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Savila Education

Our mission: make the world of supply chain, optimization, and project management easy to understand and fun to learn.

We're all about turning those complex topics into engaging, hands-on experiences that you can actually use in your day-to-day work.

28/07/2026

There is seasonality. Now what?
The next question is whether the seasonal effect stays constant in units or grows with demand.
Additive seasonality fits when the seasonal swing remains roughly the same size.
Multiplicative seasonality fits when the swing grows as the series grows.
The visual pattern gives you the clue.
Performance on unseen data validates the choice.

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23/07/2026

Which one do you track?

A forecast is not finished until you measure its error.

MAE, RMSE, and MAPE reveal different types of misses, so the right metric depends on the decision you need to make.

21/07/2026

If I could pick only one

Dashboards that deliver.

Because a dashboard can look beautiful and still fail.
This book covers the entire process required to build something useful, trusted, and actually adopted.

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06/07/2026

Sources to update your forecast, part 4

Updating a forecast is not enough.
You also need to document every adjustment:

* What changed
* Why you changed it
* By how much
* What information supported the decision

Then, during the next forecast cycle, go back and review it.

Did the adjustment improve the forecast?

Or was it just a guess?

This is how supply chain forecasting becomes a learning process, not a cycle of unexplained changes.

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05/07/2026

Sources to update your forecast, part 3

Earnings calls are another source of market intelligence.

During these calls, executives speak with financial analysts and shareholders about what they expect next:

* Demand by region
* Market growth or slowdown
* Sales outlook
* Risks and disruptions
* Upcoming promotions or strategic changes

You might hear that demand is softening in Europe, growing in China, or that a major disruption could affect the next quarter.

Do not copy their numbers directly into your forecast.
Use them to start a conversation with your team.

Which signals are relevant to us?

When could they affect demand?

Should we increase or decrease our baseline forecast, and by how much?

That is how a statistical forecast becomes a business-aware forecast.

part 4 coming

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29/06/2026

Demand forecasting models to start (this is Day 1)

Each one captures an extra pattern in demand:

1. Naive forecast: next period = latest observed demand
2. Average forecast: stable baseline, ignores recent shifts
3. Simple Exponential Smoothing: recent demand weighs more
4. Holt’s trend method: adds direction (up or down over time)
5. Damped trend: slows the projected trend into the future
6. Holt-Winters: adds seasonality (winter spikes, summer dips)
→ Additive: seasonal bump stays the same size
→ Multiplicative: seasonal bump grows with overall demand
1. ETS models: Error + Trend + Seasonality, statistically modeled with prediction intervals and AIC comparison
Yes, ARIMA, gradient boosting, neural networks and other more complex models exist.

But the foundations stay the same: level, trend, seasonality, and error.

Understand those four, and the complex models stop feeling like magic.

Tomorrow we build all of this in Python.

If you’re a supply chain professional who wants to start forecasting with real logic, this challenge is for you.

Comment “Python” to join the S&OP analytics challenge

June 30-July 2

25/06/2026

3 rules about forecasting every supply chain professional should know:

1️⃣ Forecasts are always wrong 2️⃣ Accuracy drops the further out you go 3️⃣ Aggregated forecasts are more reliable than granular ones

Even with their flaws, forecasts help reduce chaos, guide decisions, and keep operations moving.  Comment “banana” to join our free S&OP Analytics Challenge and build the chain from forecast → production plan → materials schedule.

24/06/2026

3 traits that predict a successful supply chain career.

(Spoiler: not AI)

1. Don’t be that assh*le
Nobody wants them on the team. Nobody follows them as leaders. This is a core human skill.

1. Stay curious
Ask questions. Challenge your thinking. Ditch the status quo.

1. Think big
Build step by step. Focus on impact, not just climbing the ladder. That’s what gives purpose.

Curious + thinking big —> join us at the S&OP analytics challenge, link in bio.

20/06/2026

Your S&OP meetings shouldn’t feel like a battle of opinions.

When the forecast is not backed by analysis, turning it into production, inventory, and materials decisions becomes stressful fast.

That’s why we created the 3-Day S&OP Challenge with Python + AI.

In 75 minutes per day, you’ll build:

Demand Forecast → Forecast Adjustment + Production Plan → BOM + Materials Schedule

Only 100 spots.

Comment “banana” and I’ll send you the link.

14/04/2026

You fix it by building a structured inventory policy — knowing which products deserve attention (ABC analysis), and calculating exactly when to reorder and how much (reorder point + safety stock). Not intuition. A repeatable method.

That’s what the next 3-day challenge covers. Realistic data, real decisions, real output.
Link in bio if you want in.

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