Why does a rolling average fail as a demand forecast?

Because it cannot contain the signals that move the market. Forum Energy Technologies forecast three product lines on a 12-month rolling average that ignored oil prices, drilling permits and seasonality, so it was structurally blind rather than merely inaccurate.

What external signals predict industrial equipment demand?

WTI crude futures gave 8 to 12 weeks of warning here, and Texas Railroad Commission drilling permit data added 11 points to 12-week forecast accuracy on its own. Overall accuracy rose from 61 correct in 100 to 84.

What does better forecasting free up in inventory?

Forum reduced its inventory balance from $28M to $21.4M without raising stockout risk, cut carrying cost by $1.8M a year and took quarterly stockouts from 34 to 4, each of which had cost roughly $42K.

Forum Energy Technologies · Manufacturing · Midland, TX · 20 weeks

Demand forecasting AI deployed across 3 product lines

An oil and gas equipment manufacturer relied on 12-month rolling averages for demand forecasting, causing frequent overstock on slow-moving SKUs and stockouts on fast movers. We developed an AI-driven forecasting model that improved accuracy, lowering inventory carrying costs by $1.8M per year and reducing stockouts from 34 to 4 each quarter.

Challenge

The manufacturer held $28M in inventory over three product lines, relying on a 12-month rolling average to forecast demand. This approach ignored oil price fluctuations, drilling permits, and seasonal shifts. As a result, slow movers piled up excess stock while fast movers ran out 34 times per quarter. Each stockout cost roughly $42K in lost sales and rush shipping.

Approach

We developed a demand forecasting model using oil price futures, drilling permit data from the Texas Railroad Commission, historical demand, and seasonal trends. The model produced weekly SKU-level forecasts for all three product lines, including confidence intervals to guide safety stock levels. We then integrated these forecasts directly into the ERP’s replenishment planning module.

Outcome

We cut inventory carrying costs by $1.8 million a year by adjusting safety stock levels based on demand patterns. Stockouts dropped sharply from 34 to 4 per quarter. We lowered the inventory balance from $28 million to $21.4 million without increasing stockout risk. The new forecasting model improved 12-week accuracy to 84 of every 100, up from 61 using the old rolling average approach.

How we tracked oil price swings to guess what the market would do next

Alright, here’s the deal. Before, we were smoothing out those wild swings in oil prices with a 12-month rolling average. It worked, but it felt like we were always a few steps behind. So, we tried something different, using WTI crude futures as an early warning system. That little change? It gave us a jump on demand shifts about 8 to 12 weeks before the orders actually popped up. Pretty cool to catch those trends way ahead of time.

We Dug Into Texas Railroad Commission Drilling Permits to Spot What’s Really Going On

We went straight to the source and pulled drilling permit data from the Texas Railroad Commission, because that’s a killer indicator for equipment demand. I built a pipeline that automatically grabs and updates this data every week, then feeds it into our demand model. The payoff? Our 12-week forecast jumped by 11 points in accuracy. Not bad at all.

We plugged the ERP system in to make restocking a whole lot easier.

We hooked the demand forecasts right into the ERP’s replenishment system. Basically, whenever stock fell below the safety level the model suggested, the system would automatically kick off purchase orders. No more us having to do it by hand. It kept the buying perfectly in line with what the forecast showed, every single time.

Results

$1.8M Annual inventory carrying cost saved
34 to 4 Quarterly stockout incidents
$6.6M Inventory balance reduction
61 correct of 100 to 84 correct of 100 forecasts 12-week forecast accuracy

We had $28 million stuck in inventory but still kept running out of the parts customers needed. Thinklytics built a model using oil prices and drilling permits to predict demand. Since then, we’ve cut stockouts from 34 down to 4 per quarter and freed up $6.6 million in working capital.
VP of Supply Chain, Oil and Gas Equipment Manufacturer

In the client's terms

The question

Is the data in a state that will survive what we want to build on it?

The situation

Twenty-eight million dollars of inventory across three product lines, forecast on a 12-month rolling average that ignored oil prices, drilling permits and seasonality. Slow movers accumulated while fast movers went out of stock 34 times a quarter, at roughly $42K a time.

What we found

A rolling average cannot represent a business whose demand is driven by external signals it does not contain. The forecast was not inaccurate through carelessness, it was structurally blind to the things that actually move this market.

What it produced

Carrying cost fell $1.8M a year and stockouts from 34 a quarter to 4. The inventory balance came down from $28M to $21.4M without raising stockout risk. Twelve-week forecast accuracy rose from 61 in 100 to 84.

The roadmap

A demand model built on oil price futures, Texas Railroad Commission drilling permit data, demand history and seasonality, producing weekly SKU-level forecasts with confidence intervals so safety stock can be set against uncertainty rather than against a single point. The forecasts were written directly into the ERP replenishment module rather than published as a report somebody has to action.

The work behind this result

We react instead of predicting, the problem this engagement resolved.
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