What is process optimization?
Process optimization is the systematic improvement of how work gets done, measured against a goal such as cycle time, cost, error rate, throughput, yield, or energy use. The term covers two kinds of process. Business processes move a case through steps and people, such as an invoice through approval or a permit through review. Industrial processes turn materials into product under settings such as temperature, pressure, line speed, and feed rate. The methods differ, but each project follows the same cycle: measure the current process, find what limits it, change it, and confirm that the change worked.
How process optimization works
For business processes, the starting point is evidence of what actually happens. Process mining reconstructs the real path of each case from event logs in enterprise resource planning (ERP), customer relationship management (CRM), and ticketing systems. It shows where work waits, loops back, or skips a control. Root cause analysis then explains why. The fix may be a changed rule, a removed approval step, or workflow automation of a manual task.
For industrial processes, machine learning models learn from historian data how settings relate to quality and output. An optimizer then recommends settings that improve the result without breaching safety or quality limits. In both cases, simulation or a digital twin lets a team try a change on a model before touching the real process.
Process optimization in practice
A county building department wants faster permit approvals. Process mining on its permitting system shows that simple residential permits, such as water heater replacements, wait days for a plan reviewer who approves them unchanged. The department creates a separate track for those permit types, with a checklist review at intake. It also adds a document classification model that flags incomplete applications at submission, before they reach a reviewer. Reviewers now spend their time on new construction and commercial plans, and the same event logs show whether approval times actually fell.
What to watch for
- Improve the bottleneck first, because speeding up any other step does not raise output and often piles up work in front of the bottleneck.
- Track the measures that are not being targeted, since a shorter cycle time can raise error rates if checks are removed carelessly.
- Expect gaps in the event data, because work done in email and spreadsheets leaves no trace in system logs.
- Involve the people who run the process, since they know the exceptions that the data does not show.