KKobin GroupProcurement KPI
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Price performance (harga PO terhadap last PO)

Standard: Min – 1% · Weight: 10% · calculated per month

Strategy — what this KPI measures

Measures negotiation performance: is each purchase cheaper than the last time the same item was bought? The target is an average price decrease of at least 1% versus the previous PO of the same item.

Data sources & how they are merged

  • Source: the LEAD TIME PR TO GR file, which carries per row both the item's 'Last Purchased Price' and its 'Current PO Price' — no price history needs to be rebuilt.
  • LEAD TIME is goods-receipt-line grain (a PO-item repeats once per receipt), so comparisons are deduped to one per (PO No, Item Code).

The full merged dataset is browsable on the Data page, including the merge explanation.

Monthly calculation — step by step

  1. Take each PO-item in the month (by PO date) with a positive Last Purchased Price and Current PO Price.
  2. Record the price change: Δ% = (current − last) ÷ last × 100. Negative Δ means cheaper — good.
  3. Exclude moves beyond −90% or +300% (unit/pack/currency changes, not negotiations).
  4. REAL = the average Δ% of the remaining comparisons.

Formula

Δ% per row = (Current PO Price − Last Purchased Price) ÷ Last Purchased Price × 100
REAL = average Δ% of all comparable rows in the month
%    = REAL ÷ (−1%) × 100, clamped to 0–100  (e.g. REAL −0.30% → 30%; any average price increase → 0%)
CAPAI = % × 10% → max 10 points

Month attribution

A comparison belongs to the month of its PO date. The last-purchased PO may be from any earlier period.

Edge cases & caveats

  • Rows with no PO, or missing/zero last or current price (e.g. a first-ever purchase), have nothing to compare and are excluded.
  • Extreme moves (a drop ≥ 90% or rise ≥ 300%) usually mean the unit of measure or currency changed in the source; these are flagged as upload warnings so they can be checked, but are still included in the average.

See the numbers

On the dashboard, click any REALvalue of this KPI to open the month's drill-down with every underlying row used in the calculation.