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KPI Checklist
Supply Chain & Logistics
10 essential supply chain KPIs — each with a definition, formula, sector benchmarks and a concrete Power BI implementation tip. Built for SMEs in logistics, distribution and manufacturing.
10
Supply Chain
KPIs
KPIs
Why these KPIs?
From measuring to managing
Most SMEs in logistics and distribution measure too little, too late — or track the wrong indicators. They find out after the fact that something went wrong, but not why or when it was already visible in the data. Den Otter Solutions compiled these 10 KPIs based on 25+ years of executive experience in supply chain and logistics.
Inventory & availability
Turns, DIO, fill rate and out-of-stock rate — the four KPIs that together qualify your inventory management.
Suppliers & chain
OTIF score, Cash Conversion Cycle and purchasing quality — the KPIs that expose your dependencies.
Cost & capacity
Transport costs, return rate and warehouse utilisation — the KPIs that show where margin is leaking.
The KPIs
10 supply chain KPIs — explained
Click any KPI to see the definition, formula, sector benchmarks and Power BI implementation tip.
Inventory & capital
Customer delivery
Supplier
Cost & capacity
Working capital
01
Inventory Turns
How many times total inventory is sold and replaced per year.
Inventory
Definition & why measure
How many times the total inventory is sold and replaced over a year. Higher turnover means less capital tied up in stock.
Low turnover signals dead stock or over-purchasing. High turnover can indicate stockout risk. Always review alongside fill rate — they tell opposing halves of the same story.
Formula
COGS ÷ Average Inventory ValueBenchmark by sector
Manufacturing
4–8×
Wholesale
6–12×
Retail / FMCG
8–15×
What does this KPI tell you?
A turnover of 6× means selling the entire stock roughly every two months. At 12× that is every month. Always compare to sector norms — for a wholesaler, 4× is a warning sign; for a manufacturer it may be entirely normal.
Power BI implementation tip
Calculate per product, location and supplier. Add a slicer on ABC value class to filter by inventory tier. Combine with DIO in a single matrix to instantly see where capital is tied up.
02
Days Inventory Outstanding (DIO)
Average number of days inventory sits on the shelf before being sold.
Inventory
Definition & why measure
The average number of days inventory sits on the shelf before being sold. Also known as Days in Inventory.
High DIO extends the cash conversion cycle and increases storage costs. Every day reduction is direct liquidity headroom — crucial for working capital management.
Formula
(Avg. Inventory ÷ COGS) × 365Benchmark by sector
FMCG
20–40 days
Wholesale
45–90 days
Custom mfg.
60–120 days
What does this KPI tell you?
DIO is the inverse of inventory turns — expressed in days. A DIO of 90 is alarming for an FMCG business. For a custom manufacturer, 90 days is entirely expected. Sector context is essential before drawing any conclusion.
Power BI implementation tip
Combine with slow-mover analysis: flag items with DIO more than twice the category average as an action list for purchasing. Colour-code: green = on target, orange = 1.5×, red = 2× — directly actionable.
03
Fill Rate (Service Level)
% of orders delivered in full and on time from stock.
Delivery
Definition & why measure
The percentage of orders delivered in full and on time from stock, without backorder or partial shipment.
Every percentage point below target costs customer confidence and drives emergency orders or lost revenue. One of the most direct customer-facing KPIs in supply chain.
Formula
(Fully delivered lines ÷ Total order lines) × 100%Benchmark by sector
SME minimum
≥ 95%
Retail / FMCG
≥ 98%
Healthcare
≥ 99%
What does this KPI tell you?
A fill rate of 92% sounds high — but for a customer placing 50 order lines per month that means 4 problems every month. The customer feels each one individually. The average hides the real impact.
Power BI implementation tip
Segment by product and customer group. Surface the ten worst-performing SKUs as an action list for safety stock decisions — that is where improvement pays fastest.
04
Out-of-Stock Rate
% of order lines where no stock was available at point of demand.
