Imagine a Morning in a Small Café
You own a cozy little bakery. Every morning, you face the same question:

How many buns should I bake today?

• If you bake too many, some buns go stale and end up in the trash. That’s a loss.
• If you bake too few, a customer might walk in, not find their favorite bun, and leave. That’s lost revenue — and maybe even a lost loyal guest.

This is Demand Planning.

Only in big companies, instead of buns, it's shampoo, chocolate, juice, medicine, clothes, or even cars. And instead of one owner — it’s a team of experts, tools, and models trying to guess:

What will the customer want, how much, and where — in the future?

📚 The Scientific Definition Demand Planning is a structured process of forecasting future customer demand (Demand Forecast), which helps companies:

• produce the right amount of products,
• buy raw materials,
• plan logistics,
• coordinate marketing and sales.

🎯 The goal is not just to “guess sales,”
but to ensure balance: no overstock, no shortages, no chaos.

❗ Why Is This So Important?

• If the forecast is too low → products go out of stock, unhappy customers, lost sales.
• If it’s too high → inventory piles up, money is locked in warehouses, lower profits.
• If the forecast is made randomly → chaos, stress, delays, and burnout.

🥐 From Buns to Global BusinessLet’s scale up.

At Nestlé, one of the world’s largest FMCG companies, hundreds of specialists are responsible for Demand Planning.
Each region builds its local forecast — later combined into a global model.

📍 For example:
A factory in Hungary makes chocolate for many countries. The demand forecast in Germany directly affects:

• factory load,
• cocoa purchasing,
• marketing campaigns,
• even the factory staff schedule.

✅ If the forecast is accurate — everything runs like a clock.
❌ If not — tons of chocolate sit in the warehouse, melting into lost money.

👩‍💼 Who Is a Demand Planner?

A Demand Planner is the person who owns the forecast and aligns it with other departments — sales, marketing, finance, production.

They are not just “Excel fortune-tellers”. They are analysts who:

• gather data (sales history, promo, weather, trends),
• build forecast models,
• lead S&OP meetings (Sales & Operations Planning),
• explain to business what numbers can be trusted.

🧠 Glossary (Key Terms to Remember)


🛠 Tools Companies UseYes — everything often starts with Excel.
But as companies grow, they adopt more advanced systems:

• SAP IBP → used by Nestlé, Ritter Sport
• Anaplan → used by Unilever
• o9 → used by Kraft Heinz, General Mills
• Power BI → for data visualization in PepsiCo, Coca-Cola
• Forecast Pro, Gurobi, Python → for advanced forecasting in data science teams

But remember:
Tools are important, but logic is more important.

Even Excel can be powerful if you ask the right questions.

🚫 Myth #1: “Just Forecast Sales — That’s Enough”Many companies think that a forecast is some “magic number” and once you have it — you’re done.
But actually, the forecast is just the beginning. It’s a starting point for discussion:

• Should we run a promotion?
• Do we have enough stock?
• Who is responsible if the forecast was wrong?

These questions are answered during S&OP meetings, where Demand Planners meet with marketing, sales, finance, and supply chain.

📌 Mini Case: Starbucks and Customer BehaviorIn the U.S., Starbucks uses something called behavioral forecasting.
They don’t just look at past sales. They also analyze:

• Weather (hot weather = more cold drinks)
• Seasons (Pumpkin Spice Latte in fall)
• Time & Location (downtown cafés have a spike in the morning)

That’s why the right product shows up at the right time — and it feels like magic. But it’s smart data behind the scenes.


🧾 ConclusionDemand Planning is not just a table of numbers.
It’s the art of seeing the future through data.
It turns chaos into structure, guesses into decisions — and losses into profit.