Top 5 Libraries for Demand Forecasting for Every Demand Type

Today we dive deeper into our Demand Forecasting series. You'll discover:

• Which Demand Types exist

• Which 5 Python/R Libraries are great for Demand Forecasting for Each Demand Type!

▶️BONUS: Grab a complete Autogluon Python notebook by filling out a quick survey here.

Disclaimer:

Our content, brought to you by SCM Sync, is purely for educational purposes and should not be taken as professional investment or supply chain advice. We encourage you to review our full disclaimer before diving in.

“We can only see a short distance ahead, but we can see plenty there that needs to be done.” - Alan Turing

Top 5 Libraries for Demand Forecasting for Every Demand Type

Navigating the unpredictable world of demand forecasting requires the right tools—think of it as choosing the right kind of coffee for your morning routine.

For a deep dive why different demand types exist of a product's lifecycle please refer to this LinkedIn Post here. Each type has its unique flavor, and here's the brew that fits every demand pattern:

With that said, here are 5 libraries that you should start using for demand forecasting today! 👊

1. Suited for Volatile Demand

Transformers have proven their might in volatile markets. They can unravel the complexities of erratic consumer behavior like no other.

Python Library: pytorch-forecasting

The Temporal Fusion Transformer from this library is adept at forecasting items that see sudden spikes in demand, such as emergency medical supplies during a health scare. And for an extra boost, the TFT from the autogluon package has shown impressive performance in volatile demand scenarios, like predicting the next hot toy each holiday season.

2. General Applicability

When the demand landscape is as varied as a retailer's inventory, autogluon steps up. It's like having an all-terrain vehicle for your data, robust across various forecasting terrains.

Python Library: autogluon

Imagine optimizing stock levels across thousands of products in real-time; autogluon can handle it, proving invaluable for large online retailers managing diverse inventories.

3. For Smooth Demand

For the steady beats of smooth demand, LightGBM and XGBoost are like rhythm sections that keep the supply chain groove tight and efficient.

Python Library: lightgbm

R Package: xgboost

Consider consumer staples like toothpaste or laundry detergent; these libraries help ensure that production schedules and inventory levels are just right, avoiding overstock and understock situations.

4. For Intermittent Demand

Croston's method and the ATA method provide clarity in the face of intermittent demand, akin to a lighthouse guiding ships through foggy waters.

R Package: tsintermittent

These methods come to the fore in industries like aerospace, where the demand for certain spare parts can be unpredictable and scarce. Forecasting with precision here ensures optimal inventory without tying up capital.

5. Time State Modelling

The pyMC library is your oracle for interpreting the cryptic signs of time series data. It dives deep into the hidden states of the market, offering foresight amidst uncertainty.

Python Library: pyMC

In automotive supply chains, where a model's popularity can fluctuate wildly, pyMC helps to forecast component demand, ensuring the assembly line runs smoothly without interruption.

Conclusion

Thanks for diving into this with me. We've scoped out some top tools that'll help steer your supply chain through the unpredictable. Whether it's pytorch-forecasting for those wild demand swings or autogluon for just about anything, you've got the tech to stay ahead. Smooth out the bumps with lightgbm or xgboost, and for those rare but tricky parts, count on tsintermittent. And when the going gets tough, pyMC will be your beacon in the fog.

In a nutshell, you're well-equipped to predict, adapt, and thrive. Keep an eye out for more deep dives into these methods—they're game-changers. Let's keep making smart, data-driven decisions together. Catch you next time!

▶️BONUS: Grab a complete Autogluon Python notebook by filling out a quick survey here.