On-time delivery is the number your customers grade you on, and it's the one most shops can't state with confidence. Promise dates live on the order, ship dates land in shipping, and the comparison - your true OTD - is rarely computed. This dashboard makes it a live metric: the on-time rate, the orders sitting past due right now, the dollars trapped in them, and the root causes behind the late ones.
What this dashboard answers
It answers both halves of delivery performance - the rearview and the windshield. On-Time Delivery % (OTD) is on-time shipments over orders shipped, trended weekly and broken out by customer. Alongside it, the forward-looking risk: Past-Due Open Orders that have already blown their promise date and the Past-Due $ Value trapped in them. Total Late Days paired with the shipped count gives average lateness, so a near-miss culture and a chronically-weeks-late culture don't read the same.
The metrics that matter
OTD is the headline customers feel, but Past-Due $ Value is the number that should drive the morning standup - it's revenue stuck behind a missed date, ranked by customer. The root-cause page attributes late orders to reasons (material late, capacity, rework, quote/engineering changes), splits on-time versus late shipment counts by the primary work center, and ages past-due orders into 1-7 / 8-30 / 31+ day buckets so the oldest, most relationship-damaging misses surface first.
Why this data is trapped
Promise dates, ship dates, and order values all live in JobBOSS (E2 Shoptech), ProShop ERP, Global Shop Solutions, ECI M1 / Epicor, or the QuickBooks-based order books smaller shops keep. Computing OTD means comparing promise to actual ship across every order and attributing the misses to a work center or reason - an analysis the ERP has the data for but doesn't assemble. The result is that "how's our on-time doing?" gets answered by the last angry customer call rather than a number.
How to read it
Read the weekly OTD line for trend and the by-customer bar for who is being let down - your biggest customer with sliding OTD is your biggest retention risk. Then work the Past-Due $ Value bar and the aging buckets: the 31+ day bucket is where customer relationships break. The Customer-by-Work-Center matrix is the diagnostic - if lateness concentrates in one work center, that's a capacity or scheduling fix; if it concentrates in one customer, it's often their late material or engineering changes, which is a different conversation.
The sample uses realistic synthetic order data - no real customers, shipments, or values.
Metrics it tracks
| Metric | What it means |
|---|---|
| Orders Shipped | Number of work orders that have shipped. |
| On-Time Shipments | Shipments delivered on or before the promise date. |
| On-Time Delivery % (OTD) | On-time shipments divided by orders shipped - share that met the promise date. |
| Past-Due Open Orders | Open orders already past their promise date. |
| Past-Due $ Value | Dollar value of open orders sitting past due. |
| Total Late Days | Sum of days late over shipped orders - pair with shipped count to read average lateness. |
Used by: Owners and operations managers accountable for delivery performance
Frequently asked questions
How is on-time delivery calculated for a job shop?
OTD is on-time shipments divided by total shipments - the share of orders that shipped on or before their promise date. This report computes it across every shipped order, trends it weekly, and breaks it out by customer and work center so you can see both the rate and what's driving the misses.
Why track past-due dollars, not just past-due orders?
Because a single large past-due order can matter more than several small ones. Past-Due $ Value ranks the revenue trapped behind missed dates, and the aging buckets surface the oldest misses - the ones most likely to cost you the customer - so you work the highest-impact orders first.
Can I build this on my own order data?
Yes. Export your orders with promise dates, ship dates, value, customer, and primary work center, then use this as a template. We model it into a Power BI report. The sample is fully synthetic, so there's no real customer or order data in it.
Build this report on your own data
Clone this Machine / job shops template — describe it and we’ll generate sample data so you can try it free, or upload your own export. You get a fully modeled, branded Power BI project that opens in Power BI Desktop.
Use this as a template →