Hours spent preparing weekly reports
The direct admin time the dashboard should replace.
Small manufacturers · KPI and pipeline dashboards
See on-time delivery, backlog and scrap without building Monday's spreadsheet
The owner of a small plant usually wants a handful of answers each week: are we shipping on time, how big is the backlog, which jobs are late and why, how much scrap and rework did we have, and are we winning enough quotes to keep the machines busy. The data is in the ERP, but getting it out means reports, exports and a spreadsheet someone rebuilds every Monday.
How it often works today
- The plant manager spends Monday morning exporting ERP reports and building a spreadsheet for the production meeting.
- On-time delivery is measured differently by sales, production and customers' supplier scorecards, so nobody agrees on the number.
- Scrap and rework are recorded on paper or not at all, so their cost is invisible until margins slip.
- Backlog by work center and machine is estimated in the scheduler's head rather than seen in one place.
We would agree the measures with you, write precise definitions and build a dashboard refreshed from the ERP and accounting system on a schedule. If the data underneath is unreliable, such as operations not being clocked, we would say so and fix that first rather than put a polished chart on top of guesses.
The improved workflow
Agree the measures
Choose a short list, such as on-time delivery to promise date, backlog by work center, quote hit rate, scrap cost and machine utilization, and define each one precisely.
Check the data quality
Test whether the ERP data supports each measure, such as clocked operations, recorded scrap and accurate promise dates, and fix collection gaps first.
Build the dashboard
Connect to the ERP and accounting system and build daily and weekly views that refresh on a schedule, with drill-down to late jobs and their reasons.
Use it in production meetings
Run the weekly production meeting from the dashboard and agree actions on late jobs, bottleneck work centers and scrap causes.
How we deliver this: AI automation for small business or fractional CTO leadership, scoped in writing and run over 90 days. See pricing or the sample scope.
What we would measure
On-time delivery to promise date
Usually the measure customers care about most.
Late jobs with a recorded cause
Shows whether the plant is learning from late shipments.
Scrap and rework cost per month
Makes a hidden margin leak visible.
Where people stay in control
- Every measure has a written definition agreed by the owner and plant manager before it is published.
- Data quality issues are shown openly on the dashboard rather than hidden by assumptions.
- Financial and margin views are limited to the owner and named managers.
Example
Illustrative: a late-job pattern
For example, a precision machining shop builds a weekly dashboard from its ERP. On-time delivery looks acceptable overall, but drilling down shows most late jobs share one cause: waiting on outside anodizing. The owner uses that view to qualify a second anodizer and to add a buffer to quoted lead times for anodized parts. This is an illustrative example rather than a measured result.
Tools typically involved
What the first 30 days look like
In the first 30 days we would sit in on your production meeting, list the questions the owner and plant manager ask weekly, define each measure, test whether your ERP data supports it and rebuild one past month to check the numbers.
Common questions
Our ERP has reports. Why add a dashboard?
Sometimes you should not. If a few saved ERP reports and a set weekly routine answer your questions, that is cheaper and easier to maintain. A dashboard makes sense when the answers need data from several systems, trend views or drill-down that the ERP reports cannot provide.
What if our shop floor data is poor?
Then the first job is improving it. Common steps include scanning travelers at each operation, recording scrap with a reason code and keeping promise dates current. We would show you which measures are trustworthy now and which need better data, rather than presenting numbers you cannot rely on.
Which KPIs matter most for a job shop?
For most, on-time delivery, backlog in hours by work center, quote hit rate and scrap or rework cost are a good starting set. Machine utilization and labor efficiency are useful once operations are recorded reliably. We would pick the list with you based on the decisions you make weekly.
The business case
Does it pay for itself?
Illustrative defaults for a manufacturer. Replace them with your numbers.
What this workflow costs today
a month in staff time, before any lost sales
Monthly fee + assumed tools +
Break-even: new clients a month (at first-year value)
Or: staff hours recovered and redeployed
Break-even additional revenue
Value of +1 point of win rate
First 90 days, service fee
Worth a closer lookTest the assumptions
Illustration only, not a forecast or guarantee. Recovered staff time becomes a cash saving only if spending actually declines or the time goes into paid work. Tool costs are an assumption; we replace every number here with yours before recommending a scope.
KPI and pipeline dashboards in other industries
Architecture firmsAuto repair shopsConstruction and general contractorsDental practicesElectrical contractorsEngineering firmsHVAC companiesIT managed service providers
KPI and pipeline dashboards: full guide
Related guides
Your first 90 days with a fractional tech leadHow to automate invoicing in QuickBooks OnlineA practical guide to CRM data cleanupHow to automate recurring client reports
Terms used here
Next step
Bring one expensive workflow. Leave with a clear next step.
In 30 minutes we look at one recurring workflow, what it costs you today and whether there is a credible case to improve it. If there is not, we will say so.
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