Dpl

How it works

What you actually get

DPL doesn't apply one blanket formula to every product. It reads the behaviour of each SKU individually and picks the right mathematical method automatically — the same way a senior demand planner would, but in seconds, across your entire catalogue.

Step 1 — The engine reads your SKU's DNA

Before forecasting anything, DPL analyses three signals for every product in your catalogue:

Volatility (CV)

How erratic is demand month to month? A product with stable sales has a low CV. A slow-moving or irregular product has a high CV.

Trend

Is demand consistently growing or declining over time? The engine detects real trend signals and separates them from random noise.

Seasonality

Does demand spike every summer or dip every January? Seasonal patterns are identified and built into the forecast automatically.

Step 2 — It selects the right model for each product

Based on those signals, DPL automatically routes each SKU to one of three proven forecasting methods:

WMA

Weighted Moving Average

Stable, regular demand

Used for products that sell consistently every month with low volatility. The engine gives more weight to recent months than older ones — so if your product just had a great quarter, that signal matters more than data from 18 months ago.

When it's used

CV (volatility) is low. Demand is regular and predictable, even if volumes vary slightly month to month.

Why it works

Simple and robust for steady products. Doesn't over-complicate what doesn't need complicating. Reacts quickly to genuine demand shifts.

CRO

Croston's Method

Intermittent / slow-moving demand

Used for products that don't sell every month — spare parts, seasonal accessories, niche items. Standard averaging fails badly here because zeros drag the forecast down. Croston solves this by treating two questions separately: how often does this product sell, and how much does it sell when it does?

When it's used

CV is high and the product has many zero-demand periods. Typical for spare parts, slow movers, or high-value low-frequency SKUs.

Why it works

Prevents systematic over-forecasting of products that barely move. Avoids building excess safety stock for items that sell once a quarter.

TSD

Time Series Decomposition

Seasonal products

Used for products with a clear repeating seasonal cycle. The engine splits historical demand into three layers — the underlying trend, the seasonal pattern, and random noise — then rebuilds the forecast from the clean structural signals only.

When it's used

A detectable seasonal cycle exists — demand reliably peaks or dips at the same time each year across multiple seasons.

Why it works

Isolating seasonality from trend means neither distorts the other. You get a forecast that captures the summer spike without inflating the baseline.

Built-in guardrails — because data is never perfect

Real-world data has stockouts, promotions, and one-off events that distort history. DPL includes a set of automatic safeguards:

Growth Cap ±50%

Year-over-year growth is capped at 50% to prevent runaway forecasts on volatile or data-sparse SKUs.

Noise Guard

On high-volatility products, conflicting micro-trends are suppressed. The forecast follows the structural signal, not the latest spike.

Stockout Detection

Zero-demand months caused by stock shortages — not real demand — are identified and excluded from your history before forecasting.

WAPE

Primary accuracy metric. Weights errors by volume so a miss on a high-selling SKU matters more than a miss on a slow mover.

MAPE

Secondary check. Percentage error per SKU, useful for spotting outlier products that need manual review.

BIAS

Tracks whether the system consistently over- or under-forecasts. A bias near zero means the engine is well-calibrated.

Ready to see it on your data?

Connect your catalogue and get a full forecast report in minutes.

Demand Forecasting & Supply Planning Engine

Smart Selection

Precision Tuning

Financial Impact

What you actually get

Step 1 — The engine reads your SKU's DNA

Step 2 — It selects the right model for each product

Weighted Moving Average

Croston's Method

Time Series Decomposition

Built-in guardrails — because data is never perfect

Ready to see it on your data?