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How Do You Measure Warehouse Travel Waste?

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Muhammad Mudassir

Muhammad Mudassir

Founder & CEO, Cognilium AI

A diagram titled "Two walks, one order." Two schematic floor plans hold the same single order of five numbered items and the same pack station. On the left, "today’s placement," the five items are scattered across four aisles and the walked path is long. On the right, "placed from the order history," the same five items are clustered near the pack station and the path is short. A note reads that the gap between the two paths is the travel waste, and that you rebuild it from a year of orders rather than time it with a stopwatch.

TL;DR

Everyone quotes the same figure, that about half of picking is walking, and it traces to a single 1996 book. You do not need it. You measure your own building by rebuilding a year of real orders against your current layout, then against a better one, and taking the gap. Your ERP will not do this for you, and the answer lands in lines per person hour.

The short answer

You measure warehouse travel waste by rebuilding the trips your orders actually required. Take a year of order history and the record of where each item is stored, and for every order work out the path a picker had to walk to collect it. Add those paths up and you have what your current layout costs you in travel. Then work out the same orders against a better placement, add those up too, and the difference between the two totals is the waste you can actually recover.

You do not need a stopwatch and you do not need to guess. The evidence is already sitting in your order history. The rest of this article is why that works, where the famous "half your time is walking" number really comes from, why the metric you are judged on cannot see any of this, and the one decision you have to make before the arithmetic means anything.

The number everyone quotes, and where it actually comes from

If you have spent any time around warehousing you have heard some version of this: about half of a picker’s day is walking, not picking. It gets repeated in vendor decks, conference talks and posts, almost always as "studies show" or "research indicates." It is the single most quoted fact in the field.

So we went and found where it comes from, because a number you are going to build a business case on should have a source you can name.

It traces to one place. Bartholdi and Hackman’s Warehouse & Distribution Science, a free textbook out of Georgia Tech and the standard academic reference, breaks a picker’s time down like this in section 3.3:

Traveling 55%. Searching 15%. Extracting 10%. Paperwork and other activities 20%.

And just above that table it says something people quote less often but that matters just as much:

Order-picking typically accounts for about 55% of warehouse operating costs

So there are two 55% figures stacked on top of each other. Order-picking is about 55% of what the warehouse costs to run, and within order-picking, travel is about 55% of the time. Put them together and travel is the largest single slice of the largest single cost in the building. That is why it is worth measuring at all.

Here is the part almost nobody mentions. Both of those figures in the textbook carry the same little reference number, [21]. Follow it to the back of the book and [21] is World-Class Warehousing by Edward Frazelle, published in 1996 by Logistics Resources International. Logistics Resources International was Frazelle’s own consultancy. The book is, in practice, self-published. There is no sample size, no methodology, no description of how the number was arrived at, anywhere in the citation chain. The academic text is careful and trustworthy, but on this specific figure it is passing along a number from a 1996 practitioner book, and everyone quoting "studies show" is quoting that one book without knowing it.

We are not telling you to throw the number out. It is probably in the right neighbourhood, and the textbook authors, who are careful people, were comfortable citing it. We are telling you that "about half" is honest and "studies show" is not, and that the difference is exactly the kind of thing this audience notices. More to the point: if the number that justifies the whole exercise is a single unverified figure from thirty years ago, you do not want to run your own business case on somebody’s slide. You want to measure your own building. Which brings us to the awkward fact underneath all of this.

The number you are actually judged on cannot see travel at all

Walk into an operation and ask the person who runs picking what their number is. They will not say a travel-time figure. They almost certainly do not have one. They will tell you lines picked per person hour, or orders out the door before the cut-off, and that is the number their own boss looks at.

This is not an accident. WERC’s DC Measures is the benchmarking study warehouse professionals actually report against, the one that lets a distribution centre compare itself to its peers. It tracks thirty-six metrics. Not one of them is travel time. The picking productivity metrics it does track are Lines Picked and Shipped per Person Hour, Orders Picked and Shipped per Person Hour, and Order Picking Accuracy. Travel does not appear anywhere in that list.

Sit with how strange that is. Travel is the biggest component of the biggest cost in the building, and the standard scorecard the trade grades itself on does not contain it. It is not that people decided travel does not matter. It is that travel is genuinely hard to see. It never shows up as its own line. It hides inside the lines-per-hour number as a slightly lower figure than it should be, quarter after quarter, and nothing on the report tells you how much of that gap is walking.

The good news is that the two numbers are the same number wearing different clothes. Bartholdi and Hackman make the connection directly in section 3.3, and it is the most useful sentence in the chapter:

since travel is the largest labor cost in a typical warehouse, the number of pick-lines is an indication of the labor required

Every line on a pick list is a location somebody has to walk to. So the labour a day of orders demands is, in large part, the total walking those orders imply. Cut the walking and the same crew clears more lines in the same hours. Travel waste and lines per person hour are not two problems. They are one problem, and lines per person hour is the side of it that lands on the scorecard. That is the unit any measurement has to end in, or the person you are talking to cannot use it.

Why you cannot just go and time it

The obvious response is to measure travel directly. Follow some pickers with a stopwatch, or read the timestamps off the handheld scanners, and see how long the walking takes.

It does not work, for a plain reason. A stopwatch on one picker on one shift tells you about that picker on that shift. It cannot tell you whether the layout is good, because you have nothing to compare it to. The picker walked as far as this layout made them walk. To know whether that was too far, you would have to know how far a better layout would have made them walk for the exact same orders, and no amount of standing on the floor with a clipboard gives you that. You would be measuring the symptom and calling it the diagnosis.

There is a scale problem on top of it. A real operation has thousands of active items and thousands of orders a week. The waste is not in one dramatic long walk you could spot by watching. It is spread in small increments across every order, a few extra metres here because two items that ship together live in different aisles, a few there because a fast mover ended up near the back. You cannot see a few extra metres. You can only see it when you add up a year of it. The floor is the wrong instrument. The order history is the right one.

How you actually measure it

You reconstruct the trips. Here is the method in full, and it is deliberately not a secret, because the method has been public since the 1960s and the thing that is actually scarce is anyone bothering to run it on a real company’s own data.

You need two things, both of which every ERP and warehouse system already holds:

  • A year of order lines. Order number, item, date. Each order is a shopping list, and each line on it is a location that had to be visited. A year rather than a quarter, because demand has seasons and a three-month window cannot see them.
  • A location master. Which item currently lives in which location, and where those locations physically sit in the building.

Now put them together. Take one historical order. It touched, say, six items. Look up where those six items were stored. That set of locations, walked in the sequence a picker would actually take, is a trip with a length. Compute that length. Do it for the next order, and the next, for a year of them, and add the lengths up. That total is what your current placement cost you in travel over the year. It is not an estimate borrowed from a survey. It is your own orders against your own layout.

Then you do it a second time. Take the same orders, but this time look up where each item should live under a better placement, one that puts the items that ship together near each other and the fast movers near the packing area. Recompute every trip. Add those up. This second total is what the same year of demand would have cost if the layout had been right.

The gap between the two totals is the travel waste, and it is the only honest way to state it, because it is measured against your real orders rather than against a benchmark from somebody else’s building. It is also, not by coincidence, exactly the number that turns into lines per person hour: the walking you remove is capacity you hand back to the same crew.

The one decision you have to make first: how the picker walks

There is a step in "compute that trip’s length" that quietly does a lot of work, and it is worth being honest about it, because it is where naive versions of this go wrong.

A set of locations is not yet a trip. To turn locations into a distance you have to decide the path the picker takes between them, and there is more than one sensible path. Bartholdi and Hackman spend a whole chapter on this, Chapter 10, and its title is the entire point: "Routing to reduce travel." At each aisle, they note, a picker really has only two options:

Travel all the way through the current aisle, picking as necessary; or else enter the aisle only as far as necessary to pick all required items therein and then return to the same end of the aisle from which it was entered.

The first is the traverse. The second is the enter-and-return. Which one is shorter depends on where in the aisle the items sit, and a good picker, or a good warehouse system, chooses per aisle. The book also shows that the tidy-looking snake path, the serpentine that walks up one aisle and down the next in order, can result in unnecessary travel, because it forces the picker down aisles they only needed to dip into.

Why does this matter for measurement? Because if you reconstruct trips using a different routing rule than the one your floor actually follows, your baseline is wrong before you start, and every saving you claim afterwards is measured off a fiction. So the first thing an honest measurement does is match the routing rule your operation genuinely uses, so the reconstructed baseline is the real one.

And it draws a line that gets blurred constantly in this industry. Routing and slotting are not the same lever. Routing, the whole subject of that chapter, makes a given trip shorter by walking the locations you have to visit in a smarter order. Slotting changes which locations you have to visit at all, by moving the items. Routing improves the trip. Good placement removes it. When you measure the waste properly, you compute the current-layout trip and the better-layout trip using the same routing rule for both, so that what you are left with is purely the cost of placement, not a routing trick smuggled in to flatter the number.

Why your ERP will not hand you this figure

A fair question at this point: if the order history and the location master both live in the system already, why does the system not just tell you? You are running Dynamics 365 or SAP or an equivalent, and it knows every order and every location. Why is this not a report?

Because it was never its job, and that is not a flaw. A system of record executes. You told it the brake pads live in aisle 14, so it sends people to aisle 14, efficiently and without complaint, and it records that they did. It will happily give you your lines per person hour, because that is a thing that happened and it logs things that happen. What it does not do, and was never built to do, is imagine a different building. Measuring travel waste means computing a counterfactual, the trips those same orders would have taken under a placement that does not exist yet. Nothing in an ERP’s job description involves re-deriving where inventory should have been from a year of its own history and comparing that to reality. Expecting it to is a category error. The analysis sits on top of the system, reads its history, and hands the answer back. It replaces nothing.

This is the same shape as a point we make about placement itself. The system will faithfully execute whatever layout a human gave it, and it will never question that layout, because questioning it was not the assignment. The gap between what the system executes and what the history says it should execute is precisely the thing worth measuring.

What the number is actually worth

We are going to disappoint anyone who wants a percentage here, and the reason is the whole point of the exercise.

We will not tell you that operations "typically" save some tidy fraction, because we would be doing exactly the thing this article opened by criticising: passing along a number with no connection to your building. We have measured your travel waste in zero warehouses until we have measured it in yours. Anyone quoting you a savings figure before they have seen your data is quoting you somebody else’s data, and you should treat it the way you would now treat "studies show."

What we will tell you is the unit the answer comes in, because that is what makes it usable. The waste, once measured, converts straight into lines per person hour, the number already on your scorecard. Every metre of walking you remove is time the same crew spends picking instead, which is more lines out the door before the cut-off with the headcount you already have. That is the version of the result an operations director can take to their own boss, because it is stated in the number they are both already judged on. The travel figure is how you find the money. Lines per person hour is how you bank it.

And the honest sequence, the one belief this whole company is built on, is measure before you move. If you cannot say what the current layout costs, you cannot prove you improved it, you cannot rank which moves are worth the disruption, and you have nothing to point at when someone asks a year later whether it worked. The measurement is not the boring bit you do before the real work. The measurement is the thing that makes the real work provable.

The short version

The famous "half your time is walking" number is real enough but traces to a single 1996 book, so measure your own building rather than trust the slide. You cannot measure travel with a stopwatch, because the floor cannot show you the layout you did not build. You measure it by reconstructing a year of real orders against your current placement, then against a better one, using the routing rule your floor actually follows, and taking the gap. Your ERP will not do this for you, because imagining a different building was never its job. And you state the answer in lines per person hour, because that is the only number the person paying for the work is actually graded on.

Before any of this is worth doing, the location data underneath it has to be trustworthy, because optimising on wrong locations is a confident wrong answer. That is its own subject: Why is my inventory location data wrong, and why does it matter?.

For the exact data a measurement needs, and the net-of-restock economics behind a good target quantity, read What data do you need to optimize warehouse picking?.

For the full ground-up treatment of the problem this sits inside, read Warehouse Pickup Optimization: The Operator’s Guide.

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Muhammad Mudassir

Muhammad Mudassir

Founder & CEO, Cognilium AI | 10+ years

Mudassir Marwat is the Founder & CEO of Cognilium AI. He has shipped 100+ production AI systems acro...

Founder & CEO of Cognilium AI; 50+ projects delivered with 96% client satisfaction; 4 production AI products built and operated; multi-cloud AI architecture (AWSGCPAzure)
Agentic AIRAG → GraphRAG retrievalVoice AIMulti-Agent Orchestration

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