Inventory Optimization with Neural Nets

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5 Easy Steps to Finally Achieve Inventory Optimization   By Alex Bekker 

So you’ve put your best foot forward to optimize your inventory, but it’s still anything but perfect? To ensure continued success and boost your bottom line, check out these five easy action points below.

1. Opt for the right approach.

When deciding on optimal inventory levels, companies want to steer clear of risky assumptions and avoid relying on “gut feelings” and personal experience. This is why best practices in inventory management tend to rely on data science. However, even if your optimization initiatives do involve data science, this doesn’t guarantee that you are using the most efficient, objective approach.

To illustrate this, let’s consider three different inventory optimization techniques:

Approach #1 — You use advanced data science methods to calculate demand, but then you add an unoptimized hunch-based safety buffer to the predicted value to form inventory. This doesn’t sound like a scientific approach to inventory optimization, does it?
Approach #2 — You calculate demand probability distribution and apply a formula that weighs holding costs against shortage costs. Although this is pure data science, it’s again based on expectations, as we don’t really know the nature of demand probability distribution and can only assume what it may be.
Approach #3 — You use a deep neural network (DNN) that considers multiple demand-influencing factors. The network’s complex architecture and its “intelligence” are designed with your specific situation in mind. Also, the network is tailored to directly predict an optimal inventory level, without producing demand forecasts in between.
While each of these three approaches has something to do with data science, the differences between them are striking. The third approach is the only one that can guarantee a higher degree of objectivity. ... "