For distributors and manufacturers, $20M to $200M in sales
Sales are fine. Margin is fine. The credit line keeps climbing, and nobody can see which buying decisions are holding the cash. The decision that hurts is rarely the write-off. It is the purchase approval that made it inevitable.
Find the cash that is already stuck, and test the next commitment before it is signed.
What you get
The cash sitting in inventory, in dollars, measured the same way on day 1 and on day 90, so the change is real.
Which buying decisions put it there, so next year's cash does not go the same way.
The commitment on the desk, tested first: the assumptions named, the week the cash leaves, and what it removes if it is wrong.
Why this works
A bespoke build firm was sure it had a cash problem. It had a sequencing problem: materials were bought weeks before the client payments that covered them had landed. Purchases were mapped against cash, week by week, and re-timed. The loan went early. “We thought we had a cash problem. We had a sequencing problem. Nobody had ever drawn the map.”
At a packaging distributor, the bestseller made 35% of revenue and turned 1.5 times a year. It was the biggest drag on working capital, and the company kept buying more of it. Buying was re-pointed at what turned. Storage cost fell 12%.
The same sequencing problem, tested on one purchase order before it is signed. Open the purchase order planner
Try it yourself
Try it on your own numbers first, free, in your browser. Nothing is sent and nothing is stored. Then see the whole method run on a worked example. Client files stay confidential.
Which product line looks like a strength and is the drag? Revenue, margin and inventory by line in, the cash each holds out.
Free, your own figuresTry itIf I approve this today, which week does it hurt?
Worked exampleOpen itWhen does cash run short, and which decision fixes it?
Worked exampleOpen itWhich buying decisions put $1.95M of a $36M distributor's cash on the shelf? Every step shown, on published data.
Worked exampleOpen itHow it works
Not a question typed into AI. A workflow designed end to end, from the raw exports to the decision, with the same steps and checks every time, so the result repeats, compares month to month, and holds.
Products, purchase orders, sales and stock on hand, from whatever system is in place. 12 months minimum. Messy data is fine. It is the normal starting point.
It cleans and joins the 4 exports. It works out why each purchase was made. It traces every dollar on the shelf back to the order that bought it. It measures what is trapped, ranks it by dollars, and drafts the report. Each step is tested against cases where the answer is already known, and it runs the same way next quarter.
The trapped cash in dollars, the decisions behind it, and the list that changes next month's buying.
Time from the team: One kickoff and one readout.
This is how the diagnostic runs, on my workflow. Building one inside your own business is the AI workflow implementation below.
Why now
Which purchases are still on the shelf, and who approved them? Which lines give the cash back in a month, and which hold it a year? Nobody asks, because answering meant reading every order and every stock line. So the team watches the top 20 items and hopes the other 4,000 behave. An AI workflow reads all of it, the same way each time, before the next buying meeting and again next quarter. Your competitors are still looking at the top 20.
How it is measured
Trapped cash, identified and priced on day 1. The definition is written down, and the baseline is page 1 of the report. This is what I promise.
How much of it has come back, measured on the same items, the same way, at day 90. Your team's actions release it, and I measure it.
Purchases not placed because of the action list. Always labelled as an estimate.
Ways to work on it
Pick the one that fits. Its price, scope and timeline open underneath.
Written findings: Full Diagnostic
The true inventory position, the cash and exposure in dollars, and a plan ranked by cash released: a 10 to 15 page report with a one-page summary for a board or a bank. Not included: doing the fixes. That is the AI workflow implementation, quoted once the size is known.
How it runs
What it takes
Messy data is the normal starting point. The work runs on the data as provided, screenshots included, and the report states what any gap does to the answer. The data is used for this work only, never shared, never used as an example, and deleted on request.
Written findings: Focused Diagnostic
One purchase or one decision, named on the order. A 3 to 5 page report: whether the numbers behind it hold, what it costs in dollars if they do not, and the first fix. Plus a readout. Not included: a second decision, or doing the fix.
How it runs
What it takes
Messy data is the normal starting point. The work runs on the data as provided, screenshots included, and the report states what any gap does to the answer. The data is used for this work only, never shared, never used as an example, and deleted on request.
Built and handed over: AI workflow implementation
One deliverable: a working AI workflow inside the business, run by its own team. One workflow, named at the start.
Scope and timeline
In scope: one workflow, named at the start, such as the inventory close, the buying review or the monthly report, plus the handoffs it depends on. Anything beyond that workflow gets its own quote. Most of the 3 months goes to mapping how the work really gets done, with your people, so the AI fits reality.
Coached: AI workflow build sessions
Live coaching while the team builds, on its own data, in its own Claude. One session unblocks one thing. 4 sessions build the whole workflow.
She reads financial reports quickly and spots patterns, opportunities, and potential issues that others might miss, turning insights into actionable strategies. Her ability to notice hidden details and identify root causes instead of just surface issues makes her invaluable.
Charles TuCEO, technology manufacturer, TaiwanSharon is an insightful, kind, and hyper-detailed analyst. She quickly recognizes core issues and provides a clear path to the answer ... she has developed a powerful intuition and sense of clarity in a very difficult subject matter: cash and inventory optimization.
Mark LuptonCFO and advisor to 8-figure companies. Founder, Greenhouse Business AdvisorsSharon is knowledgeable and approaches a situation from a real-world perspective.
She doesn't critique outputs, she diagnoses the system behind them: backtesting, train/test splits, auditability. That's the rigor senior finance professionals actually need.
Albert LeeFounder, FP&A advisory practice. FCPAShe brings positive energy, strong subject matter expertise, and practical AI insights to every interaction. Her feedback was always constructive and actionable, helping participants elevate their thinking.
Public reviews from senior finance people who have worked with Sharon. Roles are their own.
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