Paste 30+ days of daily sales for any item. The classifier returns the item’s demand pattern (smooth, intermittent, erratic, or lumpy) via the Syntetos–Boylan–Croston (SBC) framework and the forecast method that should be applied. Same methodology LineNow runs internally on every connected item every night.
Sells most days, with low variance from day to day. Future demand looks like recent history. The straightforward case — daily-sold staples like coffee beans, milk, or a flagship product.
Exponential smoothing or a simple moving average. Standard min/max thinking works here too.
Standard `z × σ × √(lead time)` formula with a moderate service level (90% is the sensible default).
This is the easy case for the system; the operator should just set a reasonable service level and let the math run.
| Days observed | 30 |
| Days with non-zero sales | 27 |
| Days with zero sales | 3 |
| Mean daily sales | 4.63 |
| Std dev (σ) | 1.72 |
| Mean non-zero demand | 5.15 |
| ADI (Average Demand Interval) | 1.11 |
| CV² (squared coeff. of variation, non-zero demand) | 0.024 |
| SBC threshold | ADI ≤ 1.32, CV² ≤ 0.49 |
| Classification | Smooth |
The classifier uses two parameters from the SBC framework (Syntetos, Boylan, Croston) to place every item into one of four regimes:
The four regimes:
The reason this matters: applying the wrong forecast to the wrong regime silently destroys margin. A smooth-demand average applied to lumpy demand will over-order on top of the spikes and stockout between them. Most SMB inventory tools use one method for every item (typically a moving average or sell-through rate), which is why their replenishment math drifts from reality within a quarter. See Coefficient of Variation for the full math.
Classifying one item by hand is useful. Classifying 200 items every night, automatically, with the right forecast applied to each regime, is what LineNow does inside its closed-loop replenishment engine. Free 90 days, $100/month per business unit after.
