Process optimization
Automating is half of it. The other half is optimizing.
Automating a badly designed process only makes it fail faster and at a larger scale. First we redesign the process with the same industrial engineering tools the big plants use, and only then do we automate it.
Bottlenecks
A process moves only as fast as its slowest step. We find which one it is, because until that step changes, adding people or software does not improve the result.
We measureWait time at each stage
Cycle time
How long an order takes from start to finish, and how much of that time is pure waiting rather than real work. In manual processes, waiting is usually most of it.
We measureTotal hours against hours actually worked
Repeated manual work
Every piece of data copied by hand from one system into another gets paid for twice: in hours and in errors. Eliminating double data entry is usually the fastest win of all.
We measureData entries per order
Errors and rework
An error caught at the end costs far more than one prevented at the start. We lower the error rate at the source instead of adding another review at the end.
We measurePercentage of cases redone
How this turns into money
Every opportunity we find comes with its number attached. It is not a marketing promise: it is arithmetic on the hours going into work a machine can do on its own.
Illustrative example: six hours a week moving chat orders into Excel is 312 hours a year. At $18 per loaded hour, that is $5,616 a year on a single task, without counting what the errors it creates cost you.
Illustrative number, not a client result. Yours gets measured on your own real operation during the Diagnostic.
Optimization is decided with data, not with opinion
That data comes out of the Diagnostic. That is why it always goes first.