Since March 2020, most Turkish companies have reached for the same short-term toolkit: freeze travel, suspend training budgets, hold open headcount. These moves were necessary to protect cash flow, but they are not structural improvements. As the acute phase of the crisis gives way to a prolonged ‘new normal’, the real question is whether organisations can emerge with a permanently leaner cost base rather than just a temporarily smaller one. The answer depends largely on combining two disciplines in the right order: Process Mining and RPA. The first shows you where value is leaking; the second stops the leak. Used in isolation or in the wrong sequence, neither delivers what it promises.
Process Mining analyses the event logs generated by enterprise systems — ERP, CRM, accounting platforms — to reconstruct how processes actually flow, as opposed to how they are supposed to flow. That distinction matters more than it might seem. In a mid-sized Turkish manufacturing company, a typical purchase order approval process might be designed with five steps. When you extract the event log and run a conformance check, you frequently find forty or more distinct execution variants. Some of those variants are legitimate exceptions; many are the accumulated residue of informal workarounds, undocumented escalations, and one-off fixes that were never cleaned up. Process Mining makes this ‘process decay’ visible and measurable. It replaces the guesswork of ‘I think the bottleneck is here’ with a precise, evidence-based map of where time, effort, and money are actually going.
The pandemic amplified the value of that visibility sharply. When teams shifted to remote work, processes that had relied on informal office coordination broke down in ways that were hard to diagnose from the outside. A logistics company in Istanbul found that its accounts payable team, now working from home, was spending nearly twice as long on e-Invoice reconciliation as it had in the office. The cause was not laziness or technical failure; it was the disappearance of the spontaneous desk-to-desk conversations that had resolved ambiguities instantly, replaced by long e-mail threads and delayed approvals. Process Mining quantifies this kind of degradation precisely: which step is consuming the most elapsed time, which user profiles are associated with the longest queues, which document types generate the highest exception rates. Without this measurement, deploying RPA is premature — you risk automating a broken process faster, which rarely ends well.
RPA, or Robotic Process Automation, uses software robots to execute repetitive, rule-based digital tasks without human intervention. In Turkey, adoption accelerated in banking, insurance, and large retail chains through 2018 and 2019, but uptake in the SME segment remains early-stage. The pandemic shifted that calculus. When the fragility of human-dependent coordination became undeniable, interest in automation rose even among companies that had previously considered it out of reach. A practical illustration: a textile exporter’s finance team spends forty-five minutes each morning pulling exchange rates from three separate bank screens and copying them into Excel before producing a daily FX report. A software robot can collect, consolidate, and deliver that data in minutes, at a scheduled time, without transcription errors. The individual saving looks modest; multiply it across twenty or thirty similar tasks and you recover a meaningful share of a full-time employee’s capacity — which, under current cost pressure in Turkey, is a real and measurable return.
The correct operating sequence for combining the two tools is straightforward: use Process Mining to map current-state behaviour and identify automation candidates, deploy RPA against those candidates, then return to Process Mining to measure whether the automation is actually performing as designed. This loop protects you from the ‘deploy and forget’ failure mode that has undermined many early RPA programmes. That said, full-scale Process Mining platforms — Celonis, UiPath Process Mining, ABBYY Timeline — carry significant upfront costs in licensing, integration effort, and data quality preparation. For Turkish SMEs, dollar-denominated licence fees are a real constraint given current exchange rate volatility. A more realistic starting point for smaller organisations is to export event logs from an existing ERP system and analyse them with accessible tools before committing to an enterprise platform. The visibility is partial, but it is still vastly better than none.
The most common point of failure in a structural efficiency programme is not technical — it is organisational. Proving with data that a process is inefficient does not automatically dissolve the resistance of the team that owns it. During a pandemic, that resistance takes a specific form: ‘Everything is already disrupted; why add another change on top of it?’ That concern is legitimate and should not be dismissed. Programmes that succeed in this environment tend to do two things: they demonstrate concretely that the change reduces the workload of the people involved, rather than simply cutting headcount, and they start with a small, willing pilot team rather than a company-wide rollout. Quiet, measurable small wins are far more persuasive right now than ambitious transformation announcements.
Before designing a post-pandemic efficiency programme, ask yourself three questions. Which of my processes do I actually know — based on data — and which ones am I estimating? Am I selecting automation candidates from evidence, or from instinct? Will the savings I achieve be one-time, or am I building a repeatable improvement cycle? The answers to those questions are what separate temporary crisis management from durable operational maturity. Process Mining and RPA are the instruments; the journey itself requires leadership that takes data seriously and can manage change under pressure — a combination that is rarer than either tool.
This article was originally written in Turkish by Gökhan MERCANOĞLU on June 29, 2020 and has been automatically translated into English and other languages using machine translation.