Advisory for distributors and manufacturers
Buying, pricing and production decisions get made every week. The report that should guide them lands 2 weeks after close, because the inventory system and the books were never connected and every line has to be tagged by hand first. By then the money is already committed, on margin by product line that is still a guess.
One clean report, days after close and not weeks, so this month's decisions are made on this month's numbers.
What you get
Margin by product line that holds, because raw material and finished goods are finally separate. Pricing and buying stand on it.
The report lands before the month's purchasing is committed, not 2 weeks after it. The 3 days of tagging rows go away with it.
The method lives in the workflow, not in one person's head. The team runs it, and it keeps running when people change.
Why this works
A furniture maker with 8 product lines and month-end done in-house. Profit looked fine and cash never did, and by the time the pack arrived the purchasing decisions it should have informed were already made. This is the same month on one screen: $264,590 sitting still for 180 days, the exceptions ranked, every figure traceable to the transactions behind it.
How it works
Anyone can ask AI a question. This is a workflow designed end to end, from the raw exports to the decision: the same steps, the same rules and the same checks every time, so the result can be repeated, compared month to month, and defended.
Which systems, which exports, who tags what. 1 call.
It pulls the exports, cleans them, classifies the items, separates raw material from finished goods, matches names across both systems, runs the checks and builds the report. The rules are written once, the team can read and change them, and the manual days go away.
Built, tested and handed over. The company owns the report and the process.
Time from the team: 2 calls, and one person who knows where the exports come from.
This is how I run a diagnostic, with my own workflow, to get you the answer. Building a workflow inside your own business is the AI workflow implementation below.
Why now
The report was never late because the math was hard. It was late because someone had to prepare the data by hand first, and that preparation needed judgment, so no spreadsheet or system could do it. An AI workflow now does that preparation the way a careful person would, in minutes, the same way every month. The company that sees its real margin by product line days after close commits its cash with better information than the one still waiting for the pack. Built once, it moves every decision after it earlier.
Ways to work on it
Pick the one that fits. Its price, scope and timeline open underneath.
Built and handed over: The report build
The business provides the numbers and says what the report has to show. How the business produces those numbers does not change. The report is designed and built as one file the team can open anywhere, with 3 rounds of revisions. The exact price is fixed in writing before any work starts. Making it refresh by itself every month is automation, and that is the AI workflow implementation.
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.
Sharon 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 Custer helps businesses maximize their cash flow through scenario planning and forecasting utilizing POs, inventory, cash, and cash flow assumptions. She has helped make this process turnkey with AI powered tools such as custom agents, dashboards, and monthly workflows and automations.
Eve ReiterDigital Solutions Architect and Lead Advisor, Procurement, NTT DATAShe has a remarkable depth of understanding regarding inventory analysis, controls, and optimization. Sharon also understands how inventory translates to cash and cash flow.
David SafeerArchitect of Cash-First Operating Systems. Founder, Cash is ClearSharon is knowledgeable and approaches a situation from a real-world perspective.
Sharon has deep expertise in AI and she teaches it with ease, giving students tips without giving a complete solution from the start. This approach motivated me extremely to come up with my own solution.
She provided thorough, insightful feedback on our projects and demonstrated deep expertise in the subject matter. Highly valuable, helping me improve both my work and my understanding of the concepts.
Start here
Free, if you want to look first