Delivery
Definition & why measure
The percentage of items or order lines where no stock was available at the point of customer demand.
Stockouts directly cause customer churn and lost revenue. They are often a symptom of inadequate demand forecasting or insufficient safety stock levels.
Formula
(Undeliverable order lines ÷ Total order lines) × 100%Benchmark by sector
E-commerce
< 1%
Retail
< 1–2%
B2B wholesale
< 3–5%
What does this KPI tell you?
Difference from fill rate: out-of-stock rate measures the absence of inventory. Fill rate measures delivery performance including lead time. Both KPIs complement each other — run them together for the full picture.
Power BI implementation tip
Link to purchase order data: show how long each item was unavailable and when replenishment arrived. This immediately exposes reorder frequency problems and slow supplier response times.
05
Supplier Reliability (OTIF)
% of supplier deliveries arriving both on time and in full.
Supplier
Definition & why measure
On Time In Full — the percentage of supplier deliveries that arrive both on time and complete.
Low OTIF forces higher safety stocks and emergency orders. It also provides concrete leverage in supplier negotiations — numbers beat opinions every time.
Formula
(OTIF deliveries ÷ Total deliveries) × 100%Benchmark by sector
SME minimum
≥ 85%
Good supplier
≥ 92%
Best-in-class
≥ 97%
What does this KPI tell you?
An OTIF of 80% means 1 in 5 deliveries has a problem — hidden costs in planning and express freight that never appear as a separate line item. That is exactly what makes OTIF so valuable: it makes invisible costs visible.
Power BI implementation tip
Rank suppliers by score each quarter. Red below 85%, amber below 92%, green above 92%. Directly usable as input for supplier review meetings — the dashboard does the argument for you.
06
Inventory Value by ABC Class
Distribution of inventory value across high (A), medium (B) and low (C) value items.
Inventory
Definition & why measure
Distribution of total inventory value across high-value (A), medium-value (B) and low-value (C) items based on Pareto logic.
C-items that tie up disproportionate capital are a direct cash flow drain. This analysis gives direction to disposal decisions and purchasing priorities.
Formula
A-items: top 20% of SKUs ≈ 80% of revenueTarget distribution
A-items
60–70%
B-items
20–30%
C-items
< 10%
What does this KPI tell you?
If C-items account for 25% of inventory value, there is work to do. They demand the same attention as A-items but generate almost no revenue. That imbalance is where capital goes to sleep.
Power BI implementation tip
Build a matrix: item value class on the Y-axis, turnover speed on the X-axis. The bottom-right quadrant (C-items, low turnover) is the disposal priority — instantly visible, immediately actionable.
07
Cash Conversion Cycle (CCC)
Days between paying suppliers and receiving customer payments.
Working cap.
Definition & why measure
The number of days between paying suppliers and receiving customer payments. The most direct measure of working capital efficiency.
A high CCC means working capital is locked up for longer. Every day shorter is direct liquidity headroom without additional financing or revenue growth.
Formula
DIO + DSO − DPOBenchmark by sector
Retail
10–30 days
Wholesale
30–60 days
Manufacturing
40–90 days
What does this KPI tell you?
The CCC connects inventory management, accounts receivable and accounts payable in one number. Reduce it by collecting faster, paying later or holding less stock — each lever has a direct impact on liquidity.
Power BI implementation tip
Show CCC trend per quarter alongside net working capital. This makes it visible whether improvements in purchasing and credit control are actually translating into better liquidity — or just shifting the problem.
08
Transport Cost as % of Revenue
Total transport costs as a percentage of net revenue.
Cost
Definition & why measure
Total outbound and inbound transport costs divided by net revenue, expressed as a percentage.
Rising transport costs signal inefficient routing, too-small order sizes or supplier concentration. The impact shows directly in margin and is often overlooked until it is already structural.
Formula
(Total transport costs ÷ Net revenue) × 100%Benchmark by sector
Manufacturing
2–5%
B2B distribution
3–6%
E-commerce
8–12%
What does this KPI tell you?
Transport costs of 10% at a B2B distributor are double the sector average. The most common root cause is small orders requiring express freight. Making order size visible immediately points to the solution.
Power BI implementation tip
Segment by carrier, region and order size. Smallest orders are most expensive per unit — making this visible in a scatter plot drives directly toward a minimum order policy.
09
Return Rate
% of units returned or credited relative to total units sold.
Cost
Definition & why measure
The percentage of returned or credited goods relative to total units sold.
High return rates increase processing costs and disrupt inventory planning. Return patterns typically point to quality or packaging failures upstream — early detection saves margin.
Formula
(Returned units ÷ Units sold) × 100%Benchmark by sector
B2B
< 2–3%
Consumer goods
5–10%
E-com. fashion
15–30%
What does this KPI tell you?
Returns cost an average of 30–40% of product value in processing. A 5% return rate on €5M revenue already means €75,000 in processing costs — before any write-downs. The number is rarely as small as it looks.
Power BI implementation tip
Categorise return reasons: damage, wrong delivery, quality. Link to supplier or production batch for root cause analysis. Show top-5 reasons as a Pareto chart — that drives the corrective action.
10
Warehouse Utilisation Rate
% of available storage capacity actually in use.
Cost
Definition & why measure
The percentage of available storage capacity that is actually in use, measured in storage locations or cubic metres.
Above 90% utilisation, pick errors and safety risks increase sharply. Below 60%, a company is paying for storage capacity that generates no return.
Formula
(Occupied locations ÷ Total locations) × 100%Utilisation levels
Under-utilised
< 60%
Optimal
80–85%
Capacity risk
> 90%
What does this KPI tell you?
The 80–85% optimum is not arbitrary — that buffer is needed for efficient pick routes, safety clearance and absorbing peak periods without chaos. Above 90%, pick errors increase exponentially. Below 60%, you are paying for air.
Power BI implementation tip
Combine with seasonal data: show peak periods and plan ahead for temporary overflow space. This prevents expensive ad-hoc storage solutions during busy seasons — and shows when consolidation makes financial sense.
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Frequently asked questions
Supply chain KPIs in Power BI — questions & answers
Which KPIs are most important for a supply chain dashboard?
For an SME supply chain dashboard, inventory turns, fill rate and supplier OTIF are the three most critical KPIs. They directly affect customer delivery, working capital and supply reliability. Den Otter Solutions implements these three as the standard baseline dashboard for new clients — and builds outward from there based on specific pain points.
How do I build a supply chain dashboard in Power BI?
A supply chain dashboard in Power BI starts with the right data sources: ERP data, order management and optionally a WMS. Most SMEs work with systems such as Exact, AFAS, SAP Business One or similar. Den Otter Solutions connects those sources via Power Query, builds a star schema model and translates the 10 KPIs on this page into interactive visuals — with drill-down by product, customer and period.
What is a good fill rate for an SME distributor?
For B2B distributors, 95% fill rate is the standard minimum. Retail customers typically expect 98% or higher. Structurally below 92% leads to customer churn, emergency orders and reputational damage. Den Otter Solutions always measures fill rate by customer group and product category — so the real pressure points are visible, not buried in an average.
How long does it take to build a supply chain dashboard in Power BI?
A complete supply chain dashboard covering these 10 KPIs typically takes 3–5 working days with structured data from a single source, or 6–10 days with multiple sources and a more complex data model. Den Otter Solutions always starts with a Data Start Scan to assess the current state and set a realistic build timeline — avoiding surprises on both sides.
Ready to put these KPIs to work
in your organisation?
Den Otter Solutions builds supply chain dashboards for SMEs in logistics, distribution and manufacturing. From data source to decision dashboard — including all 10 of these KPIs built out in your own Power BI environment.