Forecasting Model Design for SMEs

Why forecasting, and why now

Most small and mid-sized businesses already have the data they need to plan better — sales history, orders, cash movements, seasonal patterns — but it sits in spreadsheets and systems that were never built to answer "what happens next." Decisions about inventory, staffing, cash flow, and pricing end up made on gut feel, last year's number plus a guess, or whatever the loudest voice in the room believes.

Raanan's angle is straightforward: use the data the business already has to make the next decision a better one. That means building forecasting models that are accurate enough to trust, simple enough for the team to actually use, and sized correctly for a business that doesn't have a data science department.

What "SME-sized" means here

Enterprise forecasting tools are often too complex, too expensive, or too disconnected from how a small business actually operates. Raanan builds and optimizes models that fit the business as it is:

Built on the data you already collect — no new systems required to get started.
Explainable to the people who will use it, not just to a data team.
Practical to maintain after the engagement ends.
Focused on the decision it needs to support, not forecasting for its own sake.

Where This Applies

Examples of forecasting models built for SMEs

Demand & sales forecasting

Predicting product or service demand by period, region, or channel — to guide inventory levels, purchasing, and staffing instead of reacting to shortages or surplus after the fact.

Cash-flow forecasting

Projecting incoming and outgoing cash across the coming weeks and months, so financing, payment terms, and spending decisions are made ahead of a squeeze — not during one.

Revenue & pipeline forecasting

For service and project-based businesses: turning a sales pipeline or booking pattern into a realistic revenue forecast that supports hiring and investment decisions.

Optimizing an existing model

Many businesses already have a forecast — usually a spreadsheet that has grown unreliable or too fragile to trust. Raanan reviews, tests, and rebuilds these models so the numbers hold up.

Seasonal & workforce planning

Modeling seasonal patterns to plan staffing levels, shifts, and workforce costs ahead of peaks and slow periods, rather than scrambling in real time.

Scenario & sensitivity testing

Stress-testing a forecast against different assumptions — a price change, a new competitor, a slow quarter — so decisions are made with a view of the range of outcomes, not a single number.

How It Works

From raw data to a decision-ready model

1

Assess the data and the decision

Identify what data already exists, what it's missing, and which specific decision the forecast needs to support.
2

Build or optimize the model

Develop a new forecasting model, or rework an existing one, using methods matched to the data and the business — not the most complex tool available.
3

Validate against reality

Test the model against historical outcomes to confirm it's accurate enough to be trusted before it drives a real decision.
4

Implement into the workflow

Fit the model into how the business already plans and decides, so it gets used — not filed away.
5

Hand it over

Train the team to own, update, and trust the model going forward, without depending on outside help to run it.

Have data you're not using yet?

Let's talk about what it could tell you — and what decision it could improve.

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