SCIKIQ · Data Academy · Reference

The Metric Dictionary

What each business metric means, how to work it out, and — the part that is usually missing — which system holds the number and which record it comes from. Written so a finance analyst, a supply chain planner and a data engineer can read the same page and agree.

58Metrics defined
16Finance & cash
13Pricing & sales
16Supply chain & operations

01 Finance & cash

Finance & cash

Margin, cash and the ratios a board reads first. These are the numbers the rest of the dictionary rolls up into. Serves the CFO view.

GM%

Gross margin %

The share of every sales euro left after the direct cost of making or buying what you sold.

(net_revenue − cogs) ÷ net_revenue
OM

Operating margin

Profit from running the business, before interest and tax, as a share of revenue.

operating_profit ÷ net_revenue
EBITDA%

EBITDA margin

Earnings before interest, tax, depreciation and amortisation, as a share of revenue.

ebitda ÷ net_revenue
CM

Contribution margin

What one extra sale leaves behind after the costs that only exist because you made that sale.

(net_revenue − variable_cost) ÷ net_revenue
DSO

Days sales outstanding

How long, on average, customers take to pay you after you invoice them.

(accounts_receivable ÷ net_revenue) × days_in_period
DIO

Days inventory outstanding

How long stock sits before it is sold, on average.

(inventory_value ÷ cogs) × days_in_period
DPO

Days payables outstanding

How long you take to pay your suppliers.

(accounts_payable ÷ cogs) × days_in_period
CCC

Cash conversion cycle

The number of days between paying for something and being paid for it.

dso + dio − dpo
CCR

Cash conversion ratio

How much of the profit you reported actually turned into cash.

operating_cash_flow ÷ net_income
FCF

Free cash flow

The cash left after running the business and keeping its assets in shape — the money genuinely available for debt, dividends or investment.

operating_cash_flow − capital_expenditure
FA

Forecast accuracy

How close the forecast was to what actually happened, measured as absolute error against actuals.

1 − ( Σ|actual − forecast| ÷ Σactual )
OL

Operating leverage

How much profit moves for a given move in revenue.

% change in operating_profit ÷ % change in net_revenue
Capex%

Capex intensity

How much you must invest in assets for every euro of revenue.

capital_expenditure ÷ net_revenue
ROCE

Return on capital employed

The profit a business earns for every unit of capital tied up in it.

EBIT ÷ (total_assets − current_liabilities)
Leverage

Net debt to EBITDA

How many years of current earnings it would take to repay what you owe.

(total_debt − cash_and_equivalents) ÷ EBITDA
Variance

Budget variance

How far actual performance has drifted from plan, and in which direction.

(actual − budget) ÷ budget

02 Pricing & sales

Pricing & sales

What you charge, what you keep, and what it costs to win the next customer. The half of revenue that most reporting leaves out. Serves the CMO view.

ANP

Average net price

What you actually received per unit, after every discount, rebate and allowance.

net_revenue ÷ units
Gross-to-net

Price realisation

How much of your list price survives all the way to the invoice.

net_revenue ÷ list_revenue
WR

Win rate

The share of decided opportunities that you won.

opportunities_won ÷ opportunities_closed
SCL

Sales cycle length

How long a deal takes from first qualified contact to signature.

mean(close_date − created_date) for won opportunities
PC

Pipeline coverage

How many times your target is currently sitting in open, qualified pipeline.

open_pipeline_value ÷ target_for_the_period
CAC

Customer acquisition cost

What it costs, all in, to win one new customer.

(sales_cost + marketing_cost) ÷ new_customers_won
LTV:CAC

Lifetime value to CAC

How much gross profit a customer produces over their life, compared with what it cost to win them.

(average_gross_profit_per_year × expected_years) ÷ cac
NRR

Net revenue retention

What last year's customers are worth this year, before any new customers are counted.

(starting_revenue + expansion − contraction − churn) ÷ starting_revenue
MS

Market share

Your slice of a defined market, in units or in value.

your_units (or value) ÷ total_market_units
CTS

Cost to serve

Everything it costs to look after a customer after the sale is agreed.

(delivery + service + returns + order_handling) ÷ customers or orders
AOV

Average order value

What a typical order is worth, after discounts and returns.

net_revenue ÷ orders
SOW

Share of wallet

How much of a customer's spending in your category you actually hold.

your_revenue_from_the_account ÷ the_account_total_category_spend
Attach

Attach rate

How often a service, warranty, accessory or add-on is sold alongside the product it belongs to.

orders_containing_the_attached_item ÷ eligible_orders

03 Supply chain & operations

Supply chain & operations

Service, stock and the capacity to deliver. Where promises to customers either hold or quietly fail. Serves the supply chain view.

OTIF

On time in full

The share of orders that arrived when promised, with everything the customer asked for.

orders_delivered_on_time_and_complete ÷ total_orders
FR

Fill rate

How much of what was ordered you were able to ship.

units_shipped ÷ units_ordered
OEE

Overall equipment effectiveness

How much good output a line produced compared with what it could have produced running perfectly.

availability × performance × quality
Turns

Inventory turns

How many times you sell and replace your stock in a year.

cogs ÷ average_inventory_value
Cover

Stock cover

How many days of demand your current stock would satisfy.

current_stock ÷ average_daily_demand
POR

Perfect order rate

The share of orders that went through without a single failure — right items, right time, undamaged, invoiced correctly.

orders with no error at any step ÷ total_orders
SLT

Supplier lead time

How long a supplier actually takes, and how much that varies.

mean and variability of (goods_receipt_date − purchase_order_date)
Scrap%

Scrap and yield

The proportion of what you made that could not be sold.

scrapped_quantity ÷ total_produced (yield is the inverse)
SA

Schedule adherence

How much of what you planned to make you actually made, in the order you planned it.

units_produced_as_scheduled ÷ units_scheduled
FCU

Freight cost per unit

What it costs to move one unit to the customer.

total_freight_cost ÷ units_shipped
OSA

On-shelf availability

Whether the product was actually there for the shopper to buy.

store_days_product_available ÷ store_days_expected
Util

Capacity utilisation

How much of the capacity you already own is being used.

actual_output ÷ practical_capacity
MTTR

Mean time to repair

How long, on average, it takes to get a failed asset running again.

total_downtime_hours ÷ number_of_failures
Returns

Returns rate

The share of what you sold that came back.

units_returned ÷ units_sold
OCT

Order cycle time

How long a customer waits between placing an order and receiving it.

delivery_date − order_date, in calendar days
kWh/unit

Energy per unit

How much energy it takes to make one unit of output.

energy_consumed_kWh ÷ units_produced

Why this exists

A metric is a promise, not a label

Two teams reporting different numbers for the same thing is rarely a data problem. It is two definitions, both reasonable, neither written down. Every page here gives the four things a metric needs before anyone can rely on it: a formula, a level of detail, a source and an owner.

That is also exactly what a semantic layer stores. Once a definition lives in one place, every dashboard, board pack and AI agent that asks for it gets the same answer — which is the whole point of governing it.

Want these definitions running, not just written?

SCIKIQ turns a dictionary into a governed layer — one definition per metric, traced back to the source system.

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