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Short and over shipments: measuring delivery accuracy

Short and over shipments show the deviation between the ordered and the actual quantity. The metric affects OTIF/Fill Rate, logistics and return costs, and customer satisfaction.

What the metric measures

The share of short-shipped (shorts) and over-shipped (overs) volume, as well as the share of lines/orders with deviations outside contractual tolerances.

Calculation

  • Shorts Rate, % = Σ max(Ordered − Delivered, 0) / Σ Ordered × 100
  • Overs Rate, % = Σ max(Delivered − Ordered, 0) / Σ Ordered × 100
  • Line Non-Conformance, % = Lines outside tolerances / All lines × 100
  • Base: promised/confirmed qty, agreed UOM and pack multiples; partial/backorder policy

For contractual tolerances (e.g., ±2% or "in multiples of 6 units"), track specifically the deviations outside tolerances.

Calculation example

10,000 units were ordered in a month across 200 order lines. 450 units on 18 lines were short-shipped and 120 units on 6 lines over-shipped. Short-shipment rate by quantity = 450 / 10,000 × 100 = 4.5%, share of lines with variances = 24 / 200 × 100 = 12%.

Data sources

ERP/OMS (orders, confirmations), WMS (picking/packing/ship), TMS/3PL (POD), EDI/ASN, UOM/pack multiple master data, substitution rules, returns module.

Interpretation

  • Shorts↑ — stock shortages, picking errors, QA hold, IRA inaccuracies, substitution not allowed
  • Overs↑ — incorrect pack multiples/UOM, rounding up to container size, SKU substitution without approval, scanning errors
  • Balance service and costs: "zero deviations" can cost more than moderate tolerances

Useful breakdowns

  • Channels / customers / segments
  • Warehouses / shipping waves / carriers
  • SKU / categories / ABC; packaging type/pack multiple
  • Reasons (code list), substitution allowed/not allowed

Dashboard visualizations

  • Trends in Shorts/Overs Rate and the share of lines outside tolerances
  • Pareto chart of SKUs/customers/reasons
  • Heatmap by warehouse/wave
  • Funnel "ordered → picking → ship → delivery"
  • Correlation with OTIF/Fill Rate

How to set targets

For A-SKUs, aim for Shorts ≤ 0.5–1% and Overs ≤ 0.2–0.5%; for others, follow contractual tolerances. Define a partial/backorder policy, substitution rules, mandatory UOM/pack multiple scanning, and preventive alerts before ship.

Common mistakes

  • Calculating from revenue instead of quantity
  • Using "requested" as the base instead of confirmed/promised
  • Ignoring UOM/pack multiples and the difference between ship and delivery
  • Counting customer cancellations/reschedules as short shipments
  • Mixed units without normalization; backorders not tracked separately

Questions for discussion

  • Which 3 reasons cause 80% of deviations?
  • Is the UOM/pack multiple master data complete and correct?
  • What would introducing "in pack multiples" ordering in the cart/OMS achieve?
  • Where is the optimal balance between substitution and backorders?

Questions and answers

How do you calculate the short-shipment rate?

Rate, % = quantity short-shipped / quantity ordered × 100. It also helps to count the share of order lines with any variance.

What is wrong with over-shipments?

Extra goods increase inventory and tie up cash, create extra work in receiving and returns and distort the records.

How do you reduce short and over shipments?

Agree quantity tolerances in contracts, check availability before confirming orders and track the metric for each supplier.