets
• High labour hour assets
• High repeat failure types
• Trades consistently overbooked
• Large volumes of low-priority corrective work
• PMs not preventing breakdowns

The Five Bottleneck Categories

1. Asset bottlenecks (equipment) – High-downtime machines consuming excessive labour and parts.
2. Planning/scheduling bottlenecks – Low schedule compliance, long delays between approval and execution.
3. Labour bottlenecks – One trade overloaded, causing delays across the entire plant.
4. Parts/supply bottlenecks – Frequent "Awaiting Parts" status, long lead times, stockouts on critical items.
5. Production access bottlenecks – Work orders stuck waiting for machine downtime or production windows.

Final Thoughts

Work order analysis is the fastest and most reliable way to uncover bottlenecks across your plant. Your CMMS already contains the clues — what's missing is the structure to interpret them.

When you analyse work orders properly, the bottlenecks reveal themselves:

• Where work piles up
• Where jobs get stuck
• What causes downtime
• Who is overloaded
• Which assets are failing
• What PMs are ineffective
• Where delays repeat

Plants that do this weekly run with higher predictability, lower downtime and far fewer surprises.

How LeanReport Can Help

LeanReport automates the entire work order analysis process described in this guide. Upload your CMMS export (SAP PM, Maximo, MEX, UpKeep, Fiix, or any system) and receive:

• Bottleneck heatmaps showing where work piles up
• WIP analysis revealing which statuses trap work orders
• Asset rankings by downtime, labour hours and failure frequency
• Trade workload breakdowns showing resource constraints
• PM effectiveness analysis identifying which preventive tasks aren't preventing failures
• Parts bottleneck detection highlighting supply chain issues

What takes 6–8 hours manually takes LeanReport 90 seconds.

Start your free trial today and turn your work order data into actionable insights: https://leanreport.io