Process Mining Consulting

Most automation gets aimed by anecdote. Somebody senior says invoice approvals are slow, so that is what gets built, and afterwards nobody can prove it helped because there was never a before number. Process mining reads the timestamps your systems already write and shows you where the work really waits, how often it loops back, and what each delay costs. Then you automate the part that is worth automating, with a baseline you can hold it to.

Process mining that finds where work actually waits, using the event data your systems already write. Quantify the bottleneck before you automate it, and keep the baseline to prove the result.

Process mining is the practice of reconstructing how a business process really runs from the event logs your existing systems already record, rather than from how people describe it. It shows the actual paths work takes, where it waits, where it loops back for rework, and how long each step takes, which is what turns an automation idea into a business case with a number attached.

Process mining reads the timestamps already sitting in your ERP, CRM or ticketing system and rebuilds the real path a case takes through your business. It shows where work waits, how often it is reworked, and what that costs. Thinklytics uses it before an automation build, so the target is chosen on evidence and there is a baseline to measure the result against afterwards.

A reconstruction of how a process really runs, built from the event data your systems already write. No workshops where people describe what they think happens.

A quantified list of where the time goes, so the automation target is picked on evidence instead of on whoever complained loudest.

A baseline you keep. Without a before number, nobody can say later whether the build worked.

A short list of what should stay manual. Some steps are slow because a person has to think, and automating those just moves the delay.

A software purchase. We work with what you already have where we can, and the licence is yours either way, not resold through us.

A surveillance tool. This reads system timestamps about cases, not about how fast individual people work, and we scope it that way deliberately.

A six month study. If it takes longer to measure the process than to fix it, the measuring was the wrong call.

A replacement for talking to the team. The data shows where work waits. The people tell you why, and you need both.

The real process map, including the paths nobody documented and the ones people did not know were still in use.

Wait time, rework rate and handoff count per step, with the volume behind each so you can tell a rare annoyance from a daily cost.

A ranked shortlist of candidates to automate, each with the time or cost it would release and what it would take to build.

The baseline measurement, written down, so the result can be checked after the build rather than argued about.

A read on the event data itself, because a process map built on incomplete logs is confidently wrong, and you should know which parts are solid.

NAIC statutory reporting cycle, after the manual assembly steps were measured and then automated, saving $1.1M a year in compliance labour.

Prior authorisation review time, with 18,000 more cases handled a month and no extra staff.

Downtime found once tracking was automated across 6 plants, and unplanned stoppages cut by 31 hours a month.

We automated the thing everyone complained about and the cycle time barely moved.

The complaint named a step, not the bottleneck. The wait was usually sitting somewhere quieter, often in a handoff between two teams where nobody owns the clock.

No baseline was taken before the build, so there is nothing to compare against. This is the most common reason a working automation still loses its funding.

Every team describes the process differently and they are all partly right.

There are several real paths in use, and each team sees the one it touches. The event log shows all of them, including the exception path that turns out to be a third of the volume.

Something upstream is letting incomplete work through. Automating the downstream step makes the rework arrive faster, which is worse.

It is a way of working out how a process really runs by reading the event data your systems already record, instead of asking people to describe it. Every time a case moves through your ERP, CRM or ticketing system, a timestamp gets written. Put those in order and you can see the actual paths work takes, where it waits, and how often it loops back, which is usually quite different from the documented process.

So the target is chosen on evidence and so you have a before number. Automation aimed by anecdote tends to hit a step that was never the bottleneck, and without a baseline nobody can show afterwards whether it helped. Both of those are why automation projects lose funding even when the thing they built works.

Not always. For a single process with a clean event log, the analysis can be done against the data directly and the answer arrives in weeks. A dedicated platform earns its cost when you want continuous monitoring across many processes rather than a one off read. Either way the licence is yours and is not resold through us, so the recommendation is not tied to a partnership of ours.

An export of the event log for the process in question, which at minimum needs a case identifier, an activity name and a timestamp. A user or team field helps with handoffs. We can usually tell within a few days whether what you have is complete enough to build a reliable map, and if it is not, that finding matters on its own.

No, and we scope it so it cannot be. The analysis follows cases rather than people, and the team level fields are used for handoffs between groups, not for ranking individuals. Teams stop cooperating with a measurement exercise the moment they think it is about them, which is a practical reason as well as a fair one.

A single process usually takes four to six weeks from data extract to a ranked shortlist, and the variable is nearly always how clean the event log is rather than the analysis. If the log turns out to be incomplete, fixing that is its own piece of work and should be costed separately rather than buried in the estimate.

What is the difference between process mining and process intelligence?

Process intelligence is the broader label the market moved to, and it covers continuous monitoring rather than a one time study. Gartner renamed the category Process Intelligence Platforms in May 2026. In practice the first engagement is a study, and whether it becomes continuous monitoring depends on whether you have enough processes to watch to justify it.

The drivers are how many processes are in scope, how clean the event data is, and whether you want a one time study or continuous monitoring. Thinklytics prices by deliverable rather than by hours, so the scope and the cost are agreed before work starts. The first stage is priced separately, so you can stop there if what we find does not justify a build.

The build that follows, aimed at what the measurement found.

Whether your data can carry the use case before you fund it.

For when the event log itself is the thing that needs fixing first.

The most common process people want measured, then shortened.