REPORTING & ANALYTICS / DERBYSHIRE
WHAT IS INCLUDED
WHY DERBY
Customers in aerospace and rail rate their suppliers on delivery punctuality and quality, and those scorecards affect future work. Many Derby firms only see their own numbers when the customer's scorecard arrives. We build dashboards that calculate the same measures internally from your ERP and quality records, so you can see a slipping delivery performance or rising reject rate weeks before a customer raises it.
REPORTING & ANALYTICS / DERBY
Job costing is the other common gap. Quoted hours and actual hours often sit in different systems, so nobody knows which part numbers make money. By joining time bookings, material costs and sales data, a reporting layer shows margin by customer, part family and machine. For a smaller Derby business, even a single weekly report replacing a Monday morning of spreadsheet work is a worthwhile start, and it can grow from there.
IN PRACTICE / DERBY
A component supplier might track on-time-in-full and quality performance using the same rules its biggest customers apply, see the trend weekly by customer, and investigate late orders while there is still time to recover the month.
A machine shop could combine operator time bookings, machine rates and material costs against each job, then compare them to the quoted figures to find which repeat parts are underpriced and which customers are genuinely profitable.
An engineering recruitment agency could see live contractor numbers, margin by client and upcoming contract end dates in one view, rather than reconciling payroll exports with a sales spreadsheet at month end.
REPORTING & ANALYTICS ACROSS DERBY
DELIVERY / DERBY
Agree the opportunity and evidence of success.
Make direction tangible before delivery expands.
Build in visible, testable releases.
Use real performance to shape what follows.
QUESTION / DERBY
Nearly always. Messy data is normal, and part of the work is cleaning it up in a repeatable way: matching customer names, handling missing time bookings and spotting obvious errors. We start with a few measures you already care about, show where the data is unreliable, and improve the underlying records as we go rather than waiting for perfect data first.
NEARBY SERVICE COVERAGE