Inventory that matches the warehouse
Stock the system says is there but is not on the shelf, products with wrong weights or dimensions and lead times nobody updates. How to spot and fix data quality problems in operations and logistics.
The 5 most common problems
In operations, wrong data becomes a stock-out, a wrong delivery or a half-empty lorry.
System stock does not match reality
What the system says is not what is in the warehouse: unexpected stock-outs or too much inventory.
How to spot itCycle counts: difference between units counted and units in the system.
Incomplete or wrong product records
Weight, dimensions, unit of measure or barcode are missing, so shipments and warehouse space are miscalculated.
How to spot itShare of active products with every logistics attribute filled in.
The same product under several codes
An item set up twice, by supplier or by mistake. Stock gets split and you buy too much.
How to spot itMatches on barcode, supplier reference or normalised description.
Supplier data out of date
Lead times, prices or minimum quantities from months ago, so purchase planning fails.
How to spot itDate each supplier’s terms were last reviewed, and planned versus actual lead time.
Inconsistent units and locations
Bought in cases, stored in units and sold in packs with no clear conversion; locations that do not exist.
How to spot itProducts without a unit conversion and movements to undefined locations.
Recognise any of these?
Measure your area with the free checklist: 20 checks with automatic scoring.
Download the checklist →Which indicators to track
Eight indicators for inventory, products and suppliers. Thresholds are a starting point: adjust them to your operation and review them every quarter.
| Indicator | How it is calculated | Starting threshold |
|---|---|---|
| Inventory accuracy | Items whose count matches the system ÷ items counted | above 97% |
| Complete product records | Active products with weight, dimensions, unit and barcode ÷ active products | above 98% |
| Duplicate products | Products with a repeated barcode or supplier reference ÷ products | 0% |
| Supplier terms up to date | Suppliers with terms reviewed in 6 months ÷ active suppliers | above 90% |
| Reliable lead times | Orders received within the recorded lead time ÷ orders | above 90% |
| Unit conversions defined | Products with a unit conversion ÷ products that need one | 100% |
| Movements to valid locations | Movements to set-up locations ÷ movements | 100% |
| Orders without corrections | Orders with no manual fix to quantity, price or address ÷ orders | above 98% |
Where to start, in three steps
Each step links to the guide and, where there is one, to the ready-made template.
Count and compare
Cycle-count your fastest-moving items and measure how many product records are complete.
One record per product
Define the mandatory data for each product, merge duplicates and set the conversions between units.
Suppliers and monitoring
Review each supplier’s terms, compare planned and actual lead times and bring the indicators to the operations meeting.
Frequently asked questions
How often should inventory be counted?
Rather than one big annual count, cycle counts work better: the items that move most or are worth most are counted more often, the rest less. Differences are caught sooner.
Who should own product records?
A person or team accountable for the product master (often procurement, product or a master data team). Every new product should pass its rules before it is used.
What is a data contract with a supplier?
An agreement on which data they send you, in what format and with what quality; for example, product records with every logistics attribute. It saves fixing the same thing on every order.
Can AI help plan purchasing?
It can, but it learns from your history. If inventory or supplier lead times are unreliable, the forecast will be too. Clean data first; forecasting second.
Where does your data governance stand?
The free assessment tells you what to prioritise in five minutes.