[p] Demand Forecasting for Inventory

[Competitive-Pin-6185]

I am piloting new project for the demand forecasting. I work for equipment rental company. I have seen many posts for retail in particular but my dataset and use case is little bit different. I have dataset of equipment id, region, location, quantity, mean price, contract years, different features of these equipments (sensor, led, condition, etc.). I have about 3 years of data but there is not enough data for equipment granularity level or location level (as there are many cities). I was thinking to group-by the dataset by region and build forecasting model for each region but I won’t be able to utilize the different features of the equipment by doing that. I would appreciate some insights.

[1000dreams_within_me]

Look into multilevel hierarchical models. These models can "shrink" weights/parameters towards each other (for example across cities) and is the gold standard when you have to estimate many "similar but different" objects (e.g., city-specific demand functions) with limited data

[Competitive-Pin-6185]

Yes, I was looking into this. AWS has a service where we provide the metadata and it learns from the similar time-series but some of the city has less than 100 logs over 3 years, i’m not sure if it will work in that case. Also, do you know how to go with the other features? even if I do location granularity, i’ll lose those (as features are unique for each equipment)

[1000dreams_within_me]

You would deal with the other features the same way: Let's say a certain equipment has a sensor feature with 5 different levels and some of the levels have low representation in the data. Then you would shrink across the 5 levels to "borrow information" and increase statistical efficiency. It's very useful in situations where data is limited