PlantOps BI

KPI Library / Inventory

Inventory Turns

Formula: Cost of goods sold ÷ average inventory value
Typical range: 6-12 turns/year is typical for discrete manufacturers; under 4 usually means excess or slow-moving stock

Inventory turns measure how many times a company sells through and replaces its average inventory over a period, showing whether working capital is converting into revenue or sitting on a shelf. A higher number means inventory dollars are moving faster; a lower one means cash is tied up in stock that isn’t selling.

What good looks like

Discrete manufacturers commonly run 6 to 12 turns a year. Under 4 turns usually points to excess or slow-moving stock somewhere in the mix, and it’s worth checking whether the drag is raw material, WIP, or finished goods before assuming it’s a demand problem. On the other end, extremely high turns (20 or more) aren’t automatically good news either; they can signal a plant running so lean it’s exposed to stockouts and expedited freight every time demand ticks up.

The number is easy to flatter by using a single point-in-time inventory snapshot, usually taken at period end, instead of a true average. A plant that pushes hard to draw inventory down right before month-end close will post a great turns number that has nothing to do with how it operates the other 25 days of the month.

Inventory Turns in Power BI (DAX)

With a trailing COGS fact and a monthly inventory snapshot fact:

COGS (TTM) =
CALCULATE (
    SUM ( fact_cogs[cogs_amount] ),
    DATESINPERIOD ( dim_date[date], MAX ( dim_date[date] ), -12, MONTH )
)

Average Inventory =
AVERAGEX (
    VALUES ( dim_date[month] ),
    CALCULATE ( SUM ( fact_inventory_snapshot[inventory_value] ) )
)

Inventory Turns = DIVIDE ( [COGS (TTM)], [Average Inventory] )

Days of Inventory = DIVIDE ( 365, [Inventory Turns] )

AVERAGEX over monthly snapshots is doing real work here; averaging several points across the year is far harder to game than any single snapshot date.

Common mistakes

  1. Using a period-end snapshot instead of an average. A single low balance at month close can flatter the ratio without reflecting how inventory actually ran all month.
  2. Blending raw material, WIP, and finished goods into one number. Each has a different target and a different owner, and a blended turns figure obscures which one is actually the problem.
  3. Comparing turns across product lines with different margins or lead times without normalizing. A build-to-order line and a distribution-heavy line will never share the same reasonable benchmark.