Reverse Engineering: How to Get the Data You Want, without the Data You'd Need

Predictive Inventory Analytics Without Historical Data

We’ve come to expect data in real time as the norm. We have also become accustomed to relying on predictive analytics that draw from historical trends: past months, quarters, or years.

One question I’ve been tackling lately is how to help a company that hasn’t been tracking its inventory properly? How can predictive analysis be done without a reliable historical record?

I have a goal to build a monthly tracking table that clearly shows material usage and inventory balances over time for several of my clients. But to run predictive models, you typically need multiple months of accurate historical data.

Unfortunately, some companies don’t store historical inventory data. This could be due to system limitations, lack of storage protocols, or an IT decision.

So the question becomes: how can a company predict what to order, and how much material to store, if historical inventory data doesn’t exist?

One approach: work backwards using transaction data, like records of goods purchased and sold. Then, I pair this with daily inventory snapshots to reconstruct what the historical balances must have been.

For example, if today’s inventory is 20 units, and yesterday 2 units were sold, then yesterday’s inventory must have been 22. Apply this logic consistently, and you can rebuild historical inventory day by day, ultimately rolling that up into months for higher-level insights. This method allows me to retroactively populate a tracking table.

There are two added complexities that make it more interesting:

1. Not all transactions affect inventory counts. You have to carefully filter transaction types and isolate only the ones that actually move inventory.

2. Scale matters. Several of our clients move thousands of items per month. That means the tracking tables grow quickly, and the solution must be robust enough to handle large data volumes efficiently.

Once completed, this kind of historical reconstruction opens the door to tracking inventory trends, spotting anomalies, reducing waste, and improving forecasting.

Inventory management is the difference between what you think you’ll receive or consume, versus what’s actually happening. When a company lacks real-time data, they’re forced to rely on quarterly reporting. By the time those reports come in, it’s often too late to adjust for inefficiencies.

Reverse engineering the tracking tables has been super helpful in navigating predictive analyses for clients.