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Supply Chain Intelligence

Protect service without cash tied up in the wrong stock.

Supply chain and inventory optimization positions stock where demand is heading, by SKU and location, so you hold service levels while freeing the working capital frozen in stock nobody needs. 

What leaders decide with half the picture

The calls that decide working capital and service.

The data to make these calls usually exists, scattered across ERP, the WMS, sales, and the forecast, so the answer only assembles itself in hindsight. The ones that suffer:

01

Which DC gets the constrained SKU when two of them are short?

02

Reorder now or wait for the demand signal to firm up?

03

Is that slow mover dead stock or just between orders?

04

Expedite the shipment or hold the cash?

None of these is a forecasting question. What we close is the gap between knowing what's coming and moving on it in time, the window where working capital and service levels are won or lost.

successful leaders ask why

A multimillion-dollar inventory call deserves a recommendation you can question.

Every inventory move and risk flag carries the signals behind it and a confidence range, so your planner defends the call to purchasing or finance on the evidence, not on the tool's say-so.

The bigger the number, the more that matters. No one should move that much cash on faith.

What it does

Move stock toward where demand is going, not where it went.

Mined XAI turns the forecast into inventory decisions at the SKU-location level: what to hold, where, when to reorder, and what to move first.
01

Right-size inventory by location

Set targets, reorder points, and safety stock based on 90-day demand for each DC and channel, so coverage follows the signal instead of trailing it. Less cash frozen in stock nobody needs, fewer gaps where demand is climbing.

02

Prevent the stockout before the window closes

Rank service-level and understock risk early, while there's still time to reposition or expedite, so a shortage gets caught before a customer feels it.

03

Size and balance the network

When a DC decision is on the table, model where to place it and how to rebalance flow across the whole network at once — the multi-echelon picture — with the operational reason behind each option. Not a map with a pin in it.

DC placement article
04

Rank the next move, with its cost

A short list of where to act first, what it's worth if you move, and what it costs to wait, in the order your team should work it. 

See it in action
Next moves, rankedRisk × value
1
Expedite 40118 → Midwest DC
Stockout in 4 days
$1.2M
High
2
Rebalance 22907 · SE → MW
Demand shifting north
$640K
Med
3
Hold PO 31845
90 days cover already
$310K
Low
Inventory by locationDays of cover
Midwest DC
4 days
Southeast DC
21 days
Northeast DC
96 days
West DC
38 days
UnderstockOverstock
Network flow5 DCs
Midwest · low Hub
Active rerouteAt-risk DC

Why we see what others miss

The signal your forecasting tool flattens out.

Most systems run on transaction history and linear assumptions, projecting the past forward in a straight line. The problem: real demand doesn't move in straight lines. Mined XAI's Engine reads the shape of your data: which items move together and which customer behavior leads a regional shift by weeks.

Our Deep Topological Modeling uncovers the shape of your data and relationships that live below the surface of standard statistical approaches. We see structures that regression and neural networks miss entirely.

Start with the guide:Explainable AI for Supply Chain→
Deep Topological Modeling
Deep Topological Modeling visualization
Demand concentration. Dot color represents demand magnitude with red showing the highest-volume activity.
Patterns reveal whether demand occurs monthly, every other month or intermittently—helping establish the appropriate reorder cycles.
Shared demand pattern. The central pillar represents the dominant demand behavior common across the network creating a baseline for meaningful deviations.
Regional divergence. Each arm represents a location, market, or customer segment behaving differently from the network pattern, signaling where inventory positioning may need to change.
The payoff

One operating picture for planning, purchasing, and sales.

Demand, inventory, and service-level risk sit in one place the whole team can read and question, so planning, purchasing, and sales stop working three versions of the same week.

Once the picture shows why a number moved, the conversation turns practical fast: not whose figure is right, but which decision to make first and what it's worth. 

See the evidence

Start with one inventory call. See it proven in 30 to 60 days.

You don't need a network-wide rollout to trust this. Pick one inventory decision where timing and confidence matter — a category, a region, or one account that keeps missing — and judge it on results you can quantify.

Talk to us
Day 1

Pick the area

We connect the relevant data and set a baseline you can measure against.

Day 30

First results

The model runs against that baseline, and you see the moves and the reasoning on your own data.

Day 60

Ranked actions

Ranked moves with the drivers attached, measured impact, and a practical path to widen it.

By definition

What is supply chain optimization?

Supply chain optimization is the practice of positioning inventory, capacity, and replenishment across a network so you meet service targets at the lowest working capital and cost. Inventory optimization is its core: setting stock levels, reorder points, and safety stock by SKU and location against expected demand, rather than by blanket rules.

Answered

Supply chain intelligence FAQs.

Practical answers about adoption, integration, and how the recommendations hold up under real operating conditions.

Still have questions?Talk to us→
What is multi-echelon inventory optimization?
Multi-echelon inventory optimization sets stock levels across every stage of a network together, DCs, branches, and channels, so inventory is balanced between locations rather than optimized at each one in isolation. It prevents the common failure where one site overstocks while another starves the same SKU.
How do you get procurement teams to actually use the recommendations?
The platform is human-in-the-loop by design. A buyer can query any recommendation to see what data drove it, its boundaries, and which signals moved it, then weigh that against their own read of the account. Involving users early and tracking ROI as you go is what turns a recommendation into a habit.
Will it integrate with our ERP, WMS, and inventory systems?
Yes. It connects through secure APIs and data connectors, across cloud and on-prem, and delivers its recommendations inside the tools your team already uses rather than replacing them.
We need to add a distribution center. Can you tell us where to put it?
Yes. From your current operations, your network, and the demand forecast, we recommend locations that cut delivery time and keep product closer to the end customer, with the data behind each option and tuned to your goals for the new DC.
Our demand is highly seasonal and weather-dependent. Does the model account for that?
Yes, and several of our customers look exactly like that. The model weighs seasonality and other outside signals, and where a weather feed improves the forecast, we pull it in.
We're in a regulated sector that's wary of AI. Can you work here?
Yes. The methods were built for transparency and inspectability from the start, so even complex models produce reasoning you can trace and question, the paper trail you need to justify a model for compliance.
How is this different from the inventory module in our ERP?
An ERP module executes the rules you give it. Mined XAI decides what those rules should be, at the SKU-location level and from where demand is heading, and shows the reasoning behind each target. It sits on top of the ERP, not in place of it.
LET’S TALK

Bring us one decision you're about to make on partial information.

A replenishment call you're second-guessing or a slow-moving SKU you can't explain. Send it over, and we'll show you what your data already knows and the reasoning behind it.

Schedule a discovery call