Process optimisation

What is process optimisation?

Process optimisation is the deliberate work of making a process better against outcomes you can measure. Those outcomes might be quality, throughput time, cost, compliance, workload on staff or the result the customer actually gets.

Improving a process is not the same as pushing one number to its limit. Faster handling that creates more mistakes or more risk can leave you worse off overall, so good optimisation watches the trade-offs, not a single metric.

The work runs as a cycle you repeat: scope the problem, measure it, dig into the causes, redesign, test the change, and make it stick.

Start with the problem and the outcome

Write down which process, which cases and which outcome you are trying to move. 'Speed up the invoice process' is too broad to act on. 'Cut the wait between receiving a PO invoice and the first check' is something you can measure and know whether you improved.

Set a baseline with a clear definition, a time period and a data source. Look past the average to the spread, the percentiles and the exceptions, because an average hides the cases that hurt most.

Add balancing measures at the same time. If you speed up a check, track the mistakes and the rework that a rushed check might cause, so a win in one place does not quietly create a loss in another.

Find causes, not symptoms

A bottleneck on one team tells you where work piles up, not why. The reason could be batch scheduling, missing data, overload, unclear rules or an error introduced upstream. Treat the symptom and the queue just moves somewhere else.

Combine process mining with interviews, observation and reading through real files. System timestamps miss some of the work, and a conversation on its own does not tell you how often something happens. Root cause analysis is about testing alternative explanations rather than settling on the first one: a link between many handovers and long throughput time does not prove the handovers are the only cause.

Simplify before you automate

Take out steps with no demonstrable value, merge duplicate checks and put each decision with the role that should make it. Cut variation that exists only out of habit. Then automate the parts that are stable and repetitive.

Automating a bad process just makes the waste faster. A robot that copies useless data between two screens all day has sped up nothing worth doing. Often a change to a system or a policy beats RPA, because it removes the work instead of performing it faster. Pick the fix that hits the cause directly.

Example: order release

Orders wait two days on average for financial release. The analysis shows that 80 percent of them fall under a low risk threshold and are almost never refused. The team builds a rule-based straight-through route for those, and sends only the exceptions, an unusual credit status, a high amount or missing data, to a human review.

A pilot measures the time saved, but also the false approvals, the review volume and any downstream payment problems. Only once the results hold steady does the new route go wider. Rolling it out before the pilot proves itself would be swapping a slow process for an unproven one.

Process optimisation versus standardisation

Standardisation fixes an agreed baseline and the variation you allow around it. Optimisation changes that baseline to reach a better outcome. A standard makes fair comparison possible; optimisation keeps the standard from ageing into something nobody follows.

After a proven improvement, update the process model, the work instructions, the systems and the training. Skip that and the pilot stays a local exception that fades once attention moves on.

Making the change stick

Assign owners for the metric and the process, set control limits or alert thresholds, and review the exceptions. Watch for process drift, data quality slipping and whether people actually adopt the new way. When staff work around the new route, it is usually a sign the route is impractical or was never explained well.

Measure the end-to-end result, not just how fast the automated flow runs. A flow can be quick while work piles up before it or after it. Optimisation does not end at go-live: the new way of working becomes the next baseline you measure against.