Inventory: Value, Coverage, Turnover and ABC Classification

Inventory, money lying idle

Inventory exists to meet demand without waiting for the supplier. But every item on a shelf or in a warehouse is tied-up money: it has been paid for, it takes up space, it can expire or become obsolete. Inventory management seeks a balance between two risks: stockout (losing sales) and overstock (needlessly tying up money).

Value, coverage and turnover

items["stock_value"] = items["stock_units"] * items["unit_cost"]
items["daily_usage"] = items["annual_usage"] / 365
items["coverage_days"] = items["stock_units"] / items["daily_usage"]
items["usage_value"] = items["annual_usage"] * items["unit_cost"]
items["rotation"] = items["usage_value"] / items["stock_value"]

• Coverage answers the question: “if nothing more arrives, how many days can I hold out?”. P1: 400 units at 13.7 units per day, i.e. about 29 days.
• Turnover says how many times the stock “turns over” in a year. P1: 50,000 euros consumed for 4,000 euros of stock, i.e. a turnover of 12.5.

The two indicators are linked: coverage in days ≈ 365 / turnover. Overall, you compute turnover on the totals (total consumption / total stock), like any ratio. Ideally, the denominator is an average stock over the period (average of monthly readings) rather than a snapshot at one date.

Spotting overstock

A simple threshold, set with purchasing, flags items that are over-covered:

overstock = items.index[items["coverage_days"] > 120].tolist()

An item covered for six months while the supplier delivers in two weeks ties up money for no reason. Conversely, a coverage shorter than the supplier lead time signals an upcoming stockout (lesson 2 and lesson 4).

ABC classification

Not all items deserve the same attention. The Pareto principle is often observed: a small share of items accounts for most of the value consumed. ABC classification exploits it:

abc = items.sort_values("usage_value", ascending=False)
abc["cum_share"] = abc["usage_value"].cumsum() / abc["usage_value"].sum()
abc["abc_class"] = "C"
abc.loc[abc["cum_share"] <= 0.95, "abc_class"] = "B"
abc.loc[abc["cum_share"] <= 0.80, "abc_class"] = "A"

1. Sort the items by decreasing consumption value.
2. Compute the cumulative share.
3. Classify: A up to 80%, B up to 95%, C beyond.

Here, P1 and P2 (2 items out of 8) make up 75% of consumption: they are the A items. The 80 / 95 thresholds are a common convention, not a rule: some companies use 70 / 90, or classify on margin rather than consumption. Write down your choice.

Crossing ABC and stock

The classification becomes useful when you cross it with stock:

summary = abc.groupby("abc_class").agg(skus=("abc_class", "size"),
                                       usage_value=("usage_value", "sum"),
                                       stock_value=("stock_value", "sum"))

C items account for only 6% of consumption, but 21% of stock value (3,000 euros out of 14,000): that is where the money sleeps. Typical policy:

• A: careful forecasts, frequent review of parameters, close supplier follow-up;
• B: standard rules;
• C: grouped orders, reduction of the number of references, clearance of dormant items.

Key takeaways

• Coverage = stock / daily consumption; turnover = consumption / average stock.
• ABC classification sorts by value and classifies by cumulative share, with written thresholds.
• Cross ABC and stock value to know where to act.