Inventory Insighter | Higu Baruah
Higu Baruah has a unique story. He started his career in sales but ended up in data analytics. When asked about this unique path he says “knowing what happens on the ground helps me analyze data better than those who have never faced customers”.
In this blog post, we talk to Higu about one of his analytical models which he developed to optimize inventory for an automotive business. This spans across the supply chain but for this blog we will focus on the dealer capital part of the chain.
We will do this using only publicly available information, publicly available research papers and open source libraries — so that it can be deployed by anyone else working on similar problem set.
If you have to buy a car, most likely you will visit a dealership. They get their stock from the local warehouse where it reaches from a regional stockyard, or from the manufacturing plant itself. There are inventories (or floating inventories) at all these stages of the supply chain:
In modern automotive sales, there can be hundreds of SKUs, which means that no dealer can have all the models a customer may want. There will always be a non-zero waiting period until the desired vehicle is procured some customer who wants a model that is not “regular” enough to be stocked.
This creates a tension between two optimization functions:
- How to maximize available stock so that customers get their desired cars as quickly as possible. A company that fulfils demand slower than competition is likely to lose customers.
- How to maximize inventory turnover so that every dollar used in inventory is converted into sales as many times as possible. A company that rotates the capital least will require more capital to make the same revenue. Here is a comparison between two OEMs’ turnover ratio as an example:
The first step that Higu came up with was pretty straightforward — segment the SKUs on the basis of their historical demand volume, and the variation in this demand. Something along these lines:
Standard deviation is a good enough metric for “variation” in demand. Check out below inbuilt module in Python.
stdev() method in Python statistics module - GeeksforGeeks
Statistics module in Python provides a function known as , which can be used to calculate the standard deviation…
Equipped with this intelligence, a strategy can be formulated as:
- Allocate maximum capital to high volume low variation models
- Allocate minimum capital to low volume high variation models
Further, inventory can be maintained at two different level
- Cycle stock: the predicted demand per SKU based on historical demand
- Safety stock: stock to be maintained based on the historical average and the standard deviation as explained by WikiHow here
Enter: Genetic Algorithms
Higu was sort of happy with the above results because it introduced some method of madness and helped optimize turn over ration and stock availability at some pilot locations.
But he still faced the larger challenge of implementing this globally, which means factoring in local variables like:
- Dealer specific high volume SKUs which are low volume for the region
- Location specific seasonality in demand
- Introduction of new models with no prior historical demand data
To develop a model for each SKU, for each region, for each seasonal variation and down to the dealer level is daunting task. This makes the above model un-scalable and unreliable.
To overcome this challenge, Higu and team thought of implementing Genetic Algorithms. These algorithms aim to optimize for a given constraint by “evolving” just like genes do over generations.
Genetic Algorithms are one of the branches of AI that is used for okay-ish imperfect but quick solutions:
Here is a working example that can be run using Python
Genetic Algorithms - GeeksforGeeks
Genetic Algorithms(GAs) are adaptive heuristic search algorithms that belong to the larger part of evolutionary…
In Higu’s case this method was applied to get the best possible turnover ratio under a constraint of limited capital. Over a few “generations” the script learnt how to get the maximum possible turnover within this constraint.
The exact implementation is a little outside the scope of this blog, but Higu says it was able to get good enough recommendations for most of the cases very quickly. He ran the algorithm with inventory as the “chromosome” while trying to optimize for turnover ratio.
Inventory optimization has a direct and strong impact on revenue and profitability of automotive or similar manufacturing sector businesses. We saw how Higu and team was able to pull this off using relatively simple statistical model, and some AI methods to get business insights which can be applied in real world scenario.