Consider a mid-sized logistics company that decides to automate its invoice approval workflow. The team is processing hundreds of invoices manually each month, delays are piling up, and management approves an RPA investment to fix the problem. The robot goes live, the first week looks promising, and then the cracks appear. Certain invoice types are skipped, error rates climb on specific categories, and the finance team ends up spending more time correcting the robot’s mistakes than they did handling the process manually. The robot itself is not the problem. The problem is that nobody looked at the actual process before automating it. The question that was never asked: how does this process really work in practice?
Process Mining addresses exactly that gap. It analyses the event logs produced by ERP, CRM, or workflow systems to reconstruct how a process actually executes — not how it was designed on paper, but how it lives in production. It surfaces the number of distinct variants running through a single process, identifies which steps are routinely skipped, locates where approval bottlenecks concentrate, and flags which users consistently deviate from the standard path. Tools such as Celonis, UiPath Process Mining, and SAP Signavio lead this space. In Turkey, large-scale manufacturing and retail companies are running pilots, while awareness among small and medium enterprises is still forming. The discipline is not yet mainstream in the Turkish market, but interest accelerated noticeably through 2018 and into 2019.
RPA — Robotic Process Automation — automates structured, repetitive, rule-based tasks using software robots. Invoice entry, inventory updates, report compilation, e-mail routing: these are the tasks where RPA delivers reliable throughput gains. In Turkey, banking and insurance adopted the technology relatively early. Manufacturing and retail followed with growing interest from 2018 onward. Yet field experience tells a consistent story: a significant share of RPA projects fall short of their projected return on investment. The most common reason is not the technology itself. It is that automation was launched before anyone understood the process being automated.
Using both disciplines together requires a three-stage sequence. The first stage is discovery: Process Mining maps the real state of the process. These analyses routinely surface the same findings — between thirty and forty percent of process instances deviate from the standard flow, several steps are completed through undocumented manual interventions, and the majority of bottlenecks cluster not at approval gates but at waiting periods caused by data quality gaps. The second stage is simplification: the discovered variants and exceptions are analysed, unnecessary approval steps are removed, data quality issues are resolved, and exception paths are standardised — all before a single robot is deployed. The third stage is automation: RPA is applied to a process that is now clean, predictable, and fully documented. The robot produces fewer errors, maintenance costs drop, and return on investment becomes measurable rather than estimated.
In the Turkish context, this sequencing carries an additional financial argument. With sustained currency pressure and elevated inflation, the cost of a failed or underperforming IT investment is not simply a line item — it represents real budget capacity lost at a time when every lira of technology spend must justify itself. A mid-sized manufacturing firm that deploys RPA on a poorly understood process can find that annual maintenance and correction costs exceed the original project budget within eighteen months. Process Mining limits that risk from the outset by producing numerical evidence of what is worth automating and what must be fixed first. The investment becomes targeted rather than speculative.
The limitations of this approach deserve equal attention. Process Mining requires event log data of sufficient quality and volume to produce meaningful analysis. If the ERP system is not properly configured, or if a significant portion of the process runs outside any system — a common situation in Turkish SMEs — the analysis will be incomplete. Process Mining also needs to be positioned as a continuous monitoring practice rather than a one-time diagnostic exercise; a single analysis loses relevance as processes evolve. On the RPA side, the constraint is equally clear: a robot accelerates a poorly designed process without correcting it. The speed gain is real, but the underlying problem persists and now operates faster.
A practical starting point for any organisation considering this combination: identify one process — the one generating the most complaints, the most manual intervention, and the highest error rate. Extract its event logs and visualise them with a Process Mining tool. Share the findings with the team; the results frequently challenge assumptions that even experienced operators held with confidence. Complete the simplification steps, then define the RPA scope. Keep the cycle small, keep it measurable, and document what you learn before moving to the next process. Automation programmes that begin with large promises and broad scope tend to stall. Programmes that begin with a single proven cycle and expand from evidence tend to grow into something durable.
This article was originally written in Turkish by Gökhan MERCANOĞLU on February 18, 2019 and has been automatically translated into English and other languages using machine translation.