Basic Logistics Process Data Analysis

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This blog has been written with Logistics examples, but the same logics applies to any process in any industry! - Dave Hunt

Imagine you’re running a warehouse, and your pickers are always late getting orders ready for the 2 pm delivery truck. Annoying, right? That’s what happens when you don’t analyse your process data. So, let’s break it down: what is process data analysis, and why do you need it?

What’s Process Data Analysis Then?

It’s just a fancy way of saying, “Let’s look at the numbers to figure out what’s really happening.” In any process – whether it’s picking products or packing pallets – stuff happens, and data is the trail it leaves behind. Analysing this data helps you figure out what’s going well, what’s not, and how you can fix it.

Why Should You Care?

If you don’t look at the data, you’re guessing. And guessing is fine for a pub quiz, but rubbish for running a warehouse. Process data analysis helps you:

Spot Problems – See where things are going pear-shaped.
Improve Efficiency – Find the bottlenecks and sort them out.
Make Better Decisions – Data doesn’t lie (unless you mess it up, but that’s a different blog).

The 3 Steps to Process Data Analysis ( Collect , Visualise & Analyse/Act )

Collect the Data

Gather the info. This could be:

Picking times per order.
Number of picking errors.
Distance travelled by pickers. The more specific, the better. Use your warehouse management system (WMS) or track manually if needed.

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When it comes to collecting data, I tend to use the ABC Mantra of Police investigation!

Accept Nothing!
Believe No-one!
Check Everything!

Check the data truly represent the process you are analysing

Visualise It

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Nobody wants to stare at a spreadsheet all day. Turn it into graphs, charts, or dashboards. For example:

Histograms – Use these to show how picking times are distributed. Is most of the work being done quickly, or are there a lot of delays dragging the average down?

Line Charts – Ideal for spotting trends over time. For example, are picking delays increasing on Fridays, or is performance improving week by week?

Bar Charts – Compare picking times for different products or pickers.

Heat Maps – Show where most of the activity happens in the warehouse. (Are your pickers running from one end to the other like headless chickens?)

Pareto Charts – Highlight the biggest issues. For instance, 80% of delays might be caused by 20% of the products being stored too far away.

Box Plots – Also good at helping to see the biggest issues. For example, they are perfect for spotting variability and outliers in operators picking performance. Are some orders taking way longer than the rest? This can help you zoom in on the extremes.

Scatter Plots – Great for showing relationships, like whether longer picking distances lead to more errors or delays. Ideal for connecting the dots (literally) in your data.

Analyse and Act

When you can visualise the data look for patterns and outliers.

Are some orders consistently late?

Is one zone in the warehouse slowing things down?

Find the root cause and fix it (use DMAIC!).

Maybe fast-moving items need to be closer to the packing area (Think TIMWOODS!), or maybe Picker Pete needs a bit of training (or a good talking to).

Tools of the Trade

You don’t need a NASA-level system. Start simple:

Spreadsheets – Excel or Google Sheets work fine for smaller operations.
Warehouse Management Systems (WMS) – If you’ve got one, use it for reports and insights.
Process Maps – Combine your data with a flow of the picking process to see where things clog up.

Quick Wins for Beginners

Focus on one issue to start – like late deliveries or high picking times.
Look for patterns. Are delays worse at certain times of the day or with certain products?
Always ask “why?” at least five times. Why is it late? Why is it stored there? Why didn’t we fix this ages ago?

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Use the 5 Y’s (5 Whys) - Simple but often very effective to get to the real reason a process is not working as well as it could!

Final Word

Process data analysis isn’t just for huge warehouses with robots zipping about. It’s for anyone who wants to improve how things run. So, next time orders are late, don’t panic – grab the data, dive in, and sort it out. A few tweaks here and there can make all the difference.