Can Dynamics 365 Optimize Inventory Placement? What Warehouse Slotting Actually Reads

Dynamics 365 Supply Chain Management will execute a placement policy reliably and indefinitely, but it will not derive one for you. Its Warehouse slotting feature consolidates demand from open orders rather than from shipment history, so nothing in the product reads what you have already shipped and tells you that your pick faces are in the wrong places.

That distinction decides whether you need anything beyond the ERP. Everything below was read from Microsoft’s own documentation and release planner on 20 July 2026, and you can check all of it yourself in under an hour.

What the Warehouse slotting feature actually does

Dynamics 365 ships a feature named Warehouse slotting. Because of the name, most people assume it does what the warehousing industry means by slotting, which is deciding which storage location each product should occupy.

The section introduction sets the frame:

Several warehouse slotting features help warehouse managers intelligently plan picking locations before they release orders to the warehouse and create picking work.

Note "before they release orders to the warehouse". That is the tell, and the detail sits in the next sentence:

The Warehouse slotting feature lets you consolidate demand by item and unit of measure from orders that have a status of Ordered, Reserved, or Released. You can apply generated demand to locations used for picking, based on quantity, unit physical dimensions, fixed locations, and more. After the slotting plan is established, you can create replenishment work to bring the appropriate amount of inventory to each location.

Read the three order statuses. Ordered, Reserved and Released are all states of live demand: orders that exist and have not shipped yet. None of them is history. Nothing in that list is what you shipped last March.

Then read what the assignment runs on. Quantity, unit physical dimensions, fixed locations. Those are capacity questions. They answer how much fits where, not what should go where.

Then read the last sentence. The output is replenishment work. The feature is a planner that gets stock to the faces you already defined, and it is genuinely useful at that job. What it does not do is read a year of order lines and tell you the faces were assigned to the wrong items in the first place.

One caution in the interest of accuracy: Microsoft’s sentence ends with "and more", which is a hedge. The list above is what is stated, not necessarily everything the feature considers. There is also a companion feature, Warehouse slotting allocation enhancements, which adds an option letting the system consider existing on hand inventory at a target location. That is a capacity refinement, and it does not change what the demand side reads.

The complete 2026 wave 1 warehouse feature list

This is the part most write ups skip. Here is every warehouse management feature in the 2026 release wave 1 for Supply Chain Management, enumerated from the release planner on 20 July 2026, with its status:

• Improve database health by archiving load data. GA September 2026.
• Enhance warehouse efficiency with dynamic item placement. Preview and GA June 2026.
• Automate dynamic work classification with Power FX. GA June 2026.
• Enable precise serial and batch capture in cluster picking. GA June 2026.
• Streamline transfer order receiving in warehouse order management with ASNs. GA June 2026.
• Improve picking efficiency with wrist mounted scanning devices. GA June 2026.
• Warehouse Management mobile app V4. Preview August 2025.
• Capture worker IDs for warehouse packing events. GA July 2025.

Eight features. Archiving, inbound placement policy, a rules engine, batch capture, receiving, hardware, a mobile app and an audit field. There is no slotting optimisation engine in that list, and nothing in it derives placement from order history.

What the announcement said, and what the feature list contains

Microsoft’s 2026 release wave 1 announcement, dated 18 March 2026, said warehousing gains AI powered picking, inventory rebalancing, and hands free scanning. Three phrases. Map each one onto the list above and the picture sharpens.

• Inventory rebalancing maps to dynamic item placement, which describes smarter inventory balancing during inbound processes. That is putaway as stock arrives. It is not re slotting from order history.
• Hands free scanning maps cleanly to the wrist mounted scanning devices and the mobile app.
• AI powered picking has no matching feature in the warehouse list. The nearest candidates are wrist mounted scanners, which are hardware, and Power FX work classification, which is a rules engine you write the rules for.

The announcement language is more expansive than the feature list underneath it. That is a normal gap between marketing and a release planner, and it is worth knowing about before you build a roadmap on the headline. Check the release planner rather than the blog post, including this one.

None of this makes the product deficient. It makes it scoped, and the scope is stated plainly in Microsoft’s own text. The 2026 wave 2 plan was not published as of 20 July 2026, so everything here carries a date and gets re checked each wave.

What "optimal" means inside a location directive

Location directives are how Dynamics 365 decides where work goes, and the documentation uses the word optimal. It does not mean what a warehouse manager assumes.

The Strategy field on a directive action offers ten options plus a default of None:

• None, the default
• Match packing quantity
• Consolidate
• Consolidate including incoming work
• FEFO batch reservation
• Round up to the full LP and FEFO batch
• Round up to a full LP
• License plate guided
• Empty location with no incoming work
• Location aging FIFO
• Location aging LIFO

Packing quantity, consolidation, batch rotation, license plates, emptiness and age. Not one is based on distance, travel or velocity. When the documentation says a directive finds the optimal location, it means the location that satisfies your constraints, not the location that minimises walking. Which strategies are available also varies by work order type, so treat the list as what exists rather than what applies everywhere.

Warehouse spatial location, and why it is better than I expected

Warehouse spatial location is a preview feature that assigns X, Y and Z coordinates to locations and adds a step that sorts picking work by distance. I went in expecting a crude model and did not find one.

Two calculation strategies are offered, and both formulas are published. Straight line, for open layouts:

distance = √( (x₂−x₁)² + (y₂−y₁)² + (|z₂|+|z₁|)² )

And city block, for aisle based layouts:

distance = |x₂−x₁| + |y₂−y₁| + |z₂| + |z₁|

Look at how height is handled. The vertical component is the sum of the absolute heights of both locations, not the difference. On first reading that looks wrong. Two bins side by side on level two score a vertical cost of four, when the distance between them is zero.

It is not wrong. The picker comes down from level two and goes back up to level two, so descent plus ascent is the real travel. Model it as a difference instead and a pick at level five costs the same as one at floor level, which is obviously false. Somebody there is modelling reach time rather than geometry, in a preview feature. That is more thought than I assumed.

What it still does not do matters more. There is no topology, so racks, walls, one way aisles and congestion are not represented. There is no way to derive coordinates from a layout, so somebody has to produce the numbers before they can be imported. And it sorts work lines after the work has been created. It sorts the trip. It does not decide where things live. Microsoft labels it a preview feature and states it is not intended for production use.

Sorting a route through badly placed inventory is a different problem from placing inventory well. This solves the first one, and from what is published it solves it properly.

Dynamic item placement, read closely

Dynamic item placement reached general availability in June 2026. Here is the feature details paragraph in full, so nothing is trimmed:

Warehouse and item storage policies are now available, giving you dynamic control over where and how much inventory is stored. Define preferred storage locations and target quantities for each item, either by editing directly or importing data at scale. The system intelligently balances inventory during inbound processes, ensuring optimal placement for fast fulfillment. Warehouse spatial locations further optimize routes, cutting travel time and boosting picking speed.

Read sentence two next to sentence three. Two: you define the preferred storage location. Three: the system ensures optimal placement. Both are true, and they describe different jobs. You supply the policy. The system then balances inventory against that policy during inbound so the right quantities land where you said.

Optimal placement means optimal against your definition. The system will maintain that definition faithfully, forever, including if it was wrong the day it was made. Automated creation of location directives and work templates ships in the same release, and anyone who has hand built those knows what that is worth.

The objection: you can just configure velocity rules

This is the reasonable objection and it deserves a direct answer.

You can build a slotting template. You can write a query that filters on almost anything in the data model. You can configure a fixed location for every item in the warehouse.

What the system will not do is compute the ranking from your order history, or decide the assignment. It executes a policy that a person worked out somewhere else. The configuration is the delivery mechanism. It is not the analysis.

Your ABC classification is probably ranking the wrong variable

Here is the part that costs most operations the most, and it is not a Dynamics problem. It is an everywhere problem.

Bartholdi and Hackman, in the Georgia Tech warehouse science text, are blunt about it:

It is a popular misconception that an ABC analysis refers exclusively to the ranking of skus by dollar-volume. Dollar-volume will be of little interest to us.

Nobody walks further for an expensive item. They walk because it gets picked often. Those are different rankings and they disagree more than people expect.

Finance and Operations will classify items on value, on margin, or on carrying cost. All three are money. None of them is picks. Ranking by pick frequency is not a parameter you tick, it is a query somebody has to write, which is probably why most sites never have.

You can run the comparison this week and it costs nothing. Pull picks per item for twelve months, rank by that, and put the result next to your current A items. Anything high on one list and low on the other is worth looking at.

What a placement recommendation has to survive

Velocity ranking on its own is the beginner move, and it is worth saying so plainly before anyone acts on the previous section.

The first test is replenishment. Every item you pull forward has to be restocked more often, and a restock is more expensive than a pick. Bartholdi and Hackman put it directly: some items could positively hurt efficiency if they were stored in the forward pick area in less than their maximum amounts, and if too little is stored, restock costs consume any pick savings.

Ron Grove of TZA states the test in the form an operator would use:

A good package won't recommend a move that costs $4 but only saves $3 in picking labor.

So a slotting output that counts only pick savings is not a recommendation. It is half an arithmetic problem. If someone shows you one, ask where restock is netted, and treat a vague answer as a decorative number.

The second test is congestion. Concentrate the fast movers and you concentrate the people picking them. Bartholdi and Hackman name two kinds: interference at a location, where two pickers want the same small area, and interference in an aisle, where one cannot get past the other. Splitting a fast mover across several faces is the usual answer, and it tends to move the queue somewhere else rather than remove it.

The four constraints that outrank velocity

If you run a case pick operation, none of the above is your first constraint, and any recommendation that ignores this is unusable on your floor.

In a grocery or foodservice distribution centre the slot sequence is driven first by the pallet the selector has to build:

• Crushability. Crushables get picked last so they ride on top, which puts them at the end of the route regardless of how fast they move.
• Temperature zone, which cannot be crossed at any velocity.
• Hazmat segregation, which is not negotiable by anyone.
• Family grouping, so the store crew can put the delivery away without crossing the floor twice.

Pick frequency lands somewhere around fourth. Everything written above about co-occurrence and travel is a constraint that gets applied inside those, not instead of them. A slotting recommendation that ignores crush sequence is not ambitious, it is unusable, and the selector will ignore it and be right to.

That is a real limit on the approach, and it is better said out loud than discovered in a pilot.

How to check this in your own building

Three things, none of which need a vendor.

First, ask whoever owns your warehouse configuration one question: do you use the Warehouse slotting feature, and which demand strategy, Ordered, Reserved or Released? If they answer, you are doing replenishment planning, which is useful and is not what most people assume the feature is. If they cannot answer, nobody has looked at this, and that is worth knowing too.

Second, run the ABC comparison above. Picks per item for twelve months against your current classification.

Third, take the two items your operation ships together most often and walk between them. If that walk is long, no routing setting will fix it, because the trip should not exist.

What has to happen instead

The evidence you need is already in your system. Every order you have shipped records which items get picked together and how often each one is touched. That history is what determines where things should live, and it is the one input the slotting feature does not consume.

The work is unglamorous: read the history, rank items by how often they are actually picked rather than by what they are worth, find the pairs that ship together and sit far apart, net the result against the replenishment labour each move creates, apply the constraints that outrank velocity, then write the outcome back as maintained master data. Nothing new appears on the floor and nobody has to learn a new screen.

Judge it in the number your operation is already measured on. Not distance, and not travel time, which does not appear anywhere in the WERC DC Measures benchmark. Lines picked and shipped per person hour, which does.

In most picking operations, roughly half of the time goes on walking rather than picking. That figure traces to Frazelle’s book of 1996, which is the source most of the industry is quoting when it says travel dominates picking time. We have not found a methodology disclosed anywhere in the citation chain, and we would rather say so than repeat it as though it were settled.

This article is one part of a larger picture. For the ground up treatment of the whole problem, read Warehouse Pickup Optimization: The Operator’s Guide.